THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

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 Communications

Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].

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Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

An Integrated Agent Architecture for Software Defined Radio. Rapid-prototype cognitive radio, CR1, was developed to apply these.The modern software defined radio has been called the heart of a cognitive radio. Cognitive radio: an integrated agent architecture for software defined radio. Http:bwrc.eecs.berkeley.eduResearchMCMACR White paper final1.pdf. The cognitive radio, built on a software-defined radio, assumes. Radio: An Integrated Agent Architecture for Software Defined Radio, Ph.D. The need for software-defined radios is underlined and the most important notions used for such. Mitola III, Cognitive radio: an integrated agent architecture for software defined radio, Ph.D. This results in the set-theoretic ontology of radio knowledge defined in the. Cognitive Radio An Integrated Agent Architecture for Software.This article first briefly reviews the basic concepts about cognitive radio CR. Cognitive Radio-An Integrated Agent Architecture for Software Defined Radio. Cognitive Radio RHMZ 2007. Software-defined radio SDR idea 1. Cognitive radio: An integrated agent architecture for software.Cognitive Radio SOFTWARE DEFINED RADIO, AND ADAPTIVE WIRELESS SYSTEMS2 Cognitive Networks. 3 Joseph Mitola III, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio Stockholm.

/pdf/cognitive-radio-an-integrated-agent-architecture-for-1v855bviue.pdf

Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

Indoor positioning systems are essential in the industrial domain for optimized production and safe operation of mobile elements, such as mobile robots, especially in the presence of static machinery and human operators. In this paper, we assess the performance of a commercial UWB radio-based positioning system deployed in a realistic industrial scenario, considering both static and mobile use cases. Our goal is to characterize the accuracy of this system in the context of industrial use cases and applications. For the static case, an extensive analysis was presented based on measurements performed at 72 measurement positions at 3 different heights (above, at similar a level to, and below the average clutter level) in different industrial clutter conditions (open and cluttered spaces). The extensive analysis in the mobile case considered several runs of a route covered by an autonomous mobile robot equipped with multiple tags in different positions. The results indicate that a similar degree of accuracy with a median 2D positioning error smaller than 20 cm is possible in both static and mobile conditions with an optimized anchor deployment. The paper provides a complete statistical characterization of the system’s accuracy and addresses the multiple deployment trade-offs and system dynamics observed for the different configurations.

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Performance Evaluation of a UWB Positioning System Applied to Static and Mobile Use Cases in Industrial Scenarios

A New Switching Diversity Scheme for DECT Driven by a Mixed RSSI and Error Criterion

This paper is a study on the impact of the Cyclic Prefix (CP) length on the downlink Capacity in a base-band synchronized SISO-OFDM context. To measure this impact, the capacity, measured in bits per second per hertz, is chosen as quality parameter. The study shows how the lengthening of the CP affects Spectral Efficiency Loss (SEL) and Signal to Interference and Noise Ratio (SINR). SINR can be mapped into Capacity using Shannons formula. The optimum CP length is defined as the one that maximises the Capacity for a given set of system parameters. The parameters studied in this paper are: 1) the useful OFDM symbol duration, 2) the Signal to Noise Ratio (SNR) at the receiver and 3) the channel Power Delay Profile (PDP). Depending on the values of these parameters different optimum CP lengths are obtained. For a system using only one value of CP length we suggest an optimum value to be 4�Ps for an OFDM symbol length of 40�Ps and 6�Ps for an OFDM symbol length of 80�Ps.

/pdf/impact-of-cyclic-prefix-length-on-ofdm-system-capacity-4jumgk9e5f.pdf

Impact of Cyclic Prefix length on OFDM system Capacity

Indoor deployments for providing coverage and capacity inside buildings is an attractive option for cellular network operators as most of the traffic in today's cellular networks is generated indoors. This paper studies the benefit of indoor traffic offloading on the overall network capacity using a new method that allows assessing the network performance gains considering different levels of traffic offloading without the need to decide beforehand which buildings to deploy the indoor solutions in or the exact type of indoor solution used in each building. Using this method, we evaluate the impact of different amounts of indoor traffic offload and the performance of three different indoor deployment strategies on a study case network selected as a typical real world ultra dense urban area. The results show that choosing the most cost-effective buildings (lowest cost per traffic offload) first in the indoor deployment plan leads to highest network capacity gains for the same level of investment.

Preben Mogensen

Papers

Performance Evaluation of a UWB Positioning System Applied to Static and Mobile Use Cases in Industrial Scenarios

A New Switching Diversity Scheme for DECT Driven by a Mixed RSSI and Error Criterion

Impact of Cyclic Prefix length on OFDM system Capacity

Indoor Deployment Strategies for Ultra Dense Urban Areas

Testbed for Evaluation of Adaptive Transmit and Receive Antennas