The Utility of Simulink Subsystems in Handling and Processing of Biomedical Signals and Images

To model, simulate, and analyze multi-domain dynamical systems, Simulink, which is a Matlab-based graphical programming environment, can be used effectively. Due to the drag-drop facility, accessible graphic user interface components, and zero coding environments, Simulink becomes the most used tool both in industry and academia. The design cycle time of any real-time systems can be reduced using Simulink than other software tools. This article focuses mainly on the utility behind the subsystems such as enabled subsystem, triggered subsystem, triggered and enabled subsystem, and control flow subsystem in biomedical signal and image processing. Image segmentation using enabled subsystem, voiced/unvoiced classification using triggered subsystem, and the computation of root-mean-square (RMS) amplitude using If Action subsystem are implemented using breast cancer image and human voice signal. The Matlab 9.4 tool is used for experimental simulation with the biomedical signals and images.

Digital Hearing Aids Using Automated Varying Bandwidth Finite Impulse Response Filter Banks

A human auditory system is a highly complex sensitive system which transfers the acoustic sound into neuroelectrical signals toward the brain. Hearing difficulties or deafness are the outcomes of the problems occurred at any part of the auditory system. Assistive technologies such as hearing aids are developed to improve the quality of life of the hearing impaired people. Current digital hearing aids have fixed bandwidth filter banks which cannot provide enough flexibility to match with audiogram of different hearing loss. Recently, variable bandwidth filter banks have been introduced with different technologies to match more closely with the audiogram of a particular hearing loss. This research work proposes and implements a software controlled variable bandwidth FIR filter bank using Matlab GUI. In the Matlab GUI, in the filter bank, the bandwidth of every filter is adjusted dynamically in the Matlab GUI such that it fits more closely to the audiogram of a particular hearing loss. An experiment has been conducted in the developed Matlab GUI with various hearing loss, and the results show that the proposed system matches the filter bank magnitude response very closely to the audiogram of a particular hearing loss and reduces the matching error.

Advanced Digital Signal Processing Techniques on the Classification of the Heart Sound Signals

Speech processing subject primarily depends on the digital signal processing (DSP) methods, such as convolution, discrete Fourier transform (DFT), fast Fourier transforms (FFT), finite impulse response (FIR) and infinite impulse response (IIR) filters, FFT recursive and non-recursive digital filters, FFT processing, random signal theory, adaptive filters, upsampling and downsampling, etc. Recursive and non-recursive digital filters are primarily deployed to absorb the signal of interest signals and to block the unwanted signals (noise). Broadly, low-pass, high-pass, band-pass, and band-stop filters are implemented for filtering functions. In frequent, the DSP theories can be used for further biomedical engineering domains like biomedical imaging (MRI, ultrasound, CT, X-ray, PET) and genetic signal analysis-cum-processing too. In this article, the experiments such as voiced/unvoiced detection, formants estimation using FFT and spectrograms, pitch estimation and tracking and yes/no sound classification are used. Also, the analysis of normal/abnormal heart sound signals using simple energy computation and the zero-crossing rate and their results are obtained. For the entire study, the Matlab R2018a tool is used to obtain the simulation results. At last, the criticism, feedbacks, comments, reactions from the student are detailed for the exceptional development of the course.

Revisiting FPGA Implementation of Digital Filters and Exploring Approximate Computing on Biomedical Signals

In recent years, the approximate computing becomes popular in the era of VLSI (very large scale integration) domain to arrive better power, area, and delay outcomes at the cost of lower precision loss. Also, the human beings are not so intelligent to see/observe/listen the processed digital data; means even if some of the data loss occurs human beings are unable to notice them. This behavior set the engineers to research on approximate computing which are very useful in the multimedia data processing, data communications, high-volume data storage, etc. In this study, the experiments such as hum-noise removal, filters on QRS detection are implemented on an Altera FPGA EP4CEF29C7 device using Quartus II 13.1 synthesis software tool and the simulation results on device utilization reports, the speed and the power are obtained. Simulation results reveal that the approximate computational filters offer better power, area, and speed results than the conventional ones. Also, Matlab 9.4 (R2018a) simulation was used to carry out the functional verification of the actual and approximate filters.

Exploring Digital Image Dithering Techniques on a Broken Foot Image

The contouring effects appear when an image is quantized rudely irrespective of the uniform or non-uniform quantization. To mitigate the effects of contouring, a small amount of random noise is added (dithered) to the original image before quantization. Techniques such as dithering and half-toning are widely used strategies in obtaining images and texts in magazines, newspapers, books, printers, computer monitors, and LCDs. This study explores the dithering technique on a broken foot image with more elaborative methods and results. All the experiments involved in this study, such as quantization, dithering, no dithering, and dithering, quantized, and filtered techniques, are conducted using the Matlab R2016b tool. Overall information and details are retained with the aid of lowpass filtering and highpass filtering, respectively. Simulation results such as Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are obtained in every stage of the dithering procedure to analyze and compare the performance or accuracy.

Binary and Multiclass Classification of Histopathological Images Using Machine Learning Techniques

Background and Objective: Breast cancer is fairly common and widespread form of cancer among women. Digital mammogram, thermal images of breast and digital histopathological images serve as a major tool for the diagnosis and grading of cancer. In this paper, a novel attempt has been proposed using image analysis and machine learning algorithm to develop an automated system for the diagnosis and grading of cancer. Methods: BreaKHis dataset is employed for the present work where images are available with different magnification factor namely 40×, 100×, 200×, 400× and 200× magnification factor is utilized for the present work. Accurate preprocessing steps and precise segmentation of nuclei in histopathology image is a necessary prerequisite for building an automated system. In this work, 103 images from benign and 103 malignant images are used. Initially color image is reshaped to gray scale format by applying Otsu thresholding, followed by top hat, bottom hat transform in preprocessing stage. The threshold value selected based on Ridler and calvard algorithm, extended minima transform and median filtering is applied for doing further steps in preprocessing. For segmentation of nuclei distance transform and watershed are used. Finally, for feature extraction, two different methods are explored. Result: In binary classification benign and malignant classification is done with the highest accuracy rate of 89.7% using ensemble bagged tree classifier. In case of multiclass classification 5-class are taken which are adenosis, fibro adenoma, tubular adenoma, mucinous carcinoma and papillary carcinoma the combination of multiclass classification gives the accuracy of 88.1% using ensemble subspace discriminant classifier. To the best of author’s knowledge, it is the first made in a novel attempt made for binary and multiclass classification of histopathology images. Conclusion: By using ensemble bagged tree and ensemble subspace discriminant classifiers the proposed method is efficient and outperform the state of art method in the literature.

Penetration Testing and Security Assessment of Healthcare Records on Hospital Websites

At present, computer security is the flourishing field in the IT industry. Nowadays, the usage of computers and the Internet grows drastically, and hence, computers become vehicles for the attackers to spread viruses and worms, to distribute spam and spyware, and to perform denial-of-service attacks, etc. The IT engineers (even users) should know about network security threats, and at the same time, to some extent, they should know techniques to overcome the issues. The reliability and privacy of healthcare records of the patients are the most critical issue in the healthcare business industry sector. The security safeguards, such as physical, technical, and administrative safeguards, are crucial in protecting the information in all aspects. This article deals with the forty popular hospital portals in India related to the professional and network security related issues such as operating system guesses, number of open/closed/filtered ports, the name of the Web server, etc. The Nmap (network mapper) tool is used to analyze the results belong to the security perspective.

Automated Genomic Signal Processing for Diseased Gene Identification

Genomic signal processing (GSP) is the engineering discipline for the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Statistical Computations on DNA Sequences is one of key areas in which GSP can be applied. In this paper, we apply DSP tools on trinucleotide repeat disorders (too many copies of a certain nucleotide triplet in the DNA) to classify any gene sequence into diseased/non-diseased state. Intially, we collected the Gene sequences responsible for trinucleotide repeat disorders from NCBI. Then, we applied GSP techniques to convert the given gene sequence into an indicator sequence, and furthermore we apply Fast Fourier transforms (FFTs) and Discrete Wavelet Transforms (DWTs), followed by statistical feature extraction and the obtained statistical features, fed into an Artificial Neural Network to predict the state of the input genomic sequence.