A Real Time Cognitive Radio Testbed for Physical and Link Layer Experiments

情景记忆测验法（MBMT）1. 简介情景记忆测验法（MBMT）是一种常用于评估个体记忆能力的认知测试方法。

它通过要求被试者回忆一系列与特定场景相关的信息，以测量他们对情景记忆的能力和表现。

MBMT 由美国心理学家波尔斯克创立，并被广泛应用于记忆研究、认知评估以及临床诊断等领域。

2. 测试步骤MBMT 的测试步骤通常包括以下几个环节：2.1 研究阶段在研究阶段，被试者会被呈现一系列与特定场景相关的材料，例如文字描述、图像或视频。

这些材料通常包含细节性信息，例如场景的背景、人物的特征或物体的位置等。

2.2 记忆阶段在记忆阶段，被试者需要在没有任何提示的情况下，尽可能多地回忆之前研究到的信息。

他们可以用文字、图片或其他形式来表达他们的回忆结果。

2.3 识别阶段在识别阶段，被试者需要判断一系列陈述是否与他们之前研究到的信息相符。

这些陈述通常包含正确的信息、错误的信息以及无关信息。

被试者需要根据他们的回忆来判断每个陈述的准确性。

2.4 重复步骤MBMT 的测试步骤可以根据需要重复多次，以收集更多数据用于分析。

每一次重复测试都可以观察到被试者在情景记忆方面的进步或退步情况。

3. 应用领域情景记忆测验法（MBMT）在多个领域都得到了广泛应用：- 记忆研究：MBMT 可以被用来研究人类记忆的特点、机制以及影响因素。

通过观察被试者在各个阶段的表现，研究人员可以深入了解记忆的认知过程。

- 认知评估：MBMT 是一种常用的认知评估工具，在临床和教育领域中得到广泛应用。

通过测量被试者的情景记忆能力，医生或教师可以更好地评估其认知功能的状态以及与正常人群的差异。

- 临床诊断：情景记忆测验法可以用于帮助医生诊断一些与记忆障碍相关的疾病，例如阿尔茨海默病和额颞叶癫痫等。

通过比较患者与正常人群在 MBMT 上的表现，医生可以作出初步判断并进行进一步的诊断。

4. 结论情景记忆测验法（MBMT）是一种有效的工具，用于评估个体在情景记忆方面的认知能力和表现。

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自我效能感的二十四项测量法(SEES)
自我效能感是个体对自己能够成功完成某项任务的信心和信念
的度量。

在心理学领域，自我效能感被认为是一个重要的概念，与
个体的动机、情绪和行为有着密切的关系。

为了对自我效能感进行
测量，研究者们提出了各种各样的测量工具，其中一项被广泛应用
的测量工具就是自我效能感的二十四项测量法(SEES)。

自我效能感的二十四项测量法(SEES)是由美国心理学家阿尔伯特·班迪乌拉(Albert Bandura)和他的同事们在20世纪80年代初提出的。

该测量法通过对个体在各种情境下的自我效能感进行评价，从
而全面地了解个体在不同领域的自我效能感水平。

SEES包含了24个描述性陈述，被要求对每一项陈述进行评分，以表示自己对该陈述的信心程度。

通过该测量法，研究者们可以得
知个体在不同领域的自我效能感水平，例如在学术方面、运动方面、社交方面等等。

这些信息对于了解个体的心理状态以及对个体进行
心理干预都具有重要的指导意义。

自我效能感的二十四项测量法(SEES)是一个简单而有效的工具，被广泛用于心理学研究和临床实践中。

通过该测量法，我们可以更
加全面地了解个体的自我效能感，从而为个体的心理健康和成长提
供更有针对性的帮助。

松江区2023学年度第二学期模拟考质量监控试卷高三英语（满分140分，完卷时间120分钟）2024.4 考生注意：1．本考试设试卷和答题纸两部分，试卷包括试题与答题要求，所有答题必须涂（选择题）或写（非选择题）在答题纸上，做在试卷上一律不得分。

2．答题前，务必在答题纸上填写学校、班级、姓名和考号。

3．答题纸与试卷在试题编号上是一一对应的，答题时应特别注意，不能错位。

Ⅰ．Listening ComprehensionSection ADirections: In Section A, you will hear ten short conversations between two speakers. At the end of each conversation, a question will be asked about what was said. The conversations and the questions will be spoken only once. After you hear a conversation and the question about it, read the four possible answers on your paper, and decide which one is the best answer to the question you have heard.1．A．At 8:00. B．At 8:15. C．At 8:30. D．At 8:45.2．A．A professor. B．A coach. C．An engineer. D．A nurse.3．A．In a restaurant. B．In a hairdres ser’s.C．At a cinema. D．At a tailor’s. 4．A．Ways to visit a university. B．Two student tour guides.C．A tour of Fudan University. D．The campus of Fudan University.5．A．They did not make it there finally.B．They were not well received there.C．They experienced something unpleasant on the way.D．They had a wonderful time before they arrived there.6．A．Excited. B．Interested. C．Confused. D．Annoyed. 7．A．Practice the presentation in front of him. B．Watch how he makes a presentation. C．Reduce the time spent in practicing. D．Find out who her audience will be.8．A．She is always absent-minded. B．She forgot to tell the man about it.C．She is unclear about Sophie’s plan.D．She slipped in the neighboring town. 9．A．Because it took him much time to go to work.B．Because he had to save money for his journey.C．Because the job arranged many business journeys.D．Because he considered it unlucky to have that job.10．A．Buy a new printer with less noise. B．Ask the man to borrow a printer.C．Read a book on how to fix the printer. D．Get someone to repair the printer.Section BDirections: In Section B, you will hear two passages and one longer conversation. After each passage or conversation, you will be asked several questions. The passages and the conversation will be read twice, but the questions will be spoken only once. When you hear a question, read the four possible answers on your paper, and decide which one is the best answer to the question you have heard.Questions 11 through 13 are based on the following passage.11．A．How encores came into existence. B．How bands perform encores properly.C．Why audiences used to need encores. D．Why encores are part of a performance. 12．A．The 17th century. B．The 18th century. C．The 19th century. D．The 20th century. 13．A．French people were more interested in encores than others.B．Bands usually prepare more than two encores for each show.C．Recording technology boosted audiences’ needs for encores.D．Musicians can get recharged during the break before encores.Questions 14 through 16 are based on the following passage.14．A．Because of the rule for the class. B．Because of the course materials.C．Because the speaker changed his topics. D．Because the speaker disliked technology. 15．A．The students do not assess the speaker’s class fairly.B．The students are satisfied with the class environment.C．The speaker did not favor leaving technology at the door.D．The speaker were worried about students’ evaluation on him.16．A．It will stop students getting on well together.B．It may help students better understand themes.C．It will improve teaching effect by giving students more help.D．It may distract students from digging deep within themselves.Questions 17 through 20 are based on the following conversation.17．A．Doctor and patient. B．Salesman and customer.C．Teacher and student. D．Employer and employee.18．A．Fishing industry. B．Statistics. C．Computer modeling. D．Note-taking. 19．A．She is good at making model computers. B．She has decided on the title of the essay. C．She is uninterested in coping with statistics. D．She has always been weak at note-taking. 20．A．Learn to take notes immediately. B．Find out possible strategies alone.C．Read for more useful information. D．Work on her weaknesses by herself. Ⅱ．Grammar and VocabularySection ADirections: After reading the passage below, fill in the blanks to make the passage coherent and grammatically correct. For the blanks with a given word, fill in each blank with the proper form of the given word; for the other blanks, use one word that best fits each blank.Remote Work Slows Senior Housing Market RecoveryWith the rise of remote work, the market for senior housing has met with problems in its recovery. Only a few old people choose to live in senior-living communities （21）______the growing senior population and the cancelation of COVID-19 restrictions once making family visits difficult.（22）______ this trend suggests is that people’s shift to remote work contributes to the slow rebound of the senior housing market. That is, remote work is keeping many older Americans from moving into senior-living communities once warmly （23）______（welcome）.When more adults began working remotely during the pandemic（流行病）, they were able to check in on aging parents easily —they （24）______ take care of their parents’ issues on short notice.Experts have been analyzing the phenomenon in different ways. Some found that the greater flexibility to care for parents （25）______（mean）people’s delay in sending aged parents to expensive senior-housing accommodations. Therefore, markets with high levels of people working from home usually have lower senior-housing occupancy rates. Others said remote work might have some effect but also pointed to different factors. For instance, many seniors think that their family wallets are getting thinner, making some of them reluctant （26）______（send）to senior-living communities.The age at which people enter senior housing is also increasing, （27）______serves as another sign that shows people are choosing to delay transitioning. The rising cost of senior living weighs heavily on that decision. The CPI （consumer-price index）for nursing homes and adult day services rose 4.5% last May compared with （28）______in May, 2022.Still, many senior-housing operators are optimistic. When （29）______（illustrate）their point, they showed an increase in the number of people turning 80 years old over the following years and the actual wealth they have collected. Moreover, they find remote work arrangements are decreasing in some parts of the country, （30）______ employees there have seen their lowered productivity while working from home.Section BDirections: After reading the passage below, fill in each blank with a proper word chosen from the box. Each word can be used only once. Note that there is one word more than you need.A．accompanied B．allowed C．feasibly D．fueled E．intensity F．option G．prompting H．routine I．surgically J．underlying K．variedBrain Signals for Lasting PainBrain signals that reveal how much pain a person is in have been discovered by scientists who say the work is a step towards new treatments for people living with lasting pain.It is the first time researchers have decoded the brain activity 31 patients’ lasting pain. That has raised the hope that brain stimulation treatment alre ady used for Parkinson’s and major depression can help those running out of any other 32 . “We’ve learned that lasting pain can be tracked and predicted in the real world,” said Prasad Shirvalkar, lead researcher on the project at the University of California.Lasting pain affects nearly 28 million adults in the UK alone, and the causes are 33 . ranging from cancer to back problems. That being the case, lasting pain has 34 a rise in taking powerful painkillers. But nomedical treatments work well for the condition, 35 experts to call for a complete rethink in how health services handle patients with lasting pain.For the latest study, Shirvalkar and his colleagues 36 implanted electrodes（电极）into four patients with lasting pain hard to deal with after the loss of legs. The devices 37 the patients to record activity and collect data in two brain regions—the ACC and the OFC—at the press of one button on a remote handset. Several times a day, the volunteers were asked to complete short surveys on the 38 of pain, meaning how strong the pain was, and then record their brain activity. These scientists, armed with the survey responses and brain recordings, found they could use computers to predict a person’s pa in based on the electrical signals in their OFC. “We found very different brain activity 39 severe pain and have developed an objective biomarker for that kind of pain,” said Shirvalkar. The finding may explain, at least in part, why 40 painkillers are less effective for lasting pain. “The hope is that we can use the information to develop personalized brain stimulation treatment for the most severe forms of pain.”Ⅲ．Reading ComprehensionSection ADirections: For each blank in the following passage there are four words or phrases marked A, B, C and D. Fill in each blank with the word or phrase that best fits the context.The way of recording things has never ceased to develop. In the 1980s, as sales of video recorders went up, old 8mm home movies were gradually replaced by VHS （video home system）tapes. Later, video tapes of family holidays lost their appeal and the use of DVDs 41 . Those, too, have had their day. Even those holding their childhood memories in digital files on their laptops now know these files face the risk of 42 .Digitising historical documents brings huge benefits—files can be 43 and distributed, reducing the risk of their entire loss through physical damage caused by fire or flooding. And developing digital versions reduces44 on the original items. The International Dunhuang Project, 45 , has digitised items like manuscripts（手稿）from the Mogao caves in China, enabling scholars from around the world to access records easily without touching the real items.But the news that the Ministry of Justice of the UK is proposing to scan the 110 million people’s wills it holds and then destroy a handful of 46 after 25 years has shocked historians. The ministry cites this as a way of providing easier access for researchers. But that only justifies digitisation, not the 47 of the paper copies. The officials note the change will be economically efficient （saving around £4.5m a year）while keeping all the essential information.Scholars 48 . Most significantly, physical records can themselves carry important information — the kind of ink or paper used may be part of the history that historians are 49 . and error s are often made in scanning. Besides, digital copies are arguably more 50 than the material items, just in different ways. The attack from the Internet on the British Library last October has prevented scholars from 51 digitised materials it holds: imagine if researchers could not return to the originals. Some even think digitised information can easily be lost within decades no matter what 52 are put in place.The government says that it will save the original wills of “famous people for historic record”, such as that of Princess Diana’s. However, assuming that we know who will 53 to future generations is extraordinarilyproud. Mary Seacole, a pioneering nurse who now appears on the national school course in the UK, was largely54 for almost a century.The digitisation of old documents is a valuable, even essential measure. But to destroy the originals once they have been scanned, is not a matter of great 55 , but of huge damage.41．A．paused B．boomed C．recovered D．disappeared 42．A．getting outdated B．coming into style C．being fined D．making an error 43．A．deleted B．named C．copied D．altered 44．A．fight or flight B．life or death C．wear and tear D．awe and wonder 45．A．unfortunately B．additionally C．in summary D．for example 46．A．the originals B．the essentials C．the visualised D．the digitised 47．A．preservation B．classification C．publication D．destruction 48．A．applaud B．disagree C．discriminate D．withdraw 49．A．revising B．abandoning C．uncovering D．enduring 50．A．meaningful B．favourable C．resistant D．delicate 51．A．inventing B．adjusting C．accessing D．damaging 52．A．outcomes B．safeguards C．deadlines D．byproducts 53．A．matter B．respond C．lose D．live 54．A．spared B．discussed C．forgotten D．protected 55．A．sacrifice B．courage C．efficiency D．admirationSection BDirections: Read the following three passages. Each passage is followed by several questions or unfinished statements. For each of them there are four choices marked A, B, C and D. Choose the one that fits best according to the information given in the passage you have just read.（A）Charles Robert Darwin was born on 12 February 1809 in Shropshire, England. Darwin’s childhood passion was science, and his interest in chemistry, however, was clear; he was even nicknamed ‘Gas’ by his classmates.In 1825, his father sent him to study medicine at Edinburgh University, where he learned how to classify plants. Darwin became passionate about natural history and this became his focus while he studied at Cambridge. Darwin went on a voyage together with Robert Fitzroy, the captain of HMS Beagle, to South America to facilitate British trade in Patagonia. The journey was life-changing. Darwin spent much of the trip on land collecting samples of plants, animals and rocks, which helped him to develop an understanding of the processes that shape the Earth’s surface. Darwin’s analysis of the plants and animals that he gathered led him to express doubts on former explanations about how species formed and evolved over time.Darwin’s work convinced him that natural selection was key to understanding the development of the natural world. The theory of natural selection says that individuals of a species are more likely to survive when they inherit （经遗传获得）characteristics best suited for that specific environment. These features then become more widespread and can lead eventually to the development of a new species. With natural selection, Darwin argued how a wide variety of life forms developed over time from a single common ancestor.Darwin married his cousin, Emma Wedgwood, in 1839. When Darwin’s eldest daughter, Annie, died from a sudden illness in 1851, he lost his belief in God. His tenth and final child, Charles Waring Darwin, was born in 1856．Significantly for Darwin, this baby was disabled, altering how Darwin thought about the human species. Darwin had previously thought that species remained adapted until the environment changed; he now believed that every new variation was imperfect and that a struggle to survive was what drove species to adapt.Though rejected at the beginning, Darwin’s theory of evolution by natural selection is nowadays well acc epted by the scientific community as the best evidence-based explanation for the diversity and complexity of life on Earth. The Natural History Museum’s library alone has 478 editions of his On the Origin of Species in 38 languages.56．What made Darwin reconsider the origin and development of species?A．Examining plants and animals collected.B．His desire for a voyage to different continents.C．Classifying samples in a journey to South America.D．His passion for natural history at Edinburgh University.57．We can learn from paragraphs 1 to 3 that Darwin ______.A．used natural selection to develop new speciesB．enjoyed being called nicknames related to scienceC．learned some knowledge about plants when studying medicineD．argued with others over the diversity of life forms for a long period58．Which of the following changed Darwin’s view on the human species?A．That he had ten children in all. B．His youngest son’s being disabled.C．That he lost his eldest daughter. D．His marriage with Emma Wedgwood.59．This passage is mainly about ______.A．Darwin’s passion for medical science B．Darwin’s theory and experimentsC．Charles Darwin’s changing interest D．Charles Darwin’s life and work（B）Welcome to Muir Woods! This rare ancient forest is a kingdom of coast redwoods, many over 600 years old. How to get here?People using personal vehicles must have reservations before arriving at the park. （Details at.）Muir Woods National Monument is open daily, 8 a. m. to sunset. Stop by Visitor Center to get trails（路线）and program information, and to take in exhibits.What’s your path?Enjoy a walk on the paved Redwood Creek Trail （also called Main Trail）. Choose short, medium, or long loops（环线）. Other trails go deep into Muir Woods and Mount Tamalpais State Park.（Refer to the map of Muir Woods on the right for details.）Ready to explore more?Muir Woods is part of Golden Gate National Recreation Area, which includes Marin Headlands, Alcatraz, the Presidio, and Ocean Beach. Download the app at /goga.Stay safe and protect your park.Wi-Fi and cell service are not available. ·Watch for poisonous plants and falling branches. ·Do not feed or disturb animals. ·Fishing is prohibited in the park. ·Do not mark or remove trees, flowers, or other natural features. ·Go to the park website for more safety tips and regulations.AccessibilityWe make a great effort to make facilities, services, and programs accessible to all. For information, go to Visitor Center, ask a ranger, call, or check our website.More InformationMuir Woods National Monument /muwo Mill Valley, CA 94941-269660．Muir Woods will probably attract ______.①redwood lovers ②hunting lovers ③fishing lovers ④hiking loversA．①②B．③④C．①④D．②③61．What can be learned from the passage?A．Muir woods is surrounded by highland and ocean beaches.B．Visitors can read electronic maps using Wi-Fi in Muir Woods.C．Visitors are advised to call Visitor Center for safety tips and regulations.D．Reservations should be made if visitors drive private cars to Muir Woods.62．According to the map of Muir Woods, ______.A．Bridge 4 is the farthest from the parking lots of all bridgesB．Mill Valley is located on the southwest side of Muir BeachC．Bootjack Trail can lead one to Visitor Center from Bridge 3D．food and gifts can be bought on various sites in Muir Woods（C）Precognitive dreams are dreams that seemingly predict the future which cannot be inferred from actually available information. Former US President Abraham Lincoln once revealed the frightening dream to his law partner and friend Ward Hill Lamon, “…Then I heard people weep… ‘Who is dead in the White House?’ I demanded. ‘The President,’ ‘he was killed!’…” The killing did happen later.Christopher French, Professor in the Department of Psychology at Goldsmiths, stated the most likely explanation for such a phenomenon was coincidence（巧合）. “In addition to pure coincidences we must also consider the unreliability of memory”, he added. Asked what criteria would have to be met for him to accept that precognitive dreams were a reality, he said, “The primary problem with tests of the claim is that the subjects are unable to tell when the event（s）they’ve dreamed about will happen.”However, some claimed to make such tests practicable. Professor Caroline Watt at the University of Edinburgh, has conducted studies into precognitive dreaming. She stated that knowing future through dreams challenged the basic assumption of science — causality （relationship of cause and effect）.Dick Bierman, a retired physicist and psychologist, who has worked at the Universities of Amsterdam, Utrecht and Groningen, has put forward a theory that may explain precognitive dreams. It is based on the fact that when scientists use certain mathematical descriptions to talk about things like electromagnetism（电磁学）, these descriptions favour the belief that time only moves in one direction. However, in practice the wave that is running backwards in time does exist. This concept is called the time symmetry, meaning that the laws of physics look the same when time runs forward or backward. But he believes that time symmetry breaks down due to external conditions. “The key of the theory is that it assumes that there is a special context that restores th e broken time-symmetry, if the waves running backwards are ‘absorbed’ by a consistent multi-particle（多粒子）system. The brain under a dream state may be such a system where broken time-symmetry is partially restored. This is still not a full explanation for precognitive dreams but it shows where physics might be adjusted to accommodate the phenomenon,” he explains.Although Bierman’s explanation is still based on guesses and has not accepted by mainstream science, Watt does think it is worth considering. For now, believing that it’s possible to predict future with dreams remains an act of faith. Yet, it’s possible that one day we’ll wake up to a true understanding of this fascinating phenomenon. 63．According to French, what makes it difficult to test precognitive dreams?A．Unavailability of people’s dreams.B．That coincidences happen a lot in reality.C．That criteria for dream reliability are not trustworthy.D．People’s inability to tell when dreamt events will happen.64．Believers in precognitive dreams may question the truth of ______.A．the assumption of causality B．the time symmetryC．memories of ordinary people D．modern scientific tests65．We can infer from the passage that ______.A．Lincoln was warned of the killing by his friendB．Watt carried out several experiments on causalityC．researches on electromagnetism are based on the time symmetryD．time’s moving in two directions may justify precognitive dreams66．Which might be the best title of the passage?A．Should Dreams Be Assessed?B．Can Dreams Predict the Future?C．How Can Physics Be Changed to Explain Dreams?D．Why Should Scientists Study Precognitive Dreams?Section CDirections: Read the following passage. Fill in each blank with a proper sentence given in the box. Each sentence can only be used once. Note that there are two sentences more than you need.A．Labeling poses even more of a problem when it comes to kids.B．It can be helpful for those not quite able to understand why they feel the way they do.C．There seems to be a desire to see negative emotions as something requiring intervention or diagnosis. D．Labeling leads to children’s overcoming their addiction to what is posted online.E．Someone has had only a certain experience and judges all behavior with that experience.F．The basic function of a diagnosis is to give you a name for those behaviors once felt unusual.Addiction to LabelingMaybe you’ve noticed it in the comments section of popular social media posts about anxiety. depression or things alike, with a number of people claiming to pick these labels for themselves.These days, labeling is everywhere. （67）______ However, the negative part is that it’s easy for someone to identify with the characteristics without truly recognizing the context in which these characteristics would require diagnosis, according to Charlotte Armitage, a registered integrative psychotherapist and psychologist.If you have done your research and genuinely feel that you have some form of mental health concern, then finally having a name for your behaviors can be great. But the risk is that many people will seek labels and intervention for any behavior, pattern or emotion that is outside of the permanent happy group that society has set as the norm. “（68）______ Then the saying ‘a little bit of knowledge is dangerous’ springs to my mind,” Armitage adds.（69）______“Children are still developing and evolving, and many childhood behavioral features may seem like those of a disorder when there’re other potential explanations for that behavior,” Armitage notes. Ideally, a diagnosis for a child should be carried out by a qualified mental health professional. So it is with an adult.Nevertheless, the most important thing to bear in mind is that diagnosis doesn’t mean to indicate that you are broken or less capable.（70）______ And if you go deeper, it can alert you to the fact that you are not alone, and that many people experience life in the same way as you do.Ⅳ．Summary WritingDirections: Read the following passage. Summarize the main idea and the main point（s）of the passage in no more than 60 words. Use your own words as far as possible.71．Why Willing to Wait?First it was the fried chicken. Then a variety of fancy milkshakes. No matter what time it is or how bad the streets smell, there are plenty of people waiting in line for hours to get their hands on the food that everyone’s talking about. If you are not the type of person crazy for trendy foods, you probably wonder why someone would like to wait in a long line just to get a taste of a popular cream tea. There is a bit of psychology behind the craze of waiting before getting one’s chopsticks on a trendy food.People are born curiosity hunters, especially for fresh ideas, according to some experts. At the sight of a long waiting line, they just can’t help having a try. And when the trendy foods are novel in looks and favors, even innovative in their sales environment, the desire for them is upgraded. All those stimulate people to investigate more—to deal with their curiosity.In addition, having access to something that is sought out but hard to possess equips people with a feeling that improves their self-definitions. When someone is envied due to something he gained with efforts, his self-worth gets enhanced. Although it is yet to be determined whether the number of likes he receives on the photos of foods he’s posted online is connected with the level of envy from on-lookers, that feeling automatically becomes stronger.Even more, “mob psychology” comes into play: when many people are doing something—waiting in line for the sought-after milkshakes, for instance —others are eager to be part of the group and share such a type of social familiarity, kind of like the natural pursuit of a sense of belonging. Tasting the same wait-worthy food has something in common.Ⅴ．TranslationDirections: Translate the following sentences into English, using the words given in the brackets.72．大多数中国人喜欢在生日的时候吃碗面。

Listening to music while doing homework is a topic that has been debated for years. Some people believe that music can help improve concentration and make the task of doing homework more enjoyable,while others argue that it can be distracting and hinder the learning process.Here are some points to consider:1.Music Preferences:The type of music you listen to can make a difference.Instrumental music or classical music is often recommended for studying because it is less likely to contain lyrics that can be distracting.2.Personal Preference:Some individuals find that music helps them focus better,while others may find it distracting.Its important to understand your own learning style and how music affects your concentration.3.Volume Level:Keeping the volume at a moderate level can help maintain focus without being too distracting.4.Task Complexity:For more complex tasks that require deep thought and concentration, music might be more of a hindrance.However,for simpler tasks,music could potentially enhance the experience.5.Mood Enhancement:Music can improve mood,which can,in turn,make the task of doing homework more enjoyable and less of a chore.6.Background Noise:For some,music acts as a form of background noise that can help to drown out other distractions in the environment.7.Study Breaks:Music can also be used during study breaks to help relax and recharge before diving back into work.8.Cognitive Load:Studies have shown that music can interfere with the cognitive load ofa task,especially if the music is complex or contains lyrics in a language you understand.9.Productivity Techniques:Some productivity techniques,like the Pomodoro Technique, suggest using music as a reward for completing a focused work session.10.Experimentation:Ultimately,it may be beneficial to experiment with different types of music and see what works best for your personal study habits.Remember,the effectiveness of listening to music while doing homework can vary greatly from person to person,and its essential to find what works best for you.。

美联英语提供：囧研究：想要量身定做音乐？快来扫描脑电波两分钟做个小测试，看看你的英语水平/test/kuaisu.aspx?tid=16-73675-0Companies and composers have begun using software to makemusic customized to your brainwaves. 一些公司和音乐创作者开始借助软件，根据脑电波为人们量身定做音乐。

Soon you will be able to plug in your headphones, lean back in your chair, and relax to a playlist so synchronized with your brain’s chemistry that it increasesyour productivity, sleep quality, and even fights anxiety.再过不久，你就能插着耳机，躺在椅子上，听着播放列表里与你大脑化学物质相符的音乐—这些音乐能够帮助你提高效率与睡眠质量，甚至克服焦虑。

The frequency at which your brain resonates defines your state of mind. Needto chill out? Try alpha activity. Or what about a pre-workout pep-up? Pop on some beta waves.大脑的共振频率决定我们的精神状态。

需要放松一下吗？刺激大脑的α电波吧。

或者想要热热身？那就刺激一下大脑的β电波吧。

As consumer desire for personalized information and outcomes increases, the ability to listen to music that is literally in tune with your brain will provide a whole new business opportunity in the world of music streaming.由于人们对于个性化信息与结果的消费欲望日益加强，能够听到与人类大脑相得益彰的音乐对于世界流媒体音乐来说，无疑是个全新的商机。

EDCircles:A real-time circle detector with a false detection controlCuneyt Akinlar n,Cihan TopalDepartment of Computer Engineering,Anadolu University,Eskisehir26470,Turkeya r t i c l e i n f oArticle history:Received9April2012Received in revised form21September2012Accepted26September2012Available online3October2012Keywords:Circle detectionEllipse detectionReal-time image processingHelmholtz PrincipleNFAa b s t r a c tWe propose a real-time,parameter-free circle detection algorithm that has high detection rates,producesaccurate results and controls the number of false circle detections.The algorithm makes use of thecontiguous(connected)set of edge segments produced by our parameter-free edge segment detector,theEdge Drawing Parameter Free(EDPF)algorithm;hence the name EDCircles.The proposed algorithmfirstcomputes the edge segments in a given image using EDPF,which are then converted into line segments.The detected line segments are converted into circular arcs,which are joined together using two heuristicalgorithms to detect candidate circles and near-circular ellipses.The candidates arefinally validated by an acontrario validation step due to the Helmholtz principle,which eliminates false detections leaving onlyvalid circles and near-circular ellipses.We show through experimentation that EDCircles works real-time(10–20ms for640Â480images),has high detection rates,produces accurate results,and is very suitablefor the next generation real-time vision applications including automatic inspection of manufacturedproducts,eye pupil detection,circular traffic sign detection,etc.&2012Elsevier Ltd.All rights reserved.1.IntroductionDetection of circular objects in digital images is an importantand recurring problem in image processing[1]and computervision[2],and has many applications especially in such automa-tion problems as automatic inspection of manufactured products[3],aided vectorization of line drawing images[4,5],pupil and irisdetection[6–8],circular traffic sign detection[9–11],and manyothers.An ideal circle detection algorithm should run with afixed set ofinternal parameters for all images,i.e.,require no parameter tuningfor different images,be very fast(real-time if possible),detectmultiple small and large circles,work with synthetic,natural andnoisy images,have high detection rate and good accuracy,andproduce a few or no false detections.The circle detection algorithmpresented in this paper satisfies all of these properties.Traditionally,the most popular circle detection techniques arebased on the famous circle Hough transform(CHT)[12–16].Thesetechniquesfirst compute an edge map of the image using atraditional edge detector such as Canny[17],map the edge pixelsinto the three dimensional Hough circle space(x,y,r)and extractcircles that contain a certain number of edge pixels.Not onlyCHT-based techniques are very slow and memory-demanding,butthey also produce many false detections especially in the presenceof noise.Additionally,these methods have many parameters thatmust be preset by the user,which greatly limits their use.To overcome the limitations of the classical CHT-based meth-ods,many variants have been proposed including probabilistic HT[18,19],randomized HT[20,21],fuzzy HT[22],etc.There are alsoapproaches based on HT and hypothesisfiltering[23–25].Allthese methods try to correct different shortcomings of CHT,butare still memory-demanding and slow to be of any use in real-time applications.Apart from the CHT-based methods,there are several rando-mized algorithms for circle detection.Chen et al.[26]propose arandomized circle detection(RCD)algorithm that randomly selectsfour pixels from the edge map of an image,uses a distance criterionto determine whether there is a possible circle in the image.Theythen use an evidence-collecting step to test if the candidate circle isa real-circle.RCD produces good results,but is slow.Recently,Chunget al.[27,28]have proposed efficient sampling and refinementstrategies to speed up RCD and increase the accuracy of RCD’sresults.Although the new RCD variants named GRCD-R,GLRCD-R[28]have good detection rates and produce accurate results,theystill are far from being real-time.Furthermore,all RCD-variantswork on the edge map of an image computed by a traditional edgedetector such as the Sobelfilter or the Canny edge detector,whichhave many parameters that must be set by the user.Recently,many efforts have concentrated on using geneticalgorithms and evolutionary computation techniques in circledetection[29–36].Ayala-Ramirez et al.[30]proposed a geneticalgorithm(GA)for circle detection,which is capable of detectingmultiple circles but fails frequently to detect small or imperfectContents lists available at SciVerse ScienceDirectjournal homepage:/locate/prPattern Recognition0031-3203/$-see front matter&2012Elsevier Ltd.All rights reserved./10.1016/j.patcog.2012.09.020n Corresponding author.Tel.:þ902223213550x6553.E-mail addresses:cakinlar@.tr(C.Akinlar),cihant@.tr(C.Topal).Pattern Recognition46(2013)725–740circles.Dasgupta et al.[31–33]developed a swarm intelligence technique named adaptive bacterial foraging optimization (ABFO)for circle detection.Their algorithm produces good results but is sensitive to noise.Cuevas et e discrete differential evolution (DDE)optimization [34],harmony search optimization (HSA)[35]and an artificial immune system optimization technique named Clonal Selection Algorithm (CSA)[36]for circle detection.Although these evolutionary computation techniques have good detection rates and accurate results,they usually require multiple runs to detect multiple circles,and are quite slow to be suitable for real-time applications.Just like RCD,these algorithms work on an edge map pre-computed by a traditional edge detection algorithm with many parameters.Frosio et al.[37]propose a real-time circle detection algorithm based on maximum likelihood.Their method is fast andcan detect partially occluded circular objects,but requires that the radius of the circles to be detected be predefined,which greatly limits its applications.Wu et al.[41]present a circle detection algorithm that runs 7frames/s on 640Â480images.The authors claim to achieve high success rate,but there is not much experi-mental validation to back their claims.Zhang et al.[38]propose an ellipse detection algorithm that can be used for real-time face detection.Liu et al.[39]present an ellipse detector for noisy images and Prasad et al.[40]present an ellipse detector using the edge curvature and convexity information.While both algorithms produce good results,they are slow and not suitable for real-time applications.Vizireanu et al.[42–44]make use of mathematical morphol-ogy for shape decomposition of an image and use the morpholo-gical shape decomposition representation of the image for recognition of different shapes and patterns in the image.While their algorithms are good for the detection of general shapes in an image,they are not suitable for real-time applications.Desolneux et al.[60]is the first to talk about the a contrario circular arc detection.Recently,Patraucean et al.[45,46]propose a parameter-free ellipse detection algorithm based on the a contrario framework of Desolneux et al.[58].The authors extend the line segment detector (LSD)by Grompone von Gioi et al.[63]to detect circular and elliptic arcs in a given image without requiring any parameters,while controlling the number of false detections by the Helmholtz principle [58].They then use the proposed algorithm (named ELSD [46])for the detection identification of Bubble Tags [47].In this paper,we present a real-time (10–20ms on 640Âimages),parameter-free circle detection algorithm that has high detection rates,produces accurate results,and has an a contrario validation step due to the Helmholtz principle that lets it control the number of false detections.The proposed algorithm makes use of the contiguous (connected)set of edge segments produced by our parameter-free edge segment detector,the edge drawing parameter free (EDPF)[48–53];hence the name EDCircles [54,55].Given an input image,EDCircles first computes the edge segments of the image using EDPF.Next,the resulting edge segments are turned into line segments using our line segment detector,EDLines [56,57].Computed lines are then converted into arcs,which are combined together using two heuristic algorithms to generate many candidate circles and near-circular ellipses.Finally,the candidates are vali-dated by the Helmholtz principle [58–63],which eliminates false detections leaving only valid circles and near-circular ellipses.2.The proposed algorithm:EDCirclesEDCircles follows several steps to compute the circles in a given image.The general idea is to extract line segments in an image,convert them into circular arcs and then combine these arcs to detect circles and near-circular ellipses.General outline ofEDCircles algorithm is presented in Algorithm 1and we will describe each step of EDCircles in detail in the following sections.Algorithm 1.Steps of EDCircles algorithm.1.Detect edge segments by EDPF and extract complete circles and ellipses.2.Convert the remaining edge segments into line segments.3.Detect arcs by combining line segments.4.Join arcs to detect circle candidates.5.Join the remaining arcs to detect near-circular ellipse candidates.6.Validate the candidate circles/ellipses using the Helmholtz principle.7.Output the remaining valid circles/ellipses.2.1.Edge segment detection by edge drawing parameter free (EDPF)Given an image,the first step of EDCircles is the detection of the edge segments in the image.To achieve this,we employ our recently proposed,real-time edge/edge segment detector,edge drawing (ED)[48–51].Unlike traditional edge detectors,e.g.,Canny [17],which work by identifying a set of potential edge pixels in an image and eliminating non-edge pixels through operations such as non-maximal suppression,hysteresis thresh-olding,erosion,etc.,ED follows a proactive approach and works by first identifying a set of points in the image,called the anchors,and then joins these anchors using a smart routing procedure;that is,ED literally draws edges in an image.ED outputs not only a binary edge map similar to those output by traditional edge detectors,but it also outputs the result as a set of edge segments each of which is a contiguous (connected)pixel chain [49].ED has many parameters that must be set by the user,which requires the tuning of ED’s parameters for different types of images.Ideally,one would want to have a real-time edge/edge segment detector which runs with a fixed set of internal parameters for all types of images and requires no parameter tuning.To achieve this goal,we have recently incorporated ED with the a contrario edge validation mechanism due to the Helmholtz principle [58–60],and obtained a real-time parameter-free edge segment detector,which name edge drawing parameter free (EDPF)[52,53].EDPF works running ED with all ED’s parameters at their extremes,which all possible edge segments in a given image with many false positives.We then validate the extracted edge segments by the Helmholtz principle,which eliminates false detections leaving only perceptually meaningful edge segments with respect to the a contra-rio approach.Fig.1(a)shows a 424Â436grayscale synthetic image contain-ing a big circle obstructed by four rectangular blocks,a small ellipse obstructed by three rectangular blocks,a small circle,an ellipse and an arbitrary polygon-like object.When this image is fed into EDPF,the edge segments shown in Fig.1(b)are produced.Each color in the edge map represents a different edge segment,each of which is a contiguous chain of pixels.For this image,EDPF outputs 15edge segments in just 3.7ms in a PC with 2.2GHz Intel 2670QM CPU.Notice the high quality nature of the edge map with all details clearly visible.Each edge segment traces the boundary of one or more objects in the figure.While the boundary of an object may be traced by a single edge segment,as the small circle,the ellipse and the polygonal object are in Fig.1(b),it is also possible that an object’s boundary be traced by many different edge segments.This is the case for the big circle as the circle’s boundary is traced by four different edge segments,and the small obstructed ellipse,which is traced by three different edge segments.The result totally depends on the structure of the objects,the amount of obstruction and noise in the image.That is,there is noC.Akinlar,C.Topal /Pattern Recognition 46(2013)725–740726ellipse and the polygon,the entire boundary of anobject in the image is returned as a closed curve;that is,the edge segment starts at a pixel on the boundary of an object,traces its entire boundary and ends at where it starts.In other words,the first and last pixels of the edge segment are neighbors of each other.It is highly likely that such a closed edge segment traces the boundary of a circle,an ellipse or a polygonal shape as is the case in Fig.1.So as the first step after the detection of the edge segments,we go over all edge segments,take the closed ones and see if the closed edge segment traces the entire boundary of a circle or an ellipse.Processing of a closed edge segment follows a very simple idea:We first fit a circle to the entire list of pixels in the edge segment using the least squares circle fit algorithm [64]and compute the root mean square error.If the circle fit error,i.e.,the root mean square error,is smaller than some threshold (fixed at 1.5pixels for the proposed algorithm),then we add the circle to the list of circle candidates.Just because the circle fit error is small does not mean that the edge segment is an actual circle;it is just a candidate yet and needs to go through circle validation by the Helmholtz principle to be returned as a real circle.Section 2.6describes the details of circle validation.If the circle fit fails,then we try fitting an ellipse to the pixels of the edge segment.We use the ellipse fit algorithm described in [65],which returns an ellipse equation of the form Ax 2þBxy þCy 2þDx þEy þF ¼0.If the ellipse fit error,i.e.,the root mean square error,is smaller than a certain threshold (fixed at 1.5pixels for the proposed algorithm),then we add the ellipse to the list of ellipse candidates,which also needs to go through validation by the Helmholtz principle before being returned as a real ellipse.If the edge segment is accepted either as a circle or an ellipse candidate,it is removed from the list of edge segments and is not processed any further.Otherwise,the edge segment is used in further processing along with other non-closed edge segments.2.2.Conversion of edge segments into line segmentsAfter the removal of the closed edge segments,which are taken as circle or ellipse candidates,the remaining edge segments are converted into line segments (lines for short in the rest of the paper).The motivation for this step comes from the observation that any circular shape is approximated by a consecutive set of lines (as seen in Fig.1(c)),and these lines can easily be turned into circular arcs by a simple post-processing step as described in the next section.Conversion of an edge segment into a set of lines follows the algorithm given in our line detector,EDLines [56,57].The idea is to start with a short line that satisfies a certain straightness criterion,and extend the line for as long as the root mean square error is smaller than a certain threshold,i.e.,1pixel error.Refer to EDLines [56,57]for the details of line segment extraction,where we validate the lines after detection using the Helmholtz principle to eliminate invalid detections.In EDCircles,though,we do not validate the lines after detection.The reason for this decision comes from our observation that the line segment validation algorithm due to the Helmholtz principle usually eliminates many short lines,which may be valuable for the detection of small circles in an image.So,unlike EDLines,we do not eliminate any detected lines and use all detected lines for further processing and detection of arcs.Fig.1(c)shows the lines extracted from the image shown in Fig.1(a).Clearly,circular objects are approximated by a set of consecutive lines.In the next section,we describe how these lines can be converted into circular arcs by processing of consecutive lines.2.3.Circular arc detectionWe at least three consecutive lines that Using this definition,we detect a list of lines making up an edge segment,simply walk over the lines and compute the angle between consecutive lines and the direction of turn from one line to the next.If at least three lines turn in the same direction and the angle between the lines is in-between certain thresholds,then these lines may form a circular arc.Fig.2illustrates a hypothetical edge segment being approxi-mated by 18consecutive line segments,labeled l 1through l 18.To compute the angle between two consecutive lines,we simply threat each line as a vector and compute the vector dot product.Similarly,to compute the turn of direction from one line to the next,we simply compute the vector cross product and use the sign of the result as the turn direction.Fig.3(a)illustrates the approximation of the blue right vertical edge segment in Fig.1(b)by 11consecutive line segments,labeled v1through v11.Fig.3(b)shows the details of the 11lines:their lengths,the angle between consecutive lines and the direction of the turn going from one line to the next,where a ‘þ’denotes a left turn,and ‘À’denotes a right turn.Our arc detection algorithm is based on the following idea:For a set of lines to be a potential arc candidate,they all must have the same turn direction (to the left or to the right)and the angle between consecutive lines must be in-between certain thresh-olds.If the angle is too small,we assume that the lines are collinear so they cannot be part of an arc;if the angle is too big,we assume that the lines are part of a strictly turning object such as a square,a rectangle,etc.For the purposes of our current implementation,we fix the low angle threshold to 61,and the high angle threshold to 601.These values have been obtained byFig.1.(a)A sample image (424Â436).(b)Edge segments (a contiguous chain of pixels)extracted by EDPF.Each color represents a different edge segment.EDPF outputs 15edge segments in 3.7milliseconds (ms).(c)Lines approximating the edge segments.A total of 98lines are extracted.(For interpretation of the references to color in this figure caption,the reader is referred to the web version of this article.)C.Akinlar,C.Topal /Pattern Recognition 46(2013)725–740727experimentation on a variety ofimages containing various circular objects.The bottom part of Fig.2depicts the angles between consecutive lines of the edge segment shown at the top of Fig.2,and the turn of direction from one line to the next.The angles smaller than the low angle threshold or bigger than the high angle threshold,e.g.,y 1,y 2,y 9,and y 16,have been colored red;all other angles have been colored either blue or green depending on the turn of direction.Specifically,if the next line turns to the left,the angle has been colored blue,and if the next line turns to the right,then the angle has been colored green.Having computed the angles and the turn of direction infor-mation,we simply walk over the lines of an edge segment lookingfor a set of at least three consecutive lines which all turn in the same direction and the turn angle from one line to the next is in-between the low and high angle thresholds.In Fig.2,lines v 3through v 7satisfy our criterion and is a potential arc candidate.Similarly,lines v 10through v 16make up for another arc candidate.Given a set of at least three lines that satisfy our arc candidate constraints,we first try fitting a circle to all pixels making up the lines using the circle fit algorithm in [64].If the circle fit succeeds,i.e.,if the root mean square error is less than 1.5pixels,then the extracted arc is simply added to the list of arcs,and we are done.Otherwise,we start with a short arc consisting of only of three lines and extend it line-by-line by fitting a new circle [64]until the root mean square error exceeds 1.5pixels.At this point,the to the list of arcs,and we continue processing detect more circular ing this algorithm,in Fig.2:Lines v 3through v 7form arc A1with center ðx c A 1,y c A 1Þand radius r A 1.Similarly,lines v 10through v 16form arc A 2with center ðx c A 2,y c A 2Þand radius r A 2.In a complex image consisting of many edge segments,we will have hundreds of arcs.Fig.4shows the arcs computed from the lines of Fig.1(c),and Table 1gives the details of these arcs.An arc spans a part between (StartAngle,EndAngle)of the great circle specified by (Center X,Center Y,Radius).The arc is assumed to move counter-clockwise from StartAngle to EndAngle over the great circle.As an example,A 2covers a total of 611from 911to 1521of the great circle with center coordinates (210.6,211.3)and radius ¼182.6.2.4.Candidate circle detection by arc joinAfter the computation of the arcs,the next step is to join the arcs into circle candidates.To do this,we first sort all arcs with respect to their length in descending order,and start extending the longest arc first.The motivation for this decision comes from the observation that the longest arc is the closest to a full circle,so it must be extended and completed into a full circle before theFig.3.(a)An illustration of the blue right vertical segment in Fig.1(b)being approximated by 11consecutive line segments labeled v 1through v 11.The angle between line segments v 1and v 2(y 1),v 3and v 4ðy 3Þ,v 7and v 8ðy 7Þ,and v 10and v 11(y 10)are also illustrated.(b)Lines making up the blue right vertical segment in Fig.1(b).Fig.2.(a)A hypothetical edge segment being approximated by 18consecutive line segments labeled l 1through l 18.(b)The angle y i between v i and v i þ1are illustrated and colored with red,green or blue.If the angle is bigger than a high threshold,e.g.,y 1,y 2and y 9(colored red),or if the angle is smaller than a low threshold,e.g.,y 16(also colored red),then these lines cannot be part of an arc.Otherwise,if three or more consecutive lines turn to the left,e.g.,lines v 3through v 7(angles colored blue),then these lines may form an arc.Similarly,if three or more consecutive lines turn to the right,e.g.,lines v 10through v 16(angles colored green),then these lines may form an arc.(For interpretation of the references to color in this figure caption,the reader is referred to the web version of this article.)C.Akinlar,C.Topal /Pattern Recognition 46(2013)725–740728other arcswould.During the extension of an arc,the idea is to look for arcs having similar radii and close centers,and collect a list of candidate arcs that may be combined with the current arc.Given an arc A 1to extend into a full circle,we go over all detected arcs and generate a set of candidate arcs that may be joined with A 1.We have two criterions for arc join:(1)Radius difference constraint:The radius difference between A 1and the candidate arc A 2must be within some threshold.Specifically,if A 2’s radius is within 25%of A 1’s radius,then A 2is taken as a candidate for join;otherwise A 2cannot be joined with A 1.As an example,if A 1’s radius is 100,then all arcs whose radii are between 75and 125would be taken as candidates for arc join.(2)Center distance constraint:The distance between the center of A 1and the center of the candidate arc A 2must be within some threshold.Specifically,we require that the distance between the centers of A 1and A 2must not exceed 25%of A1’s radius.As an example,if A 1’s radius is 100,then all arcs whose centers are within 25pixels of A 1’s center would be taken as candidates for arc join assuming they also satisfy the radius difference constraint.Fig.5illustrates possible scenarios during arc join for circle detection.In Fig.5(a),we illustrate a case where all potential arc candidates satisfy the center distance constraint,but one fails the radius difference constraint.Here,A 1is the arc to be extended with A 2,A 3and A 4as potential candidates for arc join.As illustrated,the centers of all arcs are very close to each other;that is,the distance of the centers of A 2,A 3and A 4from the center of A 1are all within the center distance threshold r T .As for the radius difference constraint,only A 3and A 4satisfy it,while A 2’s radius falls out of the radius difference range.So in Fig.5(a),only arcs A 3and A 4would be selected as candidates for joining with A 1.In Fig.5(b),we illustrate a case where all potential arc candidates satisfy the radius difference constraint,but one fails the center distance constraint.Here,A 1is the arc to be extended with A 2,A 3and A 4as potential candidates for arc join.As illustrated,the radii ofall arcs are very close to each other,so they all satisfy the radius difference constraint.As for the center distance constraint,only A 2and A 4satisfy it,while A 3’s center falls out of the center distance threshold r T .So in Fig.5(b),only arcs A 2and A 4would be selected as candidates for joining with A 1.After the computation of the candidate arcs,the next step is to combine them one-by-one with the extended arc A 1by fitting a new circle to the pixels making up both of the arcs.Instead of trying the join in random order,we start with the arc whose either end-point is the closest to either end-point of A 1.The motivation for this decision comes from the observation that if there is more than one arc that is part of the same great circle,it is better to start the join with the arc closest to the extended arc A 1.In Fig.5(a)for example,we would first join A 1with A 4and then with A 3.Similarly,in Fig.5(b)we would first join A 1with A 2and then A 4.After an arc A 1is extended with other arcs on the same great circle,we decide at the last step whether to make the extended arc a circle candidate.Here,we take the view that if an arc spans at least 50%of the circumference of its great circle,then we make the arc a circle candidate.Otherwise,the arc is left for circular ellipse detection.In Fig.5(a)for example,when A 1joined with A 4and A 3,the extended arc would span more 50%of the circumference of its great circle.So the extended arc would be made a circle candidate.In Fig.5(c)however,when A 1,A 2and A 3are joined together,we observe that the extended arc does not span at least 50%of the circumference of its great circle,i.e.,y 1þy 2þy 3o p ;so the extended arc is not taken as a circle putation of the total arc span is performed by simply looking at the ratio of the total number of pixels making up the joined arcs to the circumference of the newly fitted circle.If this ratio is greater than 50%,then the extended arc is taken as a circle candidate.To exemplify the ideas presented above,here is how the seven arcs depicted in Fig.4(a)and detailed in Table 1would be processed:we first take A 1,the longest arc,as the arc to be extended,with A 2,A 3,A 4,A 5,A 6and A 7as the remaining arcs.Since the radii of A 2,A 3and A 4are within 25%of A 1’s radius and their center distances are within the center distance threshold,only these three arcs would be taken as candidates for join.We next join A 1and A 2since A 2’s end-point is closest to A 1(refer to Fig.4(a)).After A 1and A 2are joined,the extended arc would now be joined with A 3since A 3’s end-point would now be closest to the extended arc.Finally,A 4would be joined.Since the final extended arc covers more than 50%of its great circle,it is taken as a circle candidate.Continuing similarly,the next longest remaining arc is A 5,so we try extending A 5with A 6and A 7being the only remaining arcs in our list of arcs.The only candidateFig.4.(a)Arcs computed from the lines of Fig.1(c).(b)Candidate circles and ellipses before validation (overlayed on top of the image with red color).(For interpretation of the references to color in this figure caption,the reader is referred to the web version of this article.)Table 1Details of the arcs shown in Fig.4(a).An arc spans a part between (StartAngle,EndAngle)of a great circle specified by (center X,center Y,Radius).The arc moves counter-clockwise from StartAngle to EndAngle over the circle.ArcCenter XCenter YRadiusStart angle (deg.)End angle (deg.)A 1210.3211.8182.232586A 2210.6211.3182.691152A 3212.2215.9178.5275312A 4210.7211.6183.0173264A 5111.1267.552.3275312A 6120.1291.434.9141219A 7139.4288.649.294143C.Akinlar,C.Topal /Pattern Recognition 46(2013)725–740729。

Cognitive Radio:Brain-Empowered Wireless CommunicationsSimon Haykin,Life Fellow,IEEEInvited PaperAbstract—Cognitive radio is viewed as a novel approach for im-proving the utilization of a precious natural resource:the radio electromagnetic spectrum.The cognitive radio,built on a software-defined radio,is de-fined as an intelligent wireless communication system that is aware of its environment and uses the methodology of under-standing-by-building to learn from the environment and adapt to statistical variations in the input stimuli,with two primary objectives in mind:•highly reliable communication whenever and wherever needed;•efficient utilization of the radio spectrum.Following the discussion of interference temperature as a new metric for the quantification and management of interference,the paper addresses three fundamental cognitive tasks.1)Radio-scene analysis.2)Channel-state estimation and predictive modeling.3)Transmit-power control and dynamic spectrum manage-ment.This paper also discusses the emergent behavior of cognitive radio. Index Terms—Awareness,channel-state estimation and predic-tive modeling,cognition,competition and cooperation,emergent behavior,interference temperature,machine learning,radio-scene analysis,rate feedback,spectrum analysis,spectrum holes,spec-trum management,stochastic games,transmit-power control, waterfilling.I.I NTRODUCTIONA.BackgroundT HE electromagnetic radio spectrum is a natural resource, the use of which by transmitters and receivers is licensed by governments.In November2002,the Federal Communica-tions Commission(FCC)published a report prepared by the Spectrum-Policy Task Force,aimed at improving the way in which this precious resource is managed in the United States[1]. The task force was made up of a team of high-level,multidis-ciplinary professional FCC staff—economists,engineers,and attorneys—from across the commission’s bureaus and offices. Among the task force majorfindings and recommendations,the secondfinding on page3of the report is rather revealing in the context of spectrum utilization:Manuscript received February1,2004;revised June4,2004.The author is with Adaptive Systems Laboratory,McMaster University, Hamilton,ON L8S4K1,Canada(e-mail:haykin@mcmaster.ca).Digital Object Identifier10.1109/JSAC.2004.839380“In many bands,spectrum access is a more signifi-cant problem than physical scarcity of spectrum,in large part due to legacy command-and-control regulation that limits the ability of potential spectrum users to obtain such access.”Indeed,if we were to scan portions of the radio spectrum in-cluding the revenue-rich urban areas,we wouldfind that[2]–[4]: 1)some frequency bands in the spectrum are largely unoc-cupied most of the time;2)some other frequency bands are only partially occupied;3)the remaining frequency bands are heavily used.The underutilization of the electromagnetic spectrum leads us to think in terms of spectrum holes,for which we offer the fol-lowing definition[2]:A spectrum hole is a band of frequencies assigned to a pri-mary user,but,at a particular time and specific geographic lo-cation,the band is not being utilized by that user.Spectrum utilization can be improved significantly by making it possible for a secondary user(who is not being serviced)to access a spectrum hole unoccupied by the primary user at the right location and the time in question.Cognitive radio[5],[6], inclusive of software-defined radio,has been proposed as the means to promote the efficient use of the spectrum by exploiting the existence of spectrum holes.But,first and foremost,what do we mean by cognitive radio? Before responding to this question,it is in order that we address the meaning of the related term“cognition.”According to the Encyclopedia of Computer Science[7],we have a three-point computational view of cognition.1)Mental states and processes intervene between inputstimuli and output responses.2)The mental states and processes are described byalgorithms.3)The mental states and processes lend themselves to scien-tific investigations.Moreover,we may infer from Pfeifer and Scheier[8]that the interdisciplinary study of cognition is concerned with exploring general principles of intelligence through a synthetic method-ology termed learning by understanding.Putting these ideas to-gether and bearing in mind that cognitive radio is aimed at im-proved utilization of the radio spectrum,we offer the following definition for cognitive radio.Cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment(i.e.,outside0733-8716/$20.00©2005IEEEworld),and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters(e.g., transmit-power,carrier-frequency,and modulation strategy)in real-time,with two primary objectives in mind:•highly reliable communications whenever and wherever needed;•efficient utilization of the radio spectrum.Six key words stand out in this definition:awareness,1in-telligence,learning,adaptivity,reliability,and efficiency. Implementation of this far-reaching combination of capabilities is indeed feasible today,thanks to the spectacular advances in digital signal processing,networking,machine learning, computer software,and computer hardware.In addition to the cognitive capabilities just mentioned,a cog-nitive radio is also endowed with reconfigurability.2This latter capability is provided by a platform known as software-defined radio,upon which a cognitive radio is built.Software-defined radio(SDR)is a practical reality today,thanks to the conver-gence of two key technologies:digital radio,and computer soft-ware[11]–[13].B.Cognitive Tasks:An OverviewFor reconfigurability,a cognitive radio looks naturally to soft-ware-defined radio to perform this task.For other tasks of a cognitive kind,the cognitive radio looks to signal-processing and machine-learning procedures for their implementation.The cognitive process starts with the passive sensing of RF stimuli and culminates with action.In this paper,we focus on three on-line cognitive tasks3: 1)Radio-scene analysis,which encompasses the following:•estimation of interference temperature of the radioenvironment;•detection of spectrum holes.2)Channel identification,which encompasses the following:•estimation of channel-state information(CSI);•prediction of channel capacity for use by thetransmitter3)Transmit-power control and dynamic spectrum manage-ment.Tasks1)and2)are carried out in the receiver,and task3)is carried out in the transmitter.Through interaction with the RF 1According to Fette[10],the awareness capability of cognitive radio em-bodies awareness with respect to the transmitted waveform,RF spectrum, communication network,geography,locally available services,user needs, language,situation,and security policy.2Reconfigurability provides the basis for the following features[13].•Adaptation of the radio interface so as to accommodate variations in the development of new interface standards.•Incorporation of new applications and services as they emerge.•Incorporation of updates in software technology.•Exploitation offlexible heterogeneous services provided by radio net-works.3Cognition also includes language and communication[9].The cognitive radio’s language is a set of signs and symbols that permits different internal constituents of the radio to communicate with each other.The cognitive task of language understanding is discussed in Mitola’s Ph.D.dissertation[6];for some further notes,see SectionXII-A.Fig.1.Basic cognitive cycle.(Thefigure focuses on three fundamental cognitive tasks.)environment,these three tasks form a cognitive cycle,4which is pictured in its most basic form in Fig.1.From this brief discussion,it is apparent that the cognitive module in the transmitter must work in a harmonious manner with the cognitive modules in the receiver.In order to maintain this harmony between the cognitive radio’s transmitter and re-ceiver at all times,we need a feedback channel connecting the receiver to the transmitter.Through the feedback channel,the receiver is enabled to convey information on the performance of the forward link to the transmitter.The cognitive radio is, therefore,by necessity,an example of a feedback communica-tion system.One other comment is in order.A broadly defined cognitive radio technology accommodates a scale of differing degrees of cognition.At one end of the scale,the user may simply pick a spectrum hole and build its cognitive cycle around that hole. At the other end of the scale,the user may employ multiple implementation technologies to build its cognitive cycle around a wideband spectrum hole or set of narrowband spectrum holes to provide the best expected performance in terms of spectrum management and transmit-power control,and do so in the most highly secure manner possible.C.Historical NotesUnlike conventional radio,the history of which goes back to the pioneering work of Guglielmo Marconi in December1901, the development of cognitive radio is still at a conceptual stage. Nevertheless,as we look to the future,we see that cognitive radio has the potential for making a significant difference to the way in which the radio spectrum can be accessed with improved utilization of the spectrum as a primary objective.Indeed,given 4The idea of a cognitive cycle for cognitive radio wasfirst described by Mitola in[5];the picture depicted in that reference is more detailed than that of Fig.1. The cognitive cycle of Fig.1pertains to a one-way communication path,with the transmitter and receiver located in two different places.In a two-way com-munication scenario,we have a transceiver(i.e.,combination of transmitter and receiver)at each end of the communication path;all the cognitive functions em-bodied in the cognitive cycle of Fig.1are built into each of the two transceivers.HAYKIN:COGNITIVE RADIO:BRAIN-EMPOWERED WIRELESS COMMUNICATIONS203its potential,cognitive radio can be justifiably described as a “disruptive,but unobtrusive technology.”The term“cognitive radio”was coined by Joseph Mitola.5In an article published in1999,Mitola described how a cognitive radio could enhance theflexibility of personal wireless services through a new language called the radio knowledge represen-tation language(RKRL)[5].The idea of RKRL was expanded further in Mitola’s own doctoral dissertation,which was pre-sented at the Royal Institute of Technology,Sweden,in May 2000[6].This dissertation presents a conceptual overview of cognitive radio as an exciting multidisciplinary subject.As noted earlier,the FCC published a report in2002,which was aimed at the changes in technology and the profound impact that those changes would have on spectrum policy[1].That re-port set the stage for a workshop on cognitive radio,which was held in Washington,DC,May2003.The papers and reports that were presented at that Workshop are available at the Web site listed under[14].This Workshop was followed by a Confer-ence on Cognitive Radios,which was held in Las Vegas,NV,in March2004[15].D.Purpose of this PaperIn a short section entitled“Research Issues”at the end of his Doctoral Dissertation,Mitola goes on to say the following[6]:“‘How do cognitive radios learn best?merits attention.’The exploration of learning in cognitive radio includes the internal tuning of parameters and the external structuring of the environment to enhance machine learning.Since many aspects of wireless networks are artificial,they may be adjusted to enhance machine learning.This dissertation did not attempt to answer these questions,but it frames them for future research.”The primary purpose of this paper is to build on Mitola’s vi-sionary dissertation by presenting detailed expositions of signal-processing and adaptive procedures that lie at the heart of cog-nitive radio.anization of this PaperThe remaining sections of the paper are organized as follows.•Sections II–V address the task of radio-scene analysis, with Section II introducing the notion of interference tem-perature as a new metric for the quantification and man-agement of interference in a radio environment.Section III reviews nonparametric spectrum analysis with emphasis on the multitaper method for spectral estimation,followed by Section IV on application of the multitaper method to noise-floor estimation.Section V discusses the related issue of spectrum-hole detection.•Section VI discusses channel-state estimation and predic-tive modeling.•Sections VII–X are devoted to multiuser cognitive radio networks,with Sections VII and VIII reviewing stochastic games and highlighting the processes of co-operation and competition that characterize multiuser networks.Section IX discusses an iterative water-filling (WF)procedure for distributed transmit-power control. 5It is noteworthy that the term“software-defined radio”was also coined by Mitola.Section X discusses the issues that arise in dynamic spectrum management,which is performed hand-in-hand with transmit-power control.•Section XI discusses the related issue of emergent be-havior that could arise in a cognitive radio environment.•Section XII concludes the paper and highlights the re-search issues that merit attention in the future development of cognitive radio.II.I NTERFERENCE T EMPERATURE Currently,the radio environment is transmitter-centric,in the sense that the transmitted power is designed to approach a pre-scribed noisefloor at a certain distance from the transmitter. However,it is possible for the RF noisefloor to rise due to the unpredictable appearance of new sources of interference, thereby causing a progressive degradation of the signal cov-erage.To guard against such a possibility,the FCC Spectrum Policy Task Force[1]has recommended a paradigm shift in in-terference assessment,that is,a shift away from largelyfixed op-erations in the transmitter and toward real-time interactions be-tween the transmitter and receiver in an adaptive manner.The recommendation is based on a new metric called the interfer-ence temperature,6which is intended to quantify and manage the sources of interference in a radio environment.Moreover, the specification of an interference-temperature limit provides a“worst case”characterization of the RF environment in a par-ticular frequency band and at a particular geographic location, where the receiver could be expected to operate satisfactorily. The recommendation is made with two key benefits in mind.7 1)The interference temperature at a receiving antenna pro-vides an accurate measure for the acceptable level of RF interference in the frequency band of interest;any trans-mission in that band is considered to be“harmful”if it would increase the noisefloor above the interference-tem-perature limit.2)Given a particular frequency band in which the interfer-ence temperature is not exceeded,that band could be made available to unserviced users;the interference-tempera-ture limit would then serve as a“cap”placed on potential RF energy that could be introduced into that band.For obvious reasons,regulatory agencies would be responsible for setting the interference-temperature limit,bearing in mind the condition of the RF environment that exists in the frequency band under consideration.What about the unit for interference temperature?Following the well-known definition of equivalent noise temperature of a receiver[20],we may state that the interference temperature is measured in degrees Kelvin.Moreover,the interference-tem-peraturelimit multiplied by Boltzmann’s constant 6We may also introduce the concept of interference temperature density, which is defined as the interference temperature per capture area of the receiving antenna[16].The interference temperature density could be made independent of the receiving antenna characteristics through the use of a reference antenna.In a historical context,the notion of radio noise temperature is discussed in the literature in the context of microwave background,and also used in the study of solar radio bursts[17],[18].7Inference temperature has aroused controversy.In[19],the National Asso-ciation for Amateur Radio presents a critique of this metric.204IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS,VOL.23,NO.2,FEBRUARY200510joules per degree Kelvin yields the cor-responding upper limit on permissible power spectral densityin a frequency band of interest,and that density is measured injoules per second or,equivalently,watts per hertz.III.R ADIO-S CENE A NALYSIS:S PACE–T IME P ROCESSINGC ONSIDERATIONSThe stimuli generated by radio emitters are nonstationaryspatio–temporal signals in that their statistics depend on bothtime and space.Correspondingly,the passive task of radio-sceneanalysis involves space–time processing,which encompassesthe following operations.1)Two adaptive,spectrally related functions,namely,es-timation of the interference temperature,and detectionof spectrum holes,both of which are performed at thereceiving end of the system.(Information obtained onthese two functions,sent to the transmitter via a feed-back channel,is needed by the transmitter to carry outthe joint function of active transmit-power control and dy-namic spectrum management.)2)Adaptive beamforming for interference control,which isperformed at both the transmitting and receiving ends ofthe system in a complementary fashion.A.Time-Frequency DistributionUnfortunately,the statistical analysis of nonstationary sig-nals,exemplified by RF stimuli,has had a rather mixed history.Although the general second-order theory of nonstationary sig-nals was published during the1940s by Loève[21],[22],it hasnot been applied nearly as extensively as the theory of stationaryprocesses published only slightly previously and independentlyby Wiener and Kolmogorov.To account for the nonstationary behavior of a signal,we haveto include time(implicitly or explicitly)in a statistical descrip-tion of the signal.Given the desirability of working in the fre-quency domain for well-established reasons,we may includethe effect of time by adopting a time-frequency distribution ofthe signal.During the last25years,many papers have been pub-lished on various estimates of time-frequency distributions;see,for example,[23]and the references cited therein.In most ofthis work,however,the signal is assumed to be deterministic.In addition,many of the proposed estimators of time-frequencydistributions are constrained to match time and frequency mar-ginal density conditions.However,the frequency marginal dis-tribution is,except for a scaling factor,just the periodogramof the signal.At least since the early work of Rayleigh[24],it has been known that the periodogram is a badly biased andinconsistent estimator of the power spectrum.We,therefore,donot consider matching marginal distributions to be important.Rather,we advocate a stochastic approach to time-frequencydistributions which is rooted in the works of Loève[21],[22]and Thomson[25],[26].For the stochastic approach,we may proceed in one of twoways.1)The incoming RF stimuli are sectioned into a continuoussequence of successive bursts,with each burst being shortenough to justify pseudostationarity and yet long enoughto produce an accurate spectral estimate.2)Time and frequency are considered jointly under theLoève transform.Approach1)is well suited for wireless communications.In anyevent,we need a nonparametric method for spectral estimationthat is both accurate and principled.For reasons that will be-come apparent in what follows,multitaper spectral estimationis considered to be the method of choice.B.Multitaper Spectral EstimationIn the spectral estimation literature,it is well known thatthe estimation problem is made difficult by the bias-variancedilemma,which encompasses the interplay between two points.•Bias of the power-spectrum estimate of a time series,dueto the sidelobe leakage phenomenon,is reduced by ta-pering(i.e.,windowing)the time series.•The cost incurred by this improvement is an increase invariance of the estimate,which is due to the loss of infor-mation resulting from a reduction in the effective samplesize.How can we resolve this dilemma by mitigating the loss of infor-mation due to tapering?The answer to this fundamental ques-tion lies in the principled use of multiple orthonormal tapers(windows),8an idea that wasfirst applied to spectral estimationby Thomson[26].The idea is embodied in the multitaper spec-tral estimation procedure.9Specifically,the procedure linearlyexpands the part of the time series in afixedbandwidthto(centered on somefrequency)in a special family ofsequences known as the Slepian sequences.10The remarkableproperty of Slepian sequences is that their Fourier transformshave the maximal energy concentration in thebandwidthto under afinite sample-size constraint.This property,in turn,allows us to trade spectral resolution for improved spec-tral characteristics,namely,reduced variance of the spectral es-timate without compromising the bias of the estimate.Given a timeseries,the multitaper spectral estima-tion procedure determines two things.1)An orthonormal sequenceof Slepian tapers denotedby.8Another method for addressing the bias-variance dilemma involves dividingthe time series into a set of possible overlapping segments,computing a pe-riodogram for each tapered(windowed)segment,and then averaging the re-sulting set of power spectral estimates,which is what is done in Welch’s method[27].However,unlike the principled use of multiple orthogonal tapers,Welch’smethod is rather ad hoc in its formulation.9In the original paper by Thomson[36],the multitaper spectral estimationprocedure is referred to as the method of multiple windows.For detailed de-scriptions of this procedure,see[26],[28]and the book by Percival and Walden[29,Ch.7].The Signal Processing Toolbox[30]includes the MATLAB code for Thomson’smultitaper method and other nonparametric,as well as parametric methods ofspectral estimation.10The Slepian sequences are also known as discrete prolate spheroidal se-quences.For detailed treatment of these sequences,see the original paper bySlepian[31],the appendix to Thomson’s paper[26],and the book by Percivaland Walden[29,Ch.8].HAYKIN:COGNITIVE RADIO:BRAIN-EMPOWERED WIRELESS COMMUNICATIONS205 2)The associated eigenspectra defined by the Fouriertransforms(1)The energy distributions of the eigenspectra are concentratedinside a resolution bandwidth,denotedby.The time-band-widthproduct(2)defines the degrees of freedom available for controlling the vari-ance of the spectral estimator.The choice ofparametersandprovides a tradeoff between spectral resolution and variance.11A natural spectral estimate,based on thefirst few eigenspectrathat exhibit the least sidelobe leakage,is givenby(3)where is the eigenvalue associated withthe th eigenspec-trum.Two points are noteworthy.1)The denominator in(3)makes theestimateunbiased.2)Provided that wechoose,then the eigen-value is close to unity,in whichcaseMoreover,the spectralestimate can be improved by theuse of“adaptive weighting,”which is designed to minimize thepresence of broadband leakage in the spectrum[26],[28].It is important to note that in[33],Stoica and Sundin showthat the multitaper spectral estimation procedure can be inter-preted as an“approximation”of the maximum-likelihood powerspectrum estimator.Moreover,they show that for widebandsignals,the multitaper spectral estimation procedure is“nearlyoptimal”in the sense that it almost achieves the Cramér–Raobound for a nonparametric spectral estimator.Most important,unlike the maximum-likelihood spectral estimator,the multi-taper spectral estimator is computationally feasible.C.Adaptive Beamforming for Interference ControlSpectral estimation accounts for the temporal characteristicof RF stimuli.To account for the spatial characteristic of RFstimuli,we resort to the use of adaptive beamforming.12Themotivation for so doing is interference control at the cognitiveradio receiver,which is achieved in two stages.11For an estimate of the variance of a multitaper spectral estimator,we mayuse a resampling technique called Jackknifing[32].The technique bypassesthe need forfinding an exact analytic expression for the probability distribu-tion of the spectral estimator,which is impractical because time-series data(e.g.,stimuli produced by the radio environment)are typically nonstationary,non-Gaussian,and frequently contain outliers.Moreover,it may be argued thatthe multitaper spectral estimation procedure results in nearly uncorrelated coef-ficients,which provides further justification for the use of jackknifing.12Adaptive beamformers,also referred to as adaptive antennas or smart an-tennas,are discussed in the books[34]–[37].In thefirst stage of interference control,the transmitter ex-ploits geographic awareness to focus its radiation pattern alongthe direction of the receiver.Two beneficial effects result frombeamforming in the transmitter.1)At the transmitter,power is preserved by avoiding radia-tion of the transmitted signal in all directions.2)Assuming that every cognitive radio transmitter follows astrategy similar to that summarized under point1),inter-ference at the receiver due to the actions of other trans-mitters is minimized.At the receiver,beamforming is performed for the adaptivecancellation of residual interference from known transmitters,as well as interference produced by other unknown transmit-ters.For this purpose,we may use a robustified version of thegeneralized sidelobe canceller[38],[39],which is designed toprotect the target RF signal and place nulls along the directionsof interferers.IV.I NTERFERENCE-T EMPERATURE E STIMATIONWith cognitive radio being receiver-centric,it is necessarythat the receiver be provided with a reliable spectral estimate ofthe interference temperature.We may satisfy this requirementby doing two things.1)Use the multitaper method to estimate the power spectrumof the interference temperature due to the cumulative dis-tribution of both internal sources of noise and externalsources of RF energy.In light of thefindings reported in[33],this estimate is near-optimal.2)Use a large number of sensors to properly“sniff”the RFenvironment,wherever it is feasible.The large number ofsensors is needed to account for the spatial variation of theRF stimuli from one location to another.The issue of multiple-sensor permissibility is raised underpoint2)because of the diverse ways in which wireless commu-nications could be deployed.For example,in an indoor buildingenvironment and communication between one building andanother,it is feasible to use multiple sensors(i.e.,antennas)placed at strategic locations in order to improve the reliabilityof interference-temperature estimation.On the other hand,inthe case of an ordinary mobile unit with limited real estate,theinterference-temperature estimation may have to be confined toa single sensor.In what follows,we describe the multiple-sensorscenario,recognizing that it includes the single-sensor scenarioas a special case.Let denote the total number of sensors deployed in the RFenvironment.Let denotethe th eigenspectrum com-puted bythe th sensor.We may then constructthe-by-spatio–temporal complex-valuedmatrix......(4)where each column is produced using stimuli sensed at a dif-ferent gridpoint,each row is computed using a different Slepian。

自我效能感的二十四项测量法(SEES)
简介
自我效能感是指一个人对自己能力和能够成功完成任务的信心
程度。

自我效能感的高低会对个人的行为和表现产生重要影响。

为
了测量自我效能感，研究者开发了不同的测量工具。

其中一种常用
的测量工具是自我效能感的二十四项测量法(SEES)。

测量内容
自我效能感的二十四项测量法(SEES)包含24个项目，每个项
目都描述了一个特定的情境或任务。

参与者需要根据自己对每个情
境或任务的信心程度进行评估。

评估可以使用一个五分Likert 量表，其中1代表完全不自信，5代表非常自信。

使用方法
使用自我效能感的二十四项测量法(SEES)进行测量时，参与者
需要仔细阅读每个项目并选择适当的评分。

评分应该基于参与者对
自身能力和信心的判断，而不是外界的评价或期望。

解析结果
通过计算参与者的平均得分，可以得出他们的自我效能感水平。

得分越高，表示参与者对自己的能力和能够成功完成任务的信心越大。

得分越低，则表示参与者的自我效能感水平较低。

应用领域
自我效能感的二十四项测量法(SEES)可以在多个领域中使用，
例如教育、职业发展和健康管理等。

通过了解个体的自我效能感水平，我们可以为他们提供针对性的支持和培训，以提高他们的能力
和信心。

总结
自我效能感的二十四项测量法(SEES)是一种常用的测量工具，
用于评估个体对自身能力和能够成功完成任务的信心程度。

通过使
用该测量工具，我们可以更好地了解个体的自我效能感水平，并为
他们提供相应的帮助和支持。