Snehanshu Saha

A novel revenue optimization model to address the operation and maintenance cost of a data center

Enterprises are enhancing investments in cloud services setting up data centers to meet growing demand. A typical investment is of the order of millions of dollars, infrastructure and recurring cost included. This paper proposes an algorithmic/analytical approach to address the issues of optimal utilization of the resources towards a feasible and profitable model. The economic sustainability of such a model is accomplished via Cobb-Douglas production function. The production model seeks to answer questions on maximal revenue given a set of budgetary constraints. The model suggests minimum investments needed to achieve target output.

Distinct adoption of k-nearest neighbour and support vector machine in classifying EEG signals of mental tasks

In this paper, an attempt is made to apply few conventional methods of EEG feature extraction and classification methods and compare their performance for a specific task. Two different feature extraction and classification methods are implemented to classify the mental tasks of EEG signals from a known dataset. For this purpose, the auto regression model and the wavelet transform is used as feature extraction. A combined EEG feature vector is also evaluated on the classification accuracy. The features extracted from these methods are applied to the k-nearest neighbour and support vector machine classifiers separately. Each subject has ten trials of each mental task, in which five trials of each task is used for training the system. The remaining five tasks are used for testing the system. Four different trial combinations of each task are made. The results are evaluated using the confusion matrix. Experimental results specify that each method has specific advantages and disadvantages and is suitable for EEG signal analysis for a specific application.

Twofold classification of motor imagery using common spatial pattern

Motor imagery (MI) is a mental rehearsal of movement without any body movement. Brain-Computer Interface (BCI) uses MI in the neurological rehabilitation, especially in stroke rehabilitation to restore the patients motor abilities. BCI based on MI translates the subjects motor intent into control signals to control the devices like robotic arms, wheelchairs or to navigate the virtual worlds. In this work, multichannel electroencephalogram (EEG) signals of imagination of a right hand and right foot movement is considered. Common spatial pattern (CSP) is used to estimate the spatial filters for the multi-channel EEG data. The spatial filters lead to weighting of the channel/electrodes according to their variance in discriminating the two tasks performed. Channels with the largest variance are considered as significant channels. A two-fold classification method using support vector machine (SVM) is used to classify the test signal into right hand movement and right foot movement. In the present work, the analysis conducted demonstrate that the proposed twofold classification scheme can achieve upto 94.2% of accuracy in discrimination of the two tasks performed. The high-recognition rate and computational simplicity make CSP a promising method for an EEG-based BCI.

ScientoBASE: a framework and model for computing scholastic indicators of non-local influence of journals via native data acquisition algorithms

Defining and measuring internationality as a function of influence diffusion of scientific journals is an open problem. There exists no metric to rank journals based on the extent or scale of internationality. Measuring internationality is qualitative, vague, open to interpretation and is limited by vested interests. With the tremendous increase in the number of journals in various fields and the unflinching desire of academics across the globe to publish in “international” journals, it has become an absolute necessity to evaluate, rank and categorize journals based on internationality. Authors, in the current work have defined internationality as a measure of influence that transcends across geographic boundaries. There are concerns raised by the authors about unethical practices reflected in the process of journal publication whereby scholarly influence of a select few are artificially boosted, primarily by resorting to editorial maneuvers. To counter the impact of such tactics, authors have come up with a new method that defines and measures internationality by eliminating such local effects when computing the influence of journals. A new metric, Non-Local Influence Quotient is proposed as one such parameter for internationality computation along with another novel metric, Other-Citation Quotient as the complement of the ratio of self-citation and total citation. In addition, SNIP and international collaboration ratio are used as two other parameters. As these journal parameters are not readily available in one place, algorithms to scrape these metrics are written and documented as a part of the current manuscript. Cobb–Douglas production function is utilized as a model to compute Journal Internationality Modeling Index. Current work elucidates the metric acquisition algorithms while delivering arguments in favor of the suitability of the proposed model. Acquired data is corroborated by different supervised learning techniques. As part of future work, the authors present a bigger picture, Reputation and Global Influence Score, that will be computed to facilitate the formation of clusters of journals of high, moderate and low internationality.

DSRS: Estimation and forecasting of journal influence in the science and technology domain via a lightweight quantitative approach

The evaluation of journals based on their influence is of interest for numerous reasons. Various methods of computing a score have been proposed for measuring the scientific influence of scholarly journals. Typically the computation of any of these scores involves compiling the citation information pertaining to the journal under consideration. This involves significant overhead since the article citation information of not only the journal under consideration but also that of other journals for the recent few years need to be stored. Our work is motivated by the idea of developing a computationally lightweight approach that does not require any data storage, yet yields a score which is useful for measuring the importance of journals. In this paper, a regression analysis based method is proposed to calculate Journal Influence Score. Proposed model is validated using historical data from the SCImago portal. The results show that the error is small between rankings obtained using the proposed method and the SCImago Journal Rank, thus proving that the proposed approach is a feasible and effective method of calculating scientific impact of journals.

QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks

Abstract In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the optimal routing by satisfying the Quality of Service (QoS) constraints is an ambitious task. Multiple paths are available from the source node to the gateway for reliability, and sometimes it is necessary to deal with failures of the link in WMN. A major challenge in a MCMR-WMN is finding the routing with QoS satisfied and an interference free path from the redundant paths, in order to transmit the packets through this path. The Particle Swarm Optimization (PSO) is an optimization technique to find the candidate solution in the search space optimally, and it applies artificial intelligence to solve the routing problem. On the other hand, the Genetic Algorithm (GA) is a population based meta-heuristic optimization algorithm inspired by the natural evolution, such as selection, mutation and crossover. PSO can easily fall into a local optimal solution, at the same time GA is not suitable for dynamic data due to the underlying dynamic network. In this paper we propose an optimal intelligent routing, using a Hybrid PSO-GA, which also meets the QoS constraints. Moreover, it integrates the strength of PSO and GA. The QoS constraints, such as bandwidth, delay, jitter and interference are transformed into penalty functions. The simulation results show that the hybrid approach outperforms PSO and GA individually, and it takes less convergence time comparatively, keeping away from converging prematurely.

EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

EEG based brain computer interface has emerged as a hot spot in the study of neuroscience, machine learning and rehabilitation in the recent years. A BCI provides a platform for direct communication between a human brain and a computer without the normal neurophysiology pathways. The electrical signals in the brain, because of their fast response to cognitive processes are most suitable as non-motor controlled mediation between the human and a computer. It can serve as a communication and control channel for different applications. Though the primary goal is to restore communication in severely paralyzed population, the BCI for speech communication fetches recognition in a variety of non-medical fields, the silent speech communication, cognitive biometrics and synthetic telepathy to name a few. A survey of diverse applications and principles of the BCI technology used for speech communication is discussed in this chapter. An ample evidence of speech communication used by “locked-in” patients is specified. Through the aid of assistive computer technology, they were able to pen their memoir. The current state-of-the-art techniques and control signals used for speech communication is described in brief. Possible future research directions are discussed. A comparison of indirect and direct methods of BCI speech production is shown. The direct method involves capturing the brain signals of the intended speech or speech imagery, processes the signals to predict the speech and synthesizes the speech production in real-time. There is enough evidence that the direct speech prediction from the neurological signals is a promising technology with fruitful results and challenging issues.

Computing the Prestige of a journal: A Revised Multiple Linear Regression Approach

The evaluation of journals based on their influence is of interest for numerous reasons. Various methods of computing a score have been proposed for measuring the scientific influence of scholarly journals. Typically the computation of any of these scores involves compiling the citation information pertaining to the journal under consideration. This involves significant overhead since the article citation information of not only the journal under consideration but also that of other journals for the recent few years need to be stored. Our work is motivated by the idea of developing a computationally lightweight scheme that does not require any data storage, yet yields a score which is useful for measuring the importance of journals. In this paper, a Journal Influence Score is mooted and a regression analysis based method is proposed to calculate the score. We validated our model using historical data from the SCImago portal. The results are promising, the rankings obtained using the proposed method compare favourably with the SCImago Journal Rank, thus indicating that the proposed approach is a feasible and effective method of calculating scientific impact of journals.

An Energy-efficient and Buffer-aware Routing Protocol for Opportunistic Smart Traffic Management

Smart Traffic Management (STM) is a major application domain for developing Smart City systems. In an STM, sensors attached to the vehicles sense the environment and exchanges the sensed data with other vehicles. Due to the high rate of mobility, an STM suffers from frequent disconnections and need to resort to opportunistic encounters for communication. Since the sensors used in STM are having limited energy and buffer space, designing energy-efficient and buffer-aware message forwarding is quite challenging in an opportunistic STM. In this paper, we have designed an energy-efficient and buffer-aware routing protocol, EBR, for an opportunistic STM, which will select the relay nodes based on their remaining energy level and buffer space. We have conducted extensive simulations for evaluating the performance of our proposed EBR protocol. We also have developed a prototype smart vehicular test-bed for evaluating performance of the EBR, in real time. Both simulation and test-bed results show that the EBR outperforms some of the existing opportunistic routing protocols in terms of delivery ratio, delivery delay and energy efficiency.

Evidence of Chaos in EEG Signals: An Application to BCI

The recent science and technology studies in neuroscience and machine learning have focused attention on investigating the functioning of the brain through nonlinear analysis. The brain is a nonlinear dynamic system, imparting randomness and nonlinearity in the EEG signals. The stochastic nature of the brain seeks the paramount importance of understanding the underlying neurophysiology. The nonlinear analysis of the dynamic structure may help to reveal the complex behavior of the brain signals. EEG signal analysis is helpful in various clinical applications to characterize the normal and diseased brain states. The EEG is used in predicting epileptic seizures, classifying the sleep stages, measuring the depth of anesthesia, and detecting the abnormal brain states. With the onset of EEG-based brain-computer interfaces, the characteristics of brain signals are used to control the devices through different mental states. Hence, the need to understand the brain state is important and crucial. In this chapter, the author introduces the theory and methods of chaos theory measurements and its applications in EEG signal analysis. A broad perspective of the techniques and implementation of the Correlation Dimension, Lyapunov Exponents, Fractal Dimension, Approximate Entropy, Sample Entropy, Hurst Exponent, Lempel-Ziv complexity, Hopf Bifurcation Theorem and Higher-order spectra is explained and their usage in EEG signal analysis is mentioned. We suggest that chaos theory provides not only potentially valuable diagnostic information but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.