dix A. Even if you don’t choose to memorize them this system aids in reference and retreival of important formulas. The book was compiled from notes developed during eight years of teaching a graduate course on the subject and was used as a text. Thus it has been student tested. Appendix F contains a number of problems, grouped to be used on a chapter by chapter basis The problems are designed to illustrate practical applications of the text material. The parts of this book of most interest and value to the EMC engineer will be the chapters on Thermal Noise, Antennas, Propagation and Transmission Lines, and  Reflection and Refraction. This is not  to downpade the chapters on Statistics and Its Applications, Signal Processing and Detection, and Some System Characteristics which also contain much potentially useful materials. Additional plus values for the book include a list of 40 references, a table of symbols used throughout the book, and a subject index. Some readers may find the condensed type and close line spacing hard to read. It was apparently set up by typewriter using an elite type face with single line spacing. When reduced down to a 6 by 9 5 inch size page it is too crowded for easy reading. In spite of this shortcoming your reviewer recommends this book as a worthwhile reference in this field of interest.

http://ieeexplore.ieee.org/iel5/5/31269/01455368.pdf

Signal analysis

Principal characteristics of speech speech production models speech analysis and analysis-synthesis systems linear predictive coding (LPC) analysis speech coding speech synthesis speech recognition future directions of speech processing. Appendices: convolution and z-transform vector quantization algorithm neural nests.

Digital speech processing, synthesis, and recognition

Review of medical image quality assessment

The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

On discrete cosine transform

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

Background: Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues. Aims: To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording. Settings and Design: Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute. Materials and Methods: Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification. Statistical Analysis: Mann–Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications. Results: Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances. Conclusions: SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the “validity” and reducing the “heterogeneity” in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.

Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study

Various Edge detection algorithm has been proposed in the literature for extracting the edges from the image. After emerging the fuzzy logic concept, a lot of Researcher of image processing shifted their attention towards the fuzzy logic concept and its applicability in the field of image processing. This paper presents a fuzzy rule base algorithm which is capable of detecting edges efficiently from the gray scale images. The performance of the proposed method is demonstrated through computer simulation in comparison with the existing sobel and prewitt edge detector.

An Efficient Fuzzy Logic Based Edge Detection Algorithm for Gray Scale Image

In the last few years, rapid growth in the technological development has been reported. This rapid growth in technology demands fast and efficient processing, transmission and storage of data. Although lots of work have been reported in the literature related to efficient processing and transmission of data, but this would not be achieved without assuring reduction in data storage, as during processing and transmission most of the efforts and time is required for either accessing the data or storing the data. Therefore to cope up with the current technological demands, the data should be in highly compressed form. One of the most important form of data is digital image which is nothing but a two dimensional signal. Digital image in their raw form require a huge amount of storage capacity so that a scheme that produces high degree of compression is required which should preserve the critical image information. However JPEG standards are already available for gray image compression, but this area is still open for algorithms, which can provide better compression ratio while keeping mean square error low. Zadah in his paper proved that, imprecise situations can be properly handled using fuzzy logic. This feature of fuzzy logic has been incorporated by introducing a novel data compression technique for gray images using fuzzy logic based fusion of available JPEG and JPEG2K Standards (FSHJPEG) to achieve higher compression ratio as compared to stand alone JPEG and JPEG2K standards. The fuzzy based soft hybrid JPEG technique (FSHJPEG) gives high compression ratio, preserving most of the image information and the image is reproduced with good quality. This new technique not only gives high compression ratio, but also reduces blocking artifacts, ringing effects and false contouring appreciably. The compression ratio obtained using FSHJPEG is more as compared to currently used standards of Image compression, preserving most of the image information.

Design and Implementation of a Highly Efficient Gray Image Compression Codec Using Fuzzy Based Soft Hybrid JPEG Standard

This paper describes a complete system for the reco gnition of isolated handwritten Devnagri character using Fourier Descri ptor and Hidden-Markov model (HMM). The HMM has the property that its stat es are not defined as a priory information, but are determined automaticall y based on a database of handwritten numerals images. In this work the image database consist of 500 images of handwritten Devnagri characters from 50 dwriters. Before extracting the features, the images are normalized using image isometrics such as translation, rotation and scaling. After normalizat ion the Fourier features are extracted using Fourier Descriptor. An automatic sy stem trained 400 images of image database and character model form with multiv ariate Gaussian state conditional distribution. A separate set of 100 cha racters was used to test the system. The recognition accuracy for individual cha racter varies from 90% to 100% for number of states per model N=3 and 100% fo r N=5

Isolated Handwritten Devnagri Character Recognition using Fourier Descriptor and HMM

Medical imaging plays a major role in contemporary health care, both as a tool in primary diagnosis and as a guide for surg ical and therapeutic procedures. Compression of radiological images is an effective mechanism for storage and transmission. The use of such images for teleradiol ogy is of increasing importance, with one of the main reasons being the ability to c all upon remotely located diagnostic experts. Whilst many researchers have ad dressed the problem of how the degradation of image quality with compression ratio affects observer-based diagnostic accuracy. In this work, we propose a qua ntitative analysis of quality for lossy compressed magnetic resonance (MR) images, an d their influence in automatic tissue classification. Peak Signal to Noise Ratio ( PSNR), as quality measurement, is not enough if we have medical images. So we need to find out new quality measurements not based in perception to make a quan titative analysis for medical image compression. This paper proposed a scheme whi ch combines the Artificial Neural Network and technique of Structure Similarit y Index Measurement (SSIM) to improve the issues. We feel that the proposed frame work is in the right direction towards the achievement of this goal.

Kavita Thakur

Papers

Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study

An Efficient Fuzzy Logic Based Edge Detection Algorithm for Gray Scale Image

Design and Implementation of a Highly Efficient Gray Image Compression Codec Using Fuzzy Based Soft Hybrid JPEG Standard

Isolated Handwritten Devnagri Character Recognition using Fourier Descriptor and HMM

Quality Assessment of Compressed MR Medical Images using General Regression Neural Network