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

The Advanced Encryption Standard (AES) algorithm has become the default choice for various security services in numerous applications. In this paper we present an AES encryption hardware core suited for devices in which low cost and low power consumption are desired. The core constitutes of a novel 8-bit architecture and supports encryption with 128-bit keys. In a 0.13 im CMOS technology our area optimized implementation consumes 3.1 kgates. The throughput at the maximum clock frequency of 153 MHz is 121 Mbps, also in feedback encryption modes. Compared to previous 8-bit implementations, we achieve significantly higher throughput with corresponding area. The energy consumption per processed block is also lower.

Design and Implementation of Low-Area and Low-Power AES Encryption Hardware Core

This paper analyzes the rate-distortion-complexity of High Efficiency Video Coding (HEVC) reference video codec (HM) and compares the results with AVC reference codec (JM). The examined software codecs are HM 6.0 using Main Profile (MP) and JM 18.0 using High Profile (HiP). These codes are benchmarked under the all-intra (AI), random access (RA), low-delay B (LB), and low-delay P (LP) coding configurations. In order to obtain a fair comparison, JM HiP anchor codec has been configured to conform to HM MP settings and coding configurations. The rate-distortion comparisons rely on objective quality assessments, i.e., bit rate differences for equal PSNR. The complexities of HM and JM have been profiled at the cycle level with Intel VTune on Intel Core 2 Duo processor. The coding efficiency of HEVC is drastically better than that of AVC. According to our experiments, the average bit rate decrements of HM MP over JM HiP are 23%, 35%, 40%, and 35% under the AI, RA, LB, and LP configurations, respectively. However, HM achieves its coding gain with a realistic overhead in complexity. Our profiling results show that the average software complexity ratios of HM MP and JM HiP encoders are 3.2× in the AI case, 1.2× in the RA case, 1.5× in the LB case, and 1.3× in the LP case. The respective ratios with HM MP and JM HiP decoders are 2.0×, 1.6×, 1.5×, and 1.4×. This paper also reveals the bottlenecks of HM codec and provides implementation guidelines for future real-time HEVC codecs.

Comparative Rate-Distortion-Complexity Analysis of HEVC and AVC Video Codecs

Wireless sensor networks (WSNs) are deployed to an area of interest to sense phenomena, process sensed data, and take actions accordingly. Due to the limited WSN node resources, distributed processing is required for completing application tasks. Proposals implementing distribution services for WSNs are evolving on different levels of generality. In this paper, these solutions are reviewed in order to determine the current status. According to the review, existing distribution technologies for computer networks are not applicable for WSNs. Operating systems (OSs) and middleware architectures for WSNs implement separate services for distribution within the existing constraints but an approach providing a complete distributed environment for applications is absent. In order to implement an efficient and adaptive environment, a middleware should be tightly integrated in the underlying OS. We recommend a framework in which a middleware distributes the application processing to a WSN so that the application lifetime is maximized. OS implements services for application tasks and information gathering as well as control interfaces for the middleware.

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A survey of application distribution in wireless sensor networks

This paper presents a novel algorithm to rapidly create a high quality network plan for IEEE 802.11 based WLAN according to assigned design requirements. The algorithm uses a Genetic Algorithm (GA) to explore the design space, and a IEEE 802.11 rate adaptation aware QoS estimation functionality to provide feedback for the algorithm and for a network designer. The algorithm selects AP devices, locations, antennas, as well as AP configuration including transmission power and frequency channel. The algorithm was used in WLAN planning for a suburb, which is under development in Tampere-Lempaala area in Finland. Compared to manual network planning, the developed algorithm was able to create a network plan with 133 % capacity, 98 % coverage, and 93 % cost. Manually the corresponding network planning took hours, whereas the algorithm computation time was 15 minutes.

Genetic Algorithm to Optimize Node Placement and Configuration for WLAN Planning

IEEE 802.16 standard defines the wireless broadband access network technology called WiMAX. WiMAX introduces several interesting advantages, and one of them is the support for QoS at the MAC level. For these purposes, the base station must allocate slots based on some algorithm. We propose a simple, yet efficient, solution for the WiMAX base station that is capable of allocating slots based on the QoS requirements, bandwidth request sizes, and the WiMAX network parameters. To test the proposed solution, we have implemented the WiMAX MAC layer in the NS-2 simulator. Several simulation scenarios are presented that demonstrate how the scheduling solution allocates resources in various cases. Simulation results reveal the proposed scheduling solution is ensures the QoS requirements of all the WiMAX service classes and shares fairly free resources achieving the work-conserving behaviour.

Timo Hämäläinen

Papers

Design and Implementation of Low-Area and Low-Power AES Encryption Hardware Core

Comparative Rate-Distortion-Complexity Analysis of HEVC and AVC Video Codecs

A survey of application distribution in wireless sensor networks

Genetic Algorithm to Optimize Node Placement and Configuration for WLAN Planning

Ensuring the QoS requirements in 802.16 scheduling