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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

http://www.csun.edu/~andrzej/COMP529-S05/papers/Mesh%20Networks%20Survey.pdf

Wireless mesh networks: a survey

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicates a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in the proposed method is demonstrated.

/pdf/autoregressive-modeling-and-feature-analysis-of-dna-4pqlcgdqr4.pdf

Autoregressive modeling and feature analysis of DNA sequences

Single-walled carbon nanotubes (SWNTs) have been used extensively for sensor fabrication due to their high surface-to-volume ratio, nanosized structure, and interesting electronic property. Lack of selectivity is a major limitation for SWNT-based sensors. However, surface modification of SWNTs with a suitable molecular recognition system can enhance the sensitivity. On the other hand, porphyrins have been widely investigated as functional materials for chemical sensor fabrication due to their several unique and interesting physicochemical properties. Structural differences between free-base and metal-substituted porphyrins make them suitable for improving the selectivity of sensors. However, their poor conductivity is an impediment in the fabrication of prophyrin-based chemiresistor sensors. The present attempt is to resolve these issues by combining free-base and metallo-porphyrins with SWNTs to fabricate SWNT–porphyrin hybrid chemiresistor sensor arrays for monitoring volatile organic compounds in the a...

Porphyrins-Functionalized Single-Walled Carbon Nanotubes Chemiresistive Sensor Arrays for VOCs.

Preface. 1. Introduction. 2. Signal Processing Essentials. 3. Quantization and Entropy Coding. 4. Linear Prediction in Narrowband and Wideband Coding. 5. Psychoacoustic Principles. 6. Time-Frequency Analysis: Filter Banks and Transforms. 7. Transform Coders. 8. Subband Coders. 9. Sinusoidal Coders. 10. Audio Coding Standards and Algorithms. 11. Lossless Audio Coding and Digital Watermarking. 12. Quality Measures for Perceptual Audio Coding. References. Index.

http://jdsp.engineering.asu.edu/books/cover-page-audio+contents.pdf

Audio Signal Processing and Coding

An interactive Web-based simulation tool called Java-DSP (J-DSP) for use in digital signal processing (DSP)-related electrical engineering courses is described. J-DSP is an object-oriented simulation environment that enables students and distance learners to perform online signal processing simulations, visualize Web-based interactive demos, and perform computer laboratories from remote locations. J-DSP is accompanied by a series of hands-on laboratory exercises that complement classroom and textbook content. The laboratories cover several fundamental concepts, including z transforms, digital filter design, spectral analysis, multirate signal processing, and statistical signal processing. Online assessment instruments for the evaluation of the J-DSP software and the associated laboratory exercises have been developed. Pre/postassessment data have been collected and analyzed for each laboratory in an effort to assess the impact of the tool on student learning.

http://jdsp.engineering.asu.edu/papers/IEEE%20Trans-Nov2005-Atti.pdf

Interactive online undergraduate laboratories using J-DSP

Considerable research has been devoted to the development of algorithms for perceptually transparent coding of high-fidelity (CD-quality) digital audio As a result, many algorithms have been proposed and several have now become international and/or commercial product standards This paper reviews algorithms for perceptually transparent coding of CD-quality digital audio, including both research and standardization activities First, psychoacoustic principles are described with the MPEG psychoacoustic signal analysis model 1 discussed in some detail Then, we review methodologies which achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms which manipulate transform components and subband signal decompositions The discussion concentrates on architectures and applications of those techniques which utilize psychoacoustic models to exploit efficiently masking characteristics of the human receiver Several algorithms which have become international and/or commercial standards are also presented, including the ISO/MPEG family and the Dolby AC-3 algorithms The paper concludes with a brief discussion of future research directions

/pdf/a-review-of-algorithms-for-perceptual-coding-of-digital-2h0gxa0e0h.pdf

Andreas Spanias

Papers

Autoregressive modeling and feature analysis of DNA sequences

Porphyrins-Functionalized Single-Walled Carbon Nanotubes Chemiresistive Sensor Arrays for VOCs.

Audio Signal Processing and Coding

Interactive online undergraduate laboratories using J-DSP

A review of algorithms for perceptual coding of digital audio signals