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

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

The PASCAL Visual Object Classes Challenge

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Abstract: In this paper we present a scheduling algorithm for traffic allocation. In our model, we use a flat pricing scenario in which the weights of the queues are updated using revenue as a target function. Due to the algorithm’s closed form nature, it is capable of operating in non-stationary environments. In addition, the algorithm is nonparametric and deterministic in the sense that any assumptions about the call density functions or duration distributions are not made.

Packet scheduling algorithm with weight optimization

The dynamic and resource constrained nature of Wireless Sensor Networks (WSNs) require that the time synchronization protocol is highly fault tolerant whilst minimizing communication overhead for energy-efficiency In current tree-based WSN synchronization protocols, the tree fault tolerance is not considered at all or incurs a significant messaging overhead In this paper, we present a robust Synchronization TREE construction and maintenance protocol (S-TREE) It can handle failed nodes and communication links, and intermittent connectivity It is integrated to the inherent neighbor discovery to minimize communication overhead The S-TREE protocol minimizes the accumulated synchronization error of any given neighbor clock estimation technique by using minimum hop paths to the reference node The protocol performance is analyzed mathematically For an example scenario of 1000 nodes S-TREE reduces communication overhead to 6% in tree construction and to 10% in remote clock estimation compared to flooding which is commonly used in related protocols

Robust tree construction and maintenance for global time synchronization protocols in Wireless Sensor Networks

Modern VLSI and FPGA chip designs utilize automated generation of the structure and component configuration for different product variations. This is based on re-usable, parametrized library components, and tools for definition, assembly, configuration and generation of final HW and SW code. A product version includes several structural hierarchies, in which each component is independently reusable and must be configured for the specific product context. IEEE 1685 "IP-XACT" standardizes the component and design descriptions and the overall process. Still the challenges are very large parameter space, name-based referencing and propagation of parameter values. Practical user problems are careless parameter renaming, duplicate names, and removing a parameter definition without first removing all references to it. In this paper, we present solutions to these problems implemented in Kactus2 v2.7 that is an open-source IP-XACT tool. Our basis is automatic identifier generation and referencing. This required major changes for Kactus2 import wizards, generators as well as expression editors and evaluators. The implementation was carried out in C++/Qt5 and we modified and added 5k LOC compared to Kactus2 v2.6. According to several use cases analysis the new solution practically eliminates the user errors in the parameter referencing, which significantly improves productivity.

Resolving parameter reference management in IP-XACT using Kactus2

Multiresolution video streaming and medical image processing require image scaling. The paper shows how image scaling can be accelerated with a new coarse-grained parallel processing method. The method is based on evenly divisible image sizes, which is, in practice, the case in most video and image standards. The image is divided into slices and all the slices are scaled in parallel. The complexity of the method is evaluated with two parallel architectures while considering memory consumption and data throughput. Several scaling functions can be handled with these generic architectures, including bilinear, bicubic, Lagrange, Gaussian and sinc interpolations. Parallelism can be adjusted independently of the complexity of the computational units.

Block-level parallel processing for scaling evenly divisible frames

Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

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Timo Hämäläinen

Papers

Packet scheduling algorithm with weight optimization

Robust tree construction and maintenance for global time synchronization protocols in Wireless Sensor Networks

Resolving parameter reference management in IP-XACT using Kactus2

Block-level parallel processing for scaling evenly divisible frames

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition