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

Sparse representations using predefined and learned dictionaries have widespread applications in signal and image processing. Sparse approximation techniques can be used to recover data from its low dimensional corrupted observations, based on the knowledge that the data is sparsely representable using a known dictionary. In this paper, we propose a method to improve data recovery by ensuring that the data recovered using sparse approximation is close its manifold. This is achieved by performing regularization using examples from the data manifold. This technique is particularly useful when the observations are highly reduced in dimensions when compared to the data and corrupted with high noise. Using an example application of image inpainting, we demonstrate that the proposed algorithm achieves a reduction in reconstruction error in comparison to using only sparse coding with predefined and learned dictionaries, when the percentage of missing pixels is high.

Improved sparse coding using manifold projections

This paper describes the design and implementation of a mobile application (app) for acoustic ranging developed for the Android platform. The ranging strategy used is an extension of the Microsoft Windows based BeepBeep method. Using only the speaker and microphone hardware supplied with commercial Android devices, indoor ranging precision under a 0.1 centimeter variance has been achieved at distances up to 4.5 meters. In addition, preliminary outdoor tests show accuracy at distances as large as 50 meters when there is a strong line of sight component. The app has been designed to be an accessible and customizable tool for educational and research applications, and features options for optimizing its accuracy in various environments.

Android acoustic ranging

Global Positioning System (GPS) receivers for military applications are highly sensitive to hostile jamming. This paper describes a partial band frequency-domain interference excision technique for GPS receivers. This has been demonstrated to be quite effective in field tests. Although the results of the field tests are classified [6] and not presented here, the algorithm is described in the paper along with a series of simulation results that demonstrate the effectiveness of this technique.

Partial band interference excision for GPS using frequency-domain exponents

Several web-based signal processing simulation packages for education have been developed in a Java environment. Although this environment has provided convenience and accessibility using standard browser technology, it has recently become vulnerable to cyber-attacks and is no longer compatible with secure browsers. In this paper, we describe our efforts to transform our award-winning J-DSP online laboratory by rebuilding it on an HTML5 framework. Along with a new simulation environment, we have redesigned the interface to enable several new functionalities and an entirely new educational experience. These new features include functions that enable real-time interfaces with sensor boards and mobile phones. The Web 4.0 HTML5 technology departs from older Java interfaces and provides an interactive graphical user interface (GUI) enabling seamless connectivity and both software and hardware experiences for students in DSP classes.

Development of signal processing online labs using HTML5 and mobile platforms

Absfract - This paper presents assessment results oJ the Java-DSP (J-DSP) on-line laboratory. J-DSP sofrware has been developed Jrom fhe ground up at Arizona State University (ASU) to support the computer lab portion of the senior-level DSP course EEE407. The sojlwore enables online interactive DSP laborafories. Along wifh the sofrware, we have developed several JDSP laboratory exercises that have been posted on the internet. Assessmenf ofthe EEE 407 labs was carried both on the web and as part of fhe instructor and class evoluafiotr. The web-based assessments have been organized into: general sofrware assessments, general laboratory assessments, concept-specific lab-by-lab assessments, and differential prdpost assessment for each lab. Storistical and qualitative evaluations hove been compiled for all the J-DSP laboratories and are described in therest ofthepaper.

Andreas Spanias

Papers

Improved sparse coding using manifold projections

Android acoustic ranging

Partial band interference excision for GPS using frequency-domain exponents

Development of signal processing online labs using HTML5 and mobile platforms

Session T2E ASSESSMENT OF THE JAVA-DSP (J-DSP) ON-LINE LABORATORY SOFTWARE