There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Thank you very much for downloading table of integrals series and products. Maybe you have knowledge that, people have look hundreds times for their chosen books like this table of integrals series and products, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. table of integrals series and products is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the table of integrals series and products is universally compatible with any devices to read.

Table Of Integrals Series And Products

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

The organization of behavior: A neuropsychological theory

This paper presents our work on the reconstruction of the complex permittivity 2-D profile of biological objects simulated as circular phantoms. An iterative reconstruction algorithm called the Levenberg-Marquardt method developed by Franchois and Pichot is tested using synthetic data. Assumed permittivity profiles are generated for a simple circular phantom using the CST microwave studio software. Then, we reconstruct the permittivity profile of the object in MATLAB by using the data from CST microwave studio. The main work in this paper focuses on the realization of the inversion algorithm on three different circular phantoms. Our results show that the permittivity profiles can be very satisfactorily reconstructed, thereby indicating the usefulness of this approach for medical diagnosis.

Microwave Imaging of Circular Phantom Using the Levenberg- Marquardt Method

Wireless capsule endoscopy is an advanced application of using ICT in healthcare and medicine. It is a non-invasive method to examine the gastrointestinal (GI) for early diagnosis and treatment. The capsule is equipped with a camera, light source, processing, control unit, and a transmitter to send the captured pictures or videos wirelessly to an external receiver system. The capsule is used to monitor small bowel diseases, esophageal diseases, and colonic diseases. The current data rate of the capsule endoscopes is limited to 1–2 Mbps with 2–3 frames per second and significant compression rates are applied to provide an operation time at least 8 hours inside the body. The data and frame rates might be sufficient for a capsule without locomotion, in which the capsule travels slowly, and the gastric wall movements mainly manage the speed. However, the next generation of the capsules will need remote locomotion and orientation control for fast and precise diagnosis. This application requests high data rate and low image or video compression rates with high frame rates for real-time navigation and control. Such a communication system becomes power hungry and limits the longevity of the device. Design of an ultra-low power wireless transmission scheme can assist the capsule device to save energy to handle the technology requirements. In this paper, we assess the communication systems of the capsule endoscopes and propose a battery-free high data rate backscatter wireless transmission scheme. The proposed system has been tested inside biological phantom in order to measure the path loss over different capsule depths. The average attenuation in a backscatter link inside biological phantom is 4.1 dB/cm in which is a linear function of capsule depth. According to link budget calculations, by using a 20 dBm transmitter at the outside of the body we can receive 8 Mbps data from the capsule at 10 cm depth. Our proposed system demonstrates superior performance compared to state-of-the-art systems in the literature.

/pdf/battery-free-wireless-communication-for-video-capsule-4in3oucuyh.pdf

Battery-free Wireless Communication for Video Capsule Endoscopy

This paper considers the problem of the centralized spectrum allocation in wireless sensor networks towards the following goals: (i) maximizing fairness, (ii) maximizing spectrum utilization, (iii) reflecting the priority among sensor data, and (iv) avoiding unnecessary spectrum handoff. We cast this problem into a biobjective mixed integer nonconvex nonlinear programming that is absolutely intractable to solve at least globally without any aid of conversion and approximation. We tackle this intractability with convexification, scalarization, and rounding method that yield good approximate integer solutions.

Approximations of multiobjective optimization for dynamic spectrum allocation in wireless sensor networks

In this paper, we use dual-hop relaying to overcome the signal blockage problem that occurs for millimeter waves (mmWaves) due to obstacles located in the propagation environment. Using device-to-device communication, a device in the neighborhood of the transmitter and the receiver can play the role of a relay by amplifying the signal from the source device and forwarding it to the destination device. We consider that both the relay and the destination devices are subject to interference. We study the performance of this mmWave network and derive an exact and asymptotic expressions for the bit error probability (BEP). The exact BEP expression is validated by Monte Carlo simulations. The asymptotic BEP allows determining the diversity order and the coding gain of the communication system. Additionally, we investigate the power allocation optimization subject to a power constraint and derive an analytical expression for the optimal power. Numerical results illustrate the gain achieved in terms of BEP thanks to optimal power allocation.

Performance Analysis and Optimization of Millimeter Wave Networks with Dual-Hop Relaying

This paper studies estimation of the dynamic aorta radius in a realistic geometry where radius variation is used as an indirect measure of central blood pressure. Four different radius estimation approaches were studied where their performance in terms of precision and sensitivity was compared. As a basis for estimation simulations, finite-difference, time-domain electromagnetic simulations of a realistic human model have been performed. Radius estimation should be based on identifying the front and rear reflections from the aorta, however the temporal sensitivity of the front reflection from the aorta is weak and non-linear; therefore robustness of radius estimates is compromised. Nonetheless, this does not preclude using the sensitivity of the rear reflections as a proxy of aorta diameter variation combined with Moens-Korteweg's relationship to perform estimations of mean pressure. Proxies of radial changes are observable and for a precision of around 0.1 mm, the results show that an emitted energy to receiver noise spectral density ratio between 110 dB and 130 dB should be sufficient, depending on the estimator.

Ilangko Balasingham

Papers

Microwave Imaging of Circular Phantom Using the Levenberg- Marquardt Method

Battery-free Wireless Communication for Video Capsule Endoscopy

Approximations of multiobjective optimization for dynamic spectrum allocation in wireless sensor networks

Performance Analysis and Optimization of Millimeter Wave Networks with Dual-Hop Relaying

Realistic simulations of aorta radius estimation