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

This paper presents a survey of functional programming languages in System-on-a-Chip (SoC) design. The motivation is improving the design productivity by better source code expressiveness, increased abstraction level in design entry, or improved automation. The survey focuses on Chisel that is one of the most potential High Level Language (HLL) based design frameworks. We include 26 papers that report implementations ranging from IP blocks to complete chips. The result is that functional programming languages are viable for SoC design and can also be deployed in production use. However, Chisel does not increase the abstraction level in a similar way as High Level Synthesis (HLS), since it is used to create circuit generators instead of direct descriptions. Additional benefit is that Chisel offloads user effort from control and connectivity structures, and makes reusability and configurability improved over traditional Hardware Description Language (HDL) designs.

A Survey on System-on-a-Chip Design Using Chisel HW Construction Language

This paper demonstrates the usage of Kvazaar open-source HEVC intra encoder in 4K real-time video encoding. In this setup, a raw 4K video is shot by an action camera, captured by an HDMI capture card, encoded in real-time by Kvazaar ultrafast preset on a 22-core Intel Xeon processor, sent to a laptop, and decoded by OpenHEVC decoder for playback. The encoding process is visualized on the fly by Kvazaar run-time visualizer.

Kvazaar: HEVC/H.265 4K30p Intra Encoder

Low energy consumption and load balancing are required for enhancing lifetime at wireless sensor networks (WSN). In addition, network dynamics and different delay, throughput, and reliability requirements demand cost-aware traffic adaptation. This paper presents a novel capacity optimization algorithm targeted at locally synchronized, low-duty cycle WSN MACs. The algorithm balances the traffic load between contention and contention free channel access. The energy-inefficient contention access is avoided, whereas the more reliable contention free access is preferred. The algorithm allows making cost-aware trade-off between delay, energy-efficiency, and throughput guided by routing layer. Analysis results show that the algorithm has 10% to 100% better energy-efficiency than IEEE 802.15.4 LR-WPAN in a typical sensing application, while providing comparable good put and delay

/pdf/cost-aware-capacity-optimization-in-dynamic-multi-hop-wsns-nclqs16kxs.pdf

Cost-aware capacity optimization in dynamic multi-hop WSNs

This demonstrator presents a run-time visualization tool for Kvazaar HEVC intra encoder. The implemented open-source tool is seamlessly integrated into Kvazaar to provide instant visual feedback of the encoding process. The visualization overlays the reconstructed HEVC video with the boundaries of the used block partitioning structure and associated intra prediction modes. The tool is also able to illustrate all Kvazaar parallelization schemes at run time: Wavefront Parallel Processing, tiles, and picture-level parallel processing. The displayed visualization information can be gradually adjusted to user needs. The tool is primarily designed for Kvazaar debugging but it also suits educational purposes.

Live demonstration: Run-time visualization of Kvazaar HEVC intra encoder

Novel metaphors in literary texts (hereinafter referred to as literary metaphors) seem to be more creative and open-ended in meaning than metaphors in non-literary texts (non-literary metaphors). However, some disagreement still exists on how literary metaphors differ from non-literary metaphors. Therefore, this study explored the neural mechanisms of literary metaphors extracted from modern Chinese poetry by using the methods of Event-Related Potentials (ERPs) and Event-Related Spectral Perturbations (ERSPs), as compared with non-literary conventional metaphors and literal expressions outside literary texts. Forty-eight subjects were recruited to make the semantic relatedness judgment after reading the prime-target pairs in three linguistic conditions. According to the ERPs results, the earliest differences were presented during the time window of P200 component (170–260 ms) in the frontal and central areas, with the amplitude of P200 for literary metaphors more positive than the other two conditions, reflecting the early allocation of attention and the early conscious experience of the experimental stimuli. Meanwhile, significant differences were presented during the time window of N400 effect (430–530 ms), with the waveform of literary metaphors more negative than others in the frontal and central topography of scalp distributions, suggesting more efforts in retrieving conceptual knowledge for literary metaphors. The ERSPs analysis revealed that the frequency bands of delta and theta were both involved in the cognitive process of literary metaphor comprehension, with delta band distributed in the frontal and central scalp and theta band in parietal and occipital electrodes. Increases in the two power bands during different time windows provided extra evidences that the processing of literary metaphors required more attention and effort than non-literary metaphors and literal expressions in the semantic related tasks, suggesting that the cognitive process of literary metaphors was distinguished by different EEG spectral patterns.

/pdf/decoding-brain-activities-of-literary-metaphor-comprehension-10rprt06.pdf

Timo Hämäläinen

Papers

A Survey on System-on-a-Chip Design Using Chisel HW Construction Language

Kvazaar: HEVC/H.265 4K30p Intra Encoder

Cost-aware capacity optimization in dynamic multi-hop WSNs

Live demonstration: Run-time visualization of Kvazaar HEVC intra encoder

Decoding brain activities of literary metaphor comprehension: An event-related potential and EEG spectral analysis