Farman Ali Khan

Generation of Multiple Fano Resonances in Plasmonic Split Nanoring Dimer

We present a computational study of the plasmonic response of a split nanoring dimer resonator which supports multiple plasmonic Fano-like resonances that arises by the coupling and interference of the dimer plasmon modes. For the generation of Fano resonances with large modulation depths, numerous configurations of the dimer resonator are analyzed which are observed to be highly dependent on the polarization of incident light. Moreover, the influence of dimension of the split nanoring structure on the spectral positions and intensities of the higher order Fano resonances are also investigated, and it is found that the asymmetric Fano line shapes can be flexibly tuned in the spectrum by varying various geometrical parameters. Such Fano resonators are also discovered to offer high values of figure of merit and contrast ratio due to which they are suitable for high-performance biological sensors.

An Intelligent Load Management System With Renewable Energy Integration for Smart Homes

Demand side management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via home energy management systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, evolutionary algorithms-based (binary particle swarm optimization, genetic algorithm, and cuckoo search) DSM model for scheduling the appliances of residential users is presented. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.

Multiple color space channel fusion for skin detection

Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking of objectionable content. We investigate color based skin detection. We linearly merge different color space channels representing it as a fusion process. The aim of fusing different color space channels is to achieve invariance against varying imaging and illumination conditions. The non-perfect correlation between the color spaces is exploited by learning weights based on an optimization for a particular color space channel using the mathematical financial model of Markowitz. The weight learning process develops a color weighted model using positive training data only. Experiments on a database of 8991 images with annotated pixel-level ground truth show that the fusion of color space channels approach is well suited to stable and robust skin detection. In terms of precision and recall, the fusion approach provides a competitive performance to other state-of-the-art approaches which require negative and positive training data with the exception of the decision tree based classifier (J48). As a real-time application, we show that the weight based color channel fusion approach can be used for learning of weights for skin detection based on detected faces in image sequences.

Implementation of Affective States and Learning Styles Tactics in Web-Based Learning Management Systems

Learning styles and affective states have a significant effect on student learning. The aim of this paper is to present a concept to identify and integrate learning styles and affective states of a learner into web-based learning management systems and therefore providing learners with adaptive courses and additional individualized pedagogical guidance that is tailored to their learning styles and affective states. Through considering affective states and learning styles, learners are provided with a learning environment that is more personalized and tailored to learners needs and current situation, leading to better learning outcomes and progress.

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

Unordered rule discovery using Ant Colony Optimization

In this article, a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization (ACO). The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets, selected from miscellaneous domains, based on several performance measures. As opposed to its ancestors, our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable. The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.

Computationally efficient generic adaptive filter (CEGAF)

Enhancement to clean speech from noisy speech has always been a challenging issue for the researcher’s community. Various researchers have used different techniques to resolve this problem. These techniques can be classified into the unsupervised and supervised approaches. Amongst the unsupervised approaches, Spectral Subtraction and Wiener Filter are commonly exploited. However, such approaches do not yield significant enhancement in the speech quality as well as intelligibility. As compared to unsupervised, supervised approaches such as Hidden Markov Model produces enhanced speech signals with better quality. However, supervised approaches need prior knowledge about the type of noise which is considered their major drawback. Moreover, for each noise type, separate models need to be trained. In this paper, a novel hybrid approach for the enhancement of speech is presented to overcome the limitations of both supervised and unsupervised approaches. The filter weights adjustment on the basis of Delta Learning Rule makes it a supervised approach. To address the issue of construction of new model for each noise type, the filter adjusts its weights automatically through minimum mean square error. It is unsupervised as there is no need of estimation of noise power spectral density. Various experiments are performed to test the performance of proposed filter with respect to different parameters. Moreover, the performance of the proposed filter is compared with state-of-the-art approaches using objective and subjective measures. The results indicate that CEGAF outperforms the algorithms such as Wiener Filter, supervised NMF and online NMF.

Fuzzy based approach for adaptivity evaluation of web based open source Learning Management Systems

Adaptive Learning Management System is a concept that has been recently explored to meet the individual needs of learners. Research studies regarding learning environments’, adaptation and personalization indicate that they play a key role to inspire, motivate and engage learners. The Learning Management Systems (LMSs) in this regard are currently facing the challenge of adapting the entire learning procedure according to the individual learning needs. Presently, there are numerous open source, custom developed, and commercial LMSs available. This paper presents an evaluation of web based open source LMSs since they can be easily supplemented and integrated with other software solutions than commercial and custom developed systems. The aim of this evaluation is to find a LMS platform that is most appropriate and has the potential for augmenting to an adaptive one. Initially, the evaluation is based on Elimination by Aspects approach for the qualification and selection of LMSs. Afterwards, the selected nine LMSs were evaluated using Linear Weighted approach (LWA) and fuzzy logic approach. The LWA was applied by assigning performance ratings to the overall categorical as well as adaptivity features. Finally the fuzzy logic was applied to the assigned ratings of each feature for the fuzzification of crisp set values. The results indicate that Moodle outperforms other platforms in overall functionally as well as in adaptation category.

A Smart Home Energy Management Strategy Based on Demand Side Management

In this paper we propose an ECG optimization model for a smart home based on DSM. The proposed model is an efficient SHEM strategy. The model is proposed keeping in view the minimization of energy consumption, energy consumption cost and energy generation cost. The model is based on efficient scheduling of appliances and an ECG optimization algorithm is proposed. We are using and optimizing energy from two energy sources namely lceg and lcd which are also known as macrogrid and microgrid respectively. The problem is solved as cost optimization problem using genetic algorithm and mathematically formulated using binary MNKP. The simulation results show that our ECG model efficiently reduces the cost of energy consumption, energy generation and energy consumption utilization.

Computationally efficient selective video encryption with chaos based block cipher

Selective encryption techniques are usually used with resource limited communication infrastructure and devices like wireless networks and mobile devices, to reduce computational burden in securing large video data. This technique of securing a subset of data and hence reducing computation is usually considered a compromise on security. Similarly, if the data is not properly selected then the encryption procedure will result in compression inefficiency and format non-compliance. In this research work, these requirements of reduced computation with increased security, format compliance and compression efficiency are addressed. The security issue is addressed by carefully selecting the substitution boxes for the block cipher in use. For compression efficiency and compliance to the format the video data is selected such that the statistical and structural characteristics are preserved. In order to increase the security, different chaotic based substitution boxes, that are an integral part and the only nonlinear operation of the block ciphers, were studied for cryptographic strengths. The selected substitution boxes were used for permutation of selected video data and its encryption by integrating the S-box with the Advanced Encryption Standard and H.264/AVC. The video data selected to be secured, consist of discrete cosine transform coefficients; signs of trailing ones and non-zero transform coefficients. The discrete cosine transform coefficients were permuted using the selected S-box while the signs of trailing ones and non-zero transform coefficients were fully encrypted using AES with the modified S-box. Simulation results showed considerable visual degradation in the decoded video. It is also shown that the compression efficiency and format compliance was not compromised while keeping the computational load at minimum.