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

Clustering of video sequences is essential in order to perform video summarization Because of the high spatial and temporal dimensions of the video data, dimensionality reduction becomes imperative before performing Euclidean distance based clustering In this paper, we present non-adaptive dimensionality reduction approaches using random projections on the video data Assuming the data to be a realization from a mixture of Gaussian distributions allows for further reduction in dimensionality using random projections The performance and computational complexity of the K-means and the K-hyperline clustering algorithms are evaluated with the reduced dimensional data Results show that random projections with an assumption of Gaussian mixtures provides the smallest number of dimensions, which leads to very low computational complexity in clustering

/pdf/dimensionality-reduction-for-distance-based-video-clustering-3pjtize3bj.pdf

Dimensionality Reduction for Distance Based Video Clustering

Signal processing algorithms, software, and hardware are being used in several fields including non-engineering areas such as arts and media. Students in these fields and particularly in the new Digital Culture major at Arizona State University (ASU) use signal processing tools in several of their projects and artistic endeavors. Yet the blind use of these DSP tools in other disciplines, without understanding their properties has been a long-standing problem. In fact, the broader issue is the disconnect between engineers that develop tools and artists that use them to design the next generation digital art applications. In that context, ASU has formed the Arts Media and Engineering (AME) School and more recently, the multidisciplinary undergraduate Digital Culture degree granting program. In order to provide formal training in signal processing to students that are non-Electrical Engineering majors, we piloted a new course titled Signal Processing for Digital Culture. This course, which is being offered online, teaches non-majors some of the basics of signal processing and covers several applications. The only prerequisite to the course is general sophomore calculus. This new online course contains several topics and is focused on an approach that teaches concepts by connecting theory to compelling applications. Future plans include introducing this course at Clarkson University as a Knowledge Area course open to students from all majors.

A new signal processing course for digital culture

This paper provides an analysis of current advancements made in acoustic noise and echo cancellation (ANEC) and describes a real-time acoustic noise and feedback cancellation implementation that is low-cost and effective. Challenges in developing this compact mobile ANEC system included integrating a high order normalized least mean square (NLMS) adaptive algorithm with an analog synchronized twochannel interface. The paper also presents electrical and mechanical details of this small footprint ANEC prototype along with test results. The system was evaluated in terms of performance and computational complexity using objective and subjective evaluations. Metrics used to assess real-time performance included the Echo Return Loss Enhancement (ERLE), the Added Stable Gain (ASG) as well as algorithm convergence curves.

Real-Time Adaptive Noise and Echo Cancellation on a Compact Microcontroller

With increased adoption of renewable energy, more effective array management strategies are needed. Many arrays are located in remote locations, where faults within the array often go unnoticed and unattended for large periods of time. Technicians sent to rectify the faults must spend a large amount of time determining the location of the fault manually. Automated monitoring systems are needed to obtain the information about the performance of the array and detect faults. Such systems must monitor the DC side of the array in addition to the AC side to identify non catastrophic faults. In this paper, a prototype comprehensive monitoring system is presented for management of utilityscale PV installations. The system continuously monitors and logs the performance and condition of the array and provides a variety of data visualization and reporting functionality to the facility operator.

https://www.actapress.com/PaperInfo.aspx?paperId=455055

A comprehensive monitoring system for photovoltaic arrays

Inverting an image onto the latent space of pre-trained generators, e.g., StyleGAN-v2, has emerged as a popular strategy to leverage strong image priors for ill-posed restoration. Several studies have showed that this approach is effective at inverting images similar to the data used for training. However, with out-of-distribution (OOD) data that the generator has not been ex-posed to, existing inversion techniques produce sub-optimal results. In this paper, we propose SPHInX (StyleGAN with Projection Heads for Inverting X), an approach for accurately embedding OOD images onto the StyleGAN latent space. SPHInX optimizes a style projection head using a novel training strategy that imposes a vicinal regularization in the StyleGAN latent space. To further enhance OOD inversion, SPHInX can ad-ditionally optimize a content projection head and noise variables in every layer. Our empirical studies on a suite of OOD data show that, in addition to producing higher quality reconstructions over the state-of-the-art inversion techniques, SPHInX is effective for ill-posed restoration tasks while offering semantic editing capabilities.

Andreas Spanias

Papers

Dimensionality Reduction for Distance Based Video Clustering

A new signal processing course for digital culture

Real-Time Adaptive Noise and Echo Cancellation on a Compact Microcontroller

A comprehensive monitoring system for photovoltaic arrays

Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images