Digital coding of waveforms: Principles and applications to speech and video

Cell phones have become a necessity for many people throughout the world. The ability to keep in touch with family, business associates, and access to email are only a few of the reasons for the increasing importance of cell phones. However, the mobile-phones in early times were bulky, restrictive to only some features and worked only in areas where there was a good connection. All these problems were resolved by integrating a processor within a cell-phone. The processor is the central hub of your smartphone. It receives and executes every command, performing billions of calculations per second. The effectiveness of the processor directly affects every application you run, whether it's the camera, the music player, or just a simple email program. In the following journal, we have discussed the distinctive features, merits, and demerits of the latest mobile phone processors of different Tech companies.

A Comparative Study on the Recent Smart Mobile Phone Processors

This paper reports an efficient electrocardiogram (ECG) data compression algorithm for tele-monitoring of cardiac patients from rural area, based on combination of two encoding techniques with discrete cosine transform. The proposed technique provides good compression ratio (CR) with low percent root-mean-square difference (PRD) values. For performance evaluation of the proposed algorithm 48 records of ECG signals are taken from MIT-BIH arrhythmia database. Each record of ECG signal is of duration 1 minute and sampled at sampling frequency of 360 Hz. Noise of the ECG signal has been removed using Savitzky-Golay filter. To transform the signal from time domain to frequency domain, discrete cosine transform has been used which compacts energy of the signal to lower order of frequency coefficients. After normalisation and rounding of transform coefficients, signals are encoded using dual encoding technique which consists of run length encoding and Huffman encoding. The dual encoding technique compresses data significantly without any loss of information. The proposed algorithm offers average values of CR, PRD, quality score, percent root mean square difference normalised, RMS error and SNR of 11.49, 3.43, 3.82, 5.51, 0.012 and 60.11 dB respectively.

/pdf/ecg-data-compression-algorithm-for-tele-monitoring-of-1xib484h2m.pdf

ECG data compression algorithm for tele-monitoring of cardiac patients

Electrocardiogram (ECG) gives useful information about morphological and functional details of heart which is used to predict various cardiac diseases. In this paper a method of detecting cardiac diseases using support vector machine (SVM) is proposed. In this proposed method diseases are modeled using the time domain features of ECG signal which are extracted using BIOPAC AcqKnowledge software. Raw ECG signal contains these useful features which can be used to detect cardiac arrhythmia. The various ECG parameters like heart rate, QRS complex, PR interval, ST segment elevation, ST interval of ECG signal are used for analysis. Based on these parameters of ECG signal, different heart disease like atrial fibrillation, sinus tachycardia, myocardial infarction and apnea are detected. The individual accuracy of tachycardia arrhythmia, MI arrhythmia, atrial fibrillation arrhythmia and apnea proposed by SVM are 83.3%, 86.4%, 88% and 85.7% respectively.

An approach of cardiac disease prediction by analyzing ECG signal

The greatest challenge faced during the process of diagnosis of cardiovascular diseases is the accurate analyses of the Electrocardiogram (ECG). Many researches are being done to classify and analyze the ECG signals automatically. In this paper, a novel method for the Auto analysis of the ECG signals using MATLAB is proposed and implemented. In this method, the raw ECG data obtained from the patient goes through a process of Wavelet Packet Decomposition (WPD) followed by Feature extraction. The classification is further done using Artificial Neural Network (ANN). This method, succeeding in differentiating the Abnormal ECG signals from the Normal signals, is proved to be a novel method for Auto analysis of ECG signals.

Auto analysis of ECG signals using artificial neural network

Recently machine learning algorithms are widely used for the prediction of different attributes, and these algorithms find widespread applications in a variety of domains. Machine learning in health care has been one of the core areas of research where machine learning models are used on the medical datasets to predict different attributes. This work provides a comparative evaluation of different classical as well as ensemble machine learning models, which are used to predict the risk of diabetes from two different datasets, i.e., PIMA Indian diabetes dataset and early-stage diabetes risk prediction dataset. From the comparative analysis, it is found that the superlearner model provides the best accuracy i.e. 86% for PIMA Indian diabetes dataset, and it provides 97% accuracy for diabetes risk prediction dataset.

Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms

Road accidents are the major risk factor in day-to-day life. The prevention and quick detection of an accident is the priority in saving the lives of human beings. Advances in technologies like the internet of things (IoT) make life better for everyone but adding technologies to control and manage traffic in a smarter way is a big challenge. Accident prevention and detection system (APDS) is developed to provide real time support to the people through IoT. The APDS aims to provide an early solution to day-to-day traffic incidents. The prevention of accidents is more important as vehicles are controlled by human beings. The parameters, like change in speed, human body part movements, overtaking, rule braking, etc., are responsible for the accident. It can be managed or controlled using some rule-based techniques. The abnormal behavior of each parameter can be identified by continuous monitoring, and reporting the same well in before may reduce the occurrence of an accident. Once an accident occurs, the detail information of accident data are shared with end-users with some proper authentication. The information sharing is established through machine-to-machine (M2M) communication. The end-users will get all the data regarding location, time of the accident, and many more details by accessing the web link through the internet.

A User Specific APDS for Smart City Applications

This study proposes deep learning-based facial emotion recognition (FER) for driver health care. The FER system will monitor the emotional state of the driver's face to identify the driver's negligence and provide immediate assistance for safety. This work uses a transfer learning-based framework for FER which will help in developing an in-vehicle driver assistance system. It implements transfer learning SqueezeNet 1.1 to classify different facial expressions. Data preprocessing techniques such as image resizing and data augmentation have been employed to improve performance. The experimental study uses static facial expressions publicly available on several benchmark databases such as CK+, KDEF, FER2013, and KMU-FED to evaluate the model's performance. The performance comparison only showed superiority over state-of-the-art technologies in the case of the KMU-FED database, i.e., maximum accuracy of 95.83 %, and the results showed comparable performance to the rest of the benchmark databases.

Deep Learning-Based Facial Emotion Recognition for Driver Healthcare

This study develops a framework for personalized care to tackle heart disease risk using an at-home system. The machine learning models used to predict heart disease are Logistic Regression, K - Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest and XG Boost. Timely and efficient detection of heart disease plays an important role in health care. It is essential to detect cardiovascular disease (CVD) at the earliest, consult a specialist doctor before the severity of the disease and start medication. The performance of the proposed model was assessed using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Compared to all machine learning algorithms, the Random Forest algorithm shows a better performance accuracy score of 90.16%. The best model may evaluate patient fitness rather than routine hospital visits. The proposed work will reduce the burden on hospitals and help hospitals reach only critical patients.

Goutam Kumar Sahoo

Papers

Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms

A User Specific APDS for Smart City Applications

Deep Learning-Based Facial Emotion Recognition for Driver Healthcare

Machine Learning-Based Heart Disease Prediction: A Study for Home Personalized Care

A Two-Tier Machine Learning Framework for Risk Assessment in Drivers with Cardiovascular Disorders