Compact light-emitting-diode sun photometer for atmospheric optical depth measurements

A new compact light-emitting diode (LED) sun photometer, in which a LED is used as a spectrally selective photodetector as well as a nonlinear feedback element in the operational amplifier, has been developed. The output voltage that is proportional to the logarithm of the incident solar intensity permits the direct measurement of atmospheric optical depths in selected spectral bands. Measurements made over Ahmedabad, India, show good agreement, within a few percent, of optical depths derived with a LED as a photodetector in a linear mode and with a LED as both a photodetector and a feedback element in an operational amplifier in log mode. The optical depths are also found to compare well with those obtained simultaneously with a conventional filter photometer.     

Various dimensional colour image encryption based on non-overlapping block-level diffusion operation

This paper proposes a colour image encryption scheme to encrypt colour images of arbitrary sizes. In this scheme, a fixed block size (3 × 8) based block-level diffusion operation is performed to encrypt arbitrary sized images. The proposed technique overcomes the limitation of performing block-level diffusion operations in arbitrary sized images. This method first performs bit-plane decomposition and concatenation operation on the three components (blue, green, and red) of the colour image. Second it performs row and column shuffling operation using the Logistic-Sine System. Then the proposed scheme executes block division and fixed block-level diffusion (exclusive-OR) operation using the key image generated by the Piece-wise Linear Chaotic Map. At last, the cipher image is generated by combining the diffused blocks. In addition, the SHA-256 hashing on plain image is used to make chaotic sequences unique in each encryption process and to protect the ciphertext against the known-plaintext attack and the chosen-plaintext attack. Simulation results and various parameter analysis demonstrate the algorithm’s best performance in image encryption and various common attacks.

     

Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal

Voice activity detection (VAD) refers to the task of identifying vocal segments from an audio clip. It helps in reducing the computational overhead as well elevate the recognition performance of speech-based systems by helping to discard the non vocal portions from an input signal. In this paper, a VAD technique is presented that uses line spectral frequency-based statistical features namely LSF-S coupled with extreme learning-based classification. The experiments were performed on a database of more than 350 h consisting of data from multifarious sources. We have obtained an encouraging overall accuracy of 99.43%.     

A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals

Higher-order spectra (HOS) is an efficient feature extraction method used in various biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities, and affective computing. The motive of this work was to explore the application of HOS for an automated diagnosis of Parkinson’s disease (PD) using electroencephalography (EEG) signals. Resting-state EEG signals collected from 20 PD patients with medication and 20 age-matched normal subjects were used in this study. HOS bispectrum features were extracted from the EEG signals. The obtained features were ranked using t value, and highly ranked features were used in order to develop the PD Diagnosis Index (PDDI). The PDDI is a single value, which can discriminate the two classes. Also, the ranked features were fed one by one to the various classifiers, namely decision tree (DT), fuzzy K-nearest neighbor (FKNN), K-nearest neighbor (KNN), naive bayes (NB), probabilistic neural network (PNN), and support vector machine (SVM), to choose the best classifier using minimum number of features. We have obtained an optimum mean classification accuracy of 99.62%, mean sensitivity and specificity of 100.00 and 99.25%, respectively, using the SVM classifier. The proposed PDDI can aid the clinicians in their diagnosis and help to test the efficacy of drugs.

     

A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals

Alcoholism is a critical disorder related to the central nervous system, caused due to repeated and excessive consumption of alcohol. The electroencephalogram (EEG) signals are used to depict brain activities. It can also be employed for diagnosis of subjects consuming excessive alcohol. In this study, we have developed an automated system for the classification of alcoholic and normal EEG signals using a recently designed duration-bandwidth product (DBP), optimized three-band orthogonal wavelet filter bank (TBOWFB), and log-energy (LE). First, we obtain sub-bands (SBs) of EEG signals using the TBOWFB. Then, we use logarithms of the energies of the SBs as the discriminating features which are fed to the least square support vector machine (LS-SVM) for the discrimination of normal and alcoholic EEG signals. We have achieved a classification accuracy (CA) of 97.08%, with ten-fold cross validation strategy. The proposed model presents a promising performance, and therefore it can be used in a practical setup to assist the medical professionals in the diagnosis of alcoholism using EEG signals automatically.     

Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

Electrocardiogram is widely used to diagnose the congestive heart failure (CHF). It is the primary noninvasive diagnostic tool that can guide in the management and follow-up of patients with CHF. Heart rate variability (HRV) signals which are nonlinear in nature possess the hidden signatures of various cardiac diseases. Therefore, this paper proposes a nonlinear methodology, empirical mode decomposition (EMD), for an automated identification and classification of normal and CHF using HRV signals. In this work, HRV signals are subjected to EMD to obtain intrinsic mode functions (IMFs). From these IMFs, thirteen nonlinear features such as approximate entropy \( (E_{\text{ap}}^{x} ) \), sample entropy \( (E_{\text{s}}^{x} ) \), Tsallis entropy \( (E_{\text{ts}}^{x} ) \), fuzzy entropy \( (E_{\text{f}}^{x} ) \), Kolmogorov Sinai entropy \( (E_{\text{ks}}^{x} ) \), modified multiscale entropy \( (E_{{{\text{mms}}_{y} }}^{x} ) \), permutation entropy \( (E_{\text{p}}^{x} ) \), Renyi entropy \( (E_{\text{r}}^{x} ) \), Shannon entropy \( (E_{\text{sh}}^{x} ) \), wavelet entropy \( (E_{\text{w}}^{x} ) \), signal activity \( (S_{\text{a}}^{x} ) \), Hjorth mobility \( (H_{\text{m}}^{x} ) \), and Hjorth complexity \( (H_{\text{c}}^{x} ) \) are extracted. Then, different ranking methods are used to rank these extracted features, and later, probabilistic neural network and support vector machine are used for differentiating the highly ranked nonlinear features into normal and CHF classes. We have obtained an accuracy, sensitivity, and specificity of 97.64, 97.01, and 98.24 %, respectively, in identifying the CHF. The proposed automated technique is able to identify the person having CHF alarming (alerting) the clinicians to respond quickly with proper treatment action. Thus, this method may act as a valuable tool for increasing the survival rate of many cardiac patients.

     

On outage minimization in RF energy harvesting relay assisted bidirectional communication

This paper explores an end-to-end outage probability experienced in a bidirectional relay assisted communication where the relay is assumed to be equipped with an RF energy harvesting circuit. First, the closed-form expression for the outage of the system is derived. This is followed by the formulation of an unconstrained optimization problem to achieve minimum outage probability with respect to the relay placement and consequent time allocation for energy harvesting. The system model is further extended in an underlay cognitive radio framework to study the impact of a primary user outage constraint on the end-to-end outage performance of the two-way communications. The accuracy of analytical results is validated through simulation results. The impact of various system parameters like relay position, time allocation factor, target rate of transmission on the outage probability is also observed. In addition, it is also shown that spectral efficiency of the communication system using hybrid power-time switching relaying protocol is much superior to similar one-way and two-way relay assisted communication system with power splitting relaying protocol.

     

Functionalization of styrene–butadiene rubber with meta‐pentadecenyl phenol for better processing: A multifunctional additive and renewable resource

Meta‐pentadecenyl phenol, a nonisoprenoid phenolic lipid, is a renewable agricultural resource and also a byproduct of the cashew industry; it is popularly known as cardanol. This study throws light on the grafting of cardanol, which has been established as a multifunctional additive for natural rubber, onto the main‐chain backbone of styrene–butadiene rubber (SBR), a synthetic polymer used to imbibe the multifunctional properties of the former, such as those of a plasticizer, curing promoter, process aid, and antioxidant, into the latter. The grafting was carried out in the solution stage on a trial basis with a peroxide catalyst, and all of the grafting parameters were optimized with a Taguchi methodology. The grafting of cardanol onto the SBR backbone was successfully confirmed by UV–visible spectroscopy, Fourier transform infrared spectroscopy, and NMR analysis. Thermal analysis of the cardanol‐grafted styrene–butadiene rubber (C‐g‐SBR) revealed a higher thermal stability and better plasticizing effect than that those found in the virgin SBR. The rheological properties of the grafted rubber indicated the improvement of the pseudo‐plastic (shear‐thinning) nature compared to that in gum SBR. The unfilled C‐g‐SBR vulcanizates exhibited physicomechanical properties comparable to 5‐phr processing‐oil‐containing SBR [oil‐plasticized styrene–butadiene rubber (OPSBR)] vulcanizates. The carbon‐black‐filled C‐g‐SBR vulcanizates exhibited improved plasticization, a faster curing rate, easy processability, and better physicomechanical properties compared to the 5‐phr OPSBR vulcanizates. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45150.     

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer‐aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real‐world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary‐real PSO, which includes the combination of categorical and numerical encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles, which take random values in the range of each attribute in the rule. Two different feature selection methods based on multi‐objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD.