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

Digital Hardware Evolution.- An Online EHW Pattern Recognition System Applied to Sonar Spectrum Classification.- Design of Electronic Circuits Using a Divide-and-Conquer Approach.- Implementing Multi-VRC Cores to Evolve Combinational Logic Circuits in Parallel.- An Intrinsic Evolvable Hardware Based on Multiplexer Module Array.- Estimating Array Connectivity and Applying Multi-output Node Structure in Evolutionary Design of Digital Circuits.- Research on the Online Evaluation Approach for the Digital Evolvable Hardware.- Research on Multi-objective On-Line Evolution Technology of Digital Circuit Based on FPGA Model.- Evolutionary Design of Generic Combinational Multipliers Using Development.- Analog Hardware Evolution.- Automatic Synthesis of Practical Passive Filters Using Clonal Selection Principle-Based Gene Expression Programming.- Research on Fault-Tolerance of Analog Circuits Based on Evolvable Hardware.- Analog Circuit Evolution Based on FPTA-2.- Bio-inspired Systems.- Knowledge Network Management System with Medicine Self Repairing Strategy.- Design of a Cell in Embryonic Systems with Improved Efficiency and Fault-Tolerance.- Design on Operator-Based Reconfigurable Hardware Architecture and Cell Circuit.- Bio-inspired Systems with Self-developing Mechanisms.- Development of a Tiny Computer-Assisted Wireless EEG Biofeedback System.- Steps Forward to Evolve Bio-inspired Embryonic Cell-Based Electronic Systems.- Evolution of Polymorphic Self-checking Circuits.- Mechanical Hardware Evolution.- Sliding Algorithm for Reconfigurable Arrays of Processors.- System-Level Modeling and Multi-objective Evolutionary Design of Pipelined FFT Processors for Wireless OFDM Receivers.- Reducing the Area on a Chip Using a Bank of Evolved Filters.- Evolutionary Design.- Walsh Function Systems: The Bisectional Evolutional Generation Pattern.- Extrinsic Evolvable Hardware on the RISA Architecture.- Evolving and Analysing "Useful" Redundant Logic.- Adaptive Transmission Technique in Underwater Acoustic Wireless Communication.- Autonomous Robot Path Planning Based on Swarm Intelligence and Stream Functions.- Research on Adaptive System of the BTT-45 Air-to-Air Missile Based on Multilevel Hierarchical Intelligent Controller.- The Design of an Evolvable On-Board Computer.- Evolutionary Algorithms in Hardware Design.- Extending Artificial Development: Exploiting Environmental Information for the Achievement of Phenotypic Plasticity.- UDT-Based Multi-objective Evolutionary Design of Passive Power Filters of a Hybrid Power Filter System.- Designing Electronic Circuits by Means of Gene Expression Programming II.- Designing Polymorphic Circuits with Evolutionary Algorithm Based on Weighted Sum Method.- Robust and Efficient Multi-objective Automatic Adjustment for Optical Axes in Laser Systems Using Stochastic Binary Search Algorithm.- Minimization of the Redundant Sensor Nodes in Dense Wireless Sensor Networks.- Evolving in Extended Hamming Distance Space: Hierarchical Mutation Strategy and Local Learning Principle for EHW.- Hardware Implementation of Evolutionary Algorithms.- Adaptive and Evolvable Analog Electronics for Space Applications.- Improving Flexibility in On-Line Evolvable Systems by Reconfigurable Computing.- Evolutionary Design of Resilient Substitution Boxes: From Coding to Hardware Implementation.- A Sophisticated Architecture for Evolutionary Multiobjective Optimization Utilizing High Performance DSP.- FPGA-Based Genetic Algorithm Kernel Design.- Using Systolic Technique to Accelerate an EHW Engine for Lossless Image Compression.

Evolvable systems : from biology to hardware : 7th International Conference, ICES 2007, Wuhan, China, September 21-23, 2007 : proceedings

This paper aims at examining the trends, challenges and theoretical gaps in the implementation of Healthcare 4.0 (H4.0) based on a scoping review of the literature. For that, we searched journal ar...

Healthcare 4.0: trends, challenges and research directions

Metaheuristic Algorithms for Healthcare

An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

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Cardiac arrhythmias classification method based on MUSIC, morphological descriptors, and neural network

Conventionally, ECG signal is measured on static condition since various types of noise including muscle artifact noise and electrode moving artifact noise are in coupled in dynamic environment. To solve this problem, various noised signals are grouped into six categories by context estimation, and effectively reconfigured noise reduction filter by neural network and genetic algorithm (GA). Neural network based control module effectively select optimal filter block by noise context based clustering at running mode and filtering performance was improved by GA at evolution mode. Experimental results showed that proposed algorithm effectively remove baseline wander noise and muscle noise and feature extraction results showed significant improvement of T duration extraction values.

Adaptable noise reduction of ECG signals for feature extraction

Patient adaptable ECG diagnosis algorithm is required and ECG based user recognition method is essential for patient adaptable smart healthcare system. For this, we developed ECG lead III signal based patient recognition model using artificial neural networks. To extract patient's feature from ECG signal, we used three level noise removing method. After noise cancellation, number of vertices, signal intervals and detailed signal shapes were extracted from ECG signal as patient features. To show the validity of proposed model, we modeled recognition models for seven adults and we tested them under the artificial stress conditions including running, drinking and smoking and proposed model showed 92% of recognition accuracy rate.

ECG based patient recognition model for smart healthcare systems

Indium thin oxide (ITO) films were deposited by a DC-powered plasma sputtering system. The transmittance characteristics of ITO film are investigated and optimized by using a neural network model and by a statistical factor analysis. For systematic modeling, 25-1 with a resolution V design was utilized for 5 process parameters including wafer temperature, DC power, chamber pressure, Cesium (Cs) canister temperature, and Cs carrier flow rate. A generalized regression neural network was used to build a model of transmittance. The statistical parameter analysis revealed a larger main effect of the power and pressure over the others. The prediction performance of genetic algorithm-optimized model is 2.70 in the root mean square error. The impact of the carrier flow rate or the wafer temperature was insensitive to the variation in the power. An increase in the transmittance was noted as either the Cs temperature or the pressure increased in particular at a lower power. The impact of the wafer temperature and carrier flow rate was the opposite of those for the Cs temperature and pressure. A high transmittance at a low surface roughness was noted as a function of the power and the Cs carrier flow rate.

Optimization of transmittance characteristic of indium tin oxide film using neural networks

This paper presents a stand-alone ubiquitous evolvable hardware (u-EHW) system that is effective for automated heart disease diagnosis applications. The proposed u-EHW system consists of a novel reconfigurable evolvable hardware (rEHW) chip, an evolvable embedded processor, and a hand-held terminal. Through adaptable reconfiguration of the filter components, the proposed u-EHW system can effectively remove various types of noise from ECG signals. Filtered signals are sent to a PDA for automated heart disease diagnosis, and diagnosis results with filtered signals are sent to the medical doctor's computer for final decision. The rEHW chip features FIR filter evolution capability, which is realised using a genetic algorithm. A parallel genetic algorithm evolves FIR filters to find the optimal filter combination configuration, associated parameters, and the structure of the feature space adaptively to noisy environments for adaptive signal processing. The embedded processor implements feature extraction and a...

Ubiquitous evolvable hardware system for heart disease diagnosis applications

Conventional physical activity systems with 3-D accelerometer or global positioning system (GPS) can effectively quantize energy expenditure for aerobic exercise. However, they are limited for aerobic exercise monitoring. To overcome this limitation, we developed EMG based portable physical activity monitoring system. Proposed system extract unique features of motion based exercise and muscular motion based exercise from measured bio-signal. Experimental results showed it can clearly classify the aerobic exercise, isotonic resistance exercise, and hybrid types of exercise. Through this system, more complex types of physical activity can be recognized accurately.

Chul Hong Min

Papers

Adaptable noise reduction of ECG signals for feature extraction

ECG based patient recognition model for smart healthcare systems

Optimization of transmittance characteristic of indium tin oxide film using neural networks

Ubiquitous evolvable hardware system for heart disease diagnosis applications

Design of bio-signal based physical activity monitoring system