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

Distributed denial-of-service (DDoS) attacks are one of the most serious threats to today’s high-speed networks. These attacks can quickly incapacitate a targeted business, costing victims millions of dollars in lost revenue and productivity. In this paper, we present a novel method which allows us to timely detect application-layer DDoS attacks that utilize encrypted protocols by applying an anomaly-based approach to statistics extracted from network packets. The method involves construction of a model of normal user behavior with the help of weighted fuzzy clustering. The construction algorithm is self-adaptive and allows one to update the model every time when a new portion of network traffic data is available for the analysis. The proposed technique is tested with realistic end user network traffic generated in the RGCE Cyber Range.

Weighted Fuzzy Clustering for Online Detection of Application DDoS Attacks in Encrypted Network Traffic

High Efficiency Video Coding (HEVC) is the key enabling technology for numerous modern media applications. Overcoming its computational complexity and customizing its rich features for real-time HEVC encoder implementations, calls for automated design methodologies. This article introduces the first complete High-Level Synthesis (HLS) implementation for HEVC intra encoder on FPGA. The C source code of our open-source Kvazaar HEVC encoder is used as a design entry point for HLS that is applied throughout the whole encoder design process, from data-intensive coding tools like intra prediction and discrete transforms to more control-oriented tools such as context-adaptive binary arithmetic coding (CABAC). Our prototype is run on Nokia AirFrame Cloud Server equipped with 2.4 GHz dual 14-core Intel Xeon processors and two Intel Arria 10 PCIe FPGA accelerator cards with 40 Gigabit Ethernet. This proof-of-concept system is designed for hardware-accelerated HEVC encoding and it achieves real-time 4K coding speed up to 120 fps. The coding performance can be easily scaled up by adding practically any number of network-connected FPGA cards to the system. These results indicate that our HLS proposal not only boosts development time, but also provides previously unseen design scalability with competitive performance over the existing FPGA and ASIC encoder implementations.

High-Level Synthesis Implementation of an Embedded Real-Time HEVC Intra Encoder on FPGA for Media Applications

The quality of future network services can be guaranteed with appropriate pricing and packet scheduling schemes. In this paper a closed form formula for updating adaptive weights of a packet scheduler is derived from a revenue-based optimization problem. The algorithm ensures bandwidth as a quality of service (QoS) requirement and optimizes revenue of the network service provider. We derive and prove the guaranteed bandwith and revenue optimization algorithm which has been tested with NS-2 simulations. The weight updating algorithm is implemented with a network simulator to compute the weights of a WFQ scheduler and compared to constant weights.

Optimized Pricing and Closed Form Algorithm for WFQ Scheduling

Contemporary multimedia processors and applications are increasingly limited by their data accessing capabilities. However, the designed Configurable Parallel Memory Architecture (CPMA) alleviates these multimedia data accessing requirements; achieving significant performance improvements over traditional memory architectures. CPMA decreases considerably the processor-memory bottleneck by widening the memory bandwidth, decreasing the number of memory accesses, and diminishing the significance of memory latency. To further enhance the performance of CPMA, this paper introduces a novel architectural extension called CPMA access instruction correlation recognition. The presented method is intended for accelerating the execution rate of consecutive, temporally conflict-free, CPMA memory accesses. As demonstrated in this paper, the superior CPMA performance can also be maintained in the case of limited access widths. In addition, the presented results confirm that CPMA can have an acceptable silicon area.

Enhanced configurable parallel memory architecture

In this paper simple temporal compression algorithms' efficiency to reduce LoRa-based sensor node energy consumption has been evaluated and measured. It is known that radio transmission is the most energy consuming operation in a wireless sensor node. In this paper three lightweight compression algorithms are implemented in an embedded LoRa platform to compress sensor data in on-line mode and the overall energy consumption is measured. Energy consumption is compared to the situation without implementing any compression algorithm. The results show that a simple compression algorithm is an effective method to improve the battery powered sensor node lifetime. Despite the radio transmission's high energy consumption, the sleep current consumption is a significant factor for the device overall lifetime if the measurement interval is long.

Timo Hämäläinen

Papers

Weighted Fuzzy Clustering for Online Detection of Application DDoS Attacks in Encrypted Network Traffic

High-Level Synthesis Implementation of an Embedded Real-Time HEVC Intra Encoder on FPGA for Media Applications

Optimized Pricing and Closed Form Algorithm for WFQ Scheduling

Enhanced configurable parallel memory architecture

LoRa-Based Sensor Node Energy Consumption with Data Compression