airConn: A framework for tiered services in public wireless LAN hot spots

Access to the data services via wireless LANs at private and public hot spot sites is becoming commonplace. The goal of the airConn project is to define an architecture and a prototype implementation that enable the provision of premium and non-premium service tiers for both transient and nontransient users of wireless hot spots. airConn provides for dynamic renegotiation of service tiers and facilitates various billing modes. Thus, it enables service providers to increase their revenue opportunities via multiple flexibility manageable service offerings.     

Statistical analysis of morphological openings

Statistical analysis of morphological openings is carried out to study noise-suppression and edge-preserving properties of binary and gray-scale structuring elements. Based on the fact that basis functions are a general representation of any morphological mapping that is translation-invariant and increasing, it is shown that a statistical analysis using this representation is feasible and more general than the threshold decomposition approach     

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.

     

An election algorithm to ensure the high availability of leader in large mobile ad hoc networks

A fundamental problem of distributed systems, leader election, is presented in the context of mobile ad hoc networks (MANETs). In many distributed systems, the presence of a leader is necessary in order to monitor underlying computations, guarantee quality functioning, take checkpoints, generate the lost token, detect quiescence conditions, etc. Hence, several leader election algorithms have been proposed in the literature. Although, most of the algorithms focus on reducing the control message (messages that have the highest priority to deliver) count, there have been almost no attention on ensuring high availability of a leader despite various types of failures, especially, in the scenarios like rescue and warfare, where the absence of the leader, even for a short duration, may lead to havoc. We focus on this issue, particularly, for large MANETs, where a large number of applications fails to perform in the absence of a leader. We present a leader election algorithm for large MANETs. The algorithm is inspired by the concept of prevailing parliamentary democracy and elects three best-nodes – in terms of performance parameters like battery life, computing power, memory, hop distance, and mobility – as the president, leader, and vice leader. The president node works as the leader of the network, in case, the leader and the vice leader both become unavailable simultaneously. On the other hand, the leader node serves all the requests. Further, we create a house of elite nodes, which ensures the presence of an executive, i.e. a leader during re-election to restrict the message overhead as well as the election latency while executing coordination related activities.     

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.