Mathematical modeling of ultra wideband in vivo radio channel

This paper proposes a novel mathematical model for an in vivo radio channel at ultra-wideband frequencies (3.1–10.6 GHz), which can be used as a reference model for in vivo channel response without performing intensive experiments or simulations. The statistics of error prediction between experimental and proposed model is RMSE = 5.29, which show the high accuracy of the proposed model. Also, the proposed model was applied to the blind data, and the statistics of error prediction is RMSE = 7.76, which also shows a reasonable accuracy of the model. This model will save the time and cost on simulations and experiments, and will help in designing an accurate link budget calculation for a future enhanced system for ultra-wideband body-centric wireless systems

Evaluation of ultra-wideband in vivo radio channel and its effects on system performance

This paper presents bit‐error‐rate (BER) performance analysis and improvement using equalizers for an in vivo radio channel at ultra‐wideband frequencies (3.1 GHz to 10.6 GHz). By conducting simulations using a bandwidth of 50 MHz, we observed that the in vivo radio channel is affected by small‐scale fading. This fading results in intersymbol interference affecting upcoming symbol transmission, causing delayed versions of the symbols to arrive at the receiver side and causes increase in BER. A 29‐taps channel was observed from the experimentally measured data using a human cadaver, and BER was calculated for the measured in vivo channel response along with the ideal additive white Gaussian noise and Rayleigh channel models. Linear and nonlinear adaptive equalizers, ie, decision feedback equalizer (DFE) and least mean square (LMS), were used to improve the BER performance of the in vivo radio channel. It is noticed that both the equalizers improve the BER but DFE has better BER compared to LMS and shows the 2‐dB and 4‐dB performance gains of DFE over the LMS at Eb/No = 12 dB and at Eb/No = 14 dB, respectively. The current findings will help guide future researchers and designers in enhancing systems performance of an ultra‐wideband in vivo wireless systems

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels

Markov random field image processing applications on ruins of the Hittite Empire

In this paper, we applied Markov random field processing to geophysical data as an alternative to classical deterministic approaches. Markov random field processing is an unsupervised statistical model-based algorithm, which does not require a priori information. We present a dynamic programming based on evaluation of noisy and superpositioned effects of the various geological structures considering a statistical maximum a posteriori criterion. The objective of the proposed modelling is to capture the intrinsic character of the input potential anomaly map in a few parameters, so as to understand the nature of the phenomenon generating the anomaly. In order to decrease processing time and to enhance image performance of the Markov random field, we introduce a preprocessing step. The preprocessing step is crucial and it helps us to solve difficult multi-disciplinary problems such as separation, enhancement of magnetic anomalies and border detection. We also decrease the noisy peak values of pixels by this smoothing process and emphasize the discontinuity properties of the noisy data by an absolute differentiation procedure. Here, the magnetic field of the input data is considered as a two-dimensional image with a matrix composed of N-1 x N-2 pixels. We evaluate each pixel of the N-1 x N-2, matrix using the Markov random field approach, regarding the neighbouring pixels and their locality in real time with no a priori training procedure. As synthetic examples, various prism models are considered and the separation and edge detection performance of the Markov random field is tested. As real data, we have evaluated the magnetic anomaly map of the Hittite civilization in the Sivas-Altinyayla region of Turkey. We have obtained satisfactory results in both synthetic and real data and concluded that the Markov random field is a compromising approach for the separation problem of regional-residual anomalies and edge detection of various geological bodies

Colonic Polyp Detection in CT Colonography with Fuzzy Rule Based 3D Template Matching

In this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8 x 8, 12 x 12 and 20 x 20 to detect polyps. The CAD system is evaluated with 1043 CT colonography images from 16 patients containing 15 marked polyps. All colon regions are segmented properly. The overall sensitivity of proposed CAD system is 100% with the level of 0.53 false positives (FPs) per slice and 11.75 FPs per patient for the 8 x 8 cell template. For the 12 x 12 cell templates, detection sensitivity is 100% at 0.494 FPs per slice and 8.75 FPs per patient and for the 20 x 20 cell templates, detection sensitivity is 86.66% with the level of 0.452 FPs per slice and 6.25 FPs per patient

Heuristic construction of high-rate linear block codes

We propose a new heuristic construction technique that generates all the even codes of length greater than 8 with full rank property for Hamming distance-4. The codes generated by the proposed method include the extended Hamming codes and the Reed Muller codes of distance-4. The way of obtaining the codes with higher minimum distance using the proposed method is also described. (c) 2005 Elsevier GmbH. All rights reserved

Multilevel turbo coded-continuous phase frequency shift keying (MLTC-CPFSK)

In this paper, we introduce a Turbo coded modulation scheme, called multilevel turbo coded-continuous phase frequency shift keying (MLTC-CPFSK). The underlying basis of multilevel coding is to partition a signal set into several levels and to encode separately each level through the respective layer of the encoder. In MLTC-CPFSK, to provide phase continuity of the signals, turbo encoder and continuous phase encoder (CPE) are serially concatenated at the last level, while all other levels consist of only a turbo encoder. Therefore, the proposed system contains multiple turbo encoder/decoder blocks in its architecture. The parallel input data sequences are encoded by our multilevel scheme and mapped to CPFSK signals. Then, for the purpose of performance analysis, these modulated signals are passed through AWGN and fading channels. At the receiver side, the input sequence of the first level is estimated by the first turbo decoder block. Subsequently, the other input sequences of other levels are computed using the estimated input bit streams of the respective previous levels. Simulation results are drawn for 4-ary CPFSK two level and 8-ary CPFSK three level turbo codes over AWGN, Rician, and Rayleigh channels for three iterations while frame sizes are chosen as 100 and 1024. It is concluded that satisfactory performance is achieved in MLTC-CPFSK systems for all SNR values in various fading environments. (C) 2008 Elsevier Ltd. All rights reserved

Lung nodule diagnosis using 3D template matching

In this paper, to utilize the third dimension of Computed Tomography, regions of interest (ROI) slices were combined to form 3D ROI image and a 3D template was determined to find the structures with similar properties of nodules. Convolution of 3D ROI image with the proposed template strengthens the shapes similar to the template and weakens the other ones. False-positive (FP) per nodule and per slice versus diagnosis sensitivity were obtained. The Computer Aided Diagnosis system achieved 100% sensitivity with 0.83 FP per nodule and 0,46 FP per slice, when the nodule thickness was greater than or equal to 5.625 mm. (c) 2006 Elsevier Ltd. All rights reserved

A BACKPROPAGATION NEURAL NETWORK APPROACH FOR OTTOMAN CHARACTER RECOGNITION

The Ottoman Empire established in 1299 and continued 6 centuries covering an area of about 5.6 million squared km. The Empire left a large collection of valuable archives interesting to historians from all over the world. Investigation and understanding these documents will shed light on the history of the world. In order to achieve access of the considered information by worldwide scientists, it is essential to translate Ottoman characters into Latin alphabet. Thus, we aimed to recognize the Ottoman characters using Artificial Neural Network (ANNT) and compared it with Support Vector Machine (SVM) approaches. We used printed type of Ottoman scripts in image acquisition. Pre-processing such as normalization and edge detection were implemented. Multilayer perceptions of ANN were trained using the backpropagation learning algorithm. As a result of our research, we are able to classify the Ottoman characters with 85.5% classification accuracy using the proposed recognition system

Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme

The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation. (C) 2013 Elsevier Ltd. All rights reserved