Performance Analysis of Channel-Aware Media Access Control Schemes

This thesis proposes a new Channel-Aware MAC (CA-MAC) protocol that allows more than two simultaneous transmissions to take place within a single wireless collision domain. In this proposed work, Multiple-Input Multiple-Output (MIMO) system is used to achieve higher spectral efficiency. The MIMO-based PHY layer has been adopted to help in controlling the transmission and to avoid any collisions by using weights gains technique on the antenna transmission, and by recovering any possible collisions using ZigZag decoding. In order to develop CA-MAC algorithm, to exploit the full potential of MIMO system, the library of 802.11x standard has been modified. NS-2 based simulations were conducted to study the performance of the proposed system. Detailed analysis and comparisons with current protocols schemes are presented

Modelling and Analysis of Smart Grids for Critical Data Communication

Practical models for the subnetworks of smart grid are presented and analyzed. Critical packet-delay bounds for these subnetworks are determined, with the overall objective of identifying parameters that would help in the design of smart grid with least end-to-end delay. A single-server non-preemptive queueing model with prioritized critical packets is presented for Home Area Network (HAN). Closed-form expressions for critical packet delay are derived and illustrated as a function of: i) critical packet arrival rate, ii) service rate, iii) utilization factor, and iv) rate of arrival of non-critical packets. Next, wireless HANs using FDMA and TDMA are presented. Upper and lower bounds on critical packet delay are derived in closed-form as functions of: i) average of signal-to interference-plus-noise ratio, ii) random channel scale, iii) transmitted power strength, iv) received power strength, v) number of EDs, vi) critical packet size, vii) number of channels, viii) path loss component, ix) distances between electrical devices and mesh client, x) channel interference range, xi) channel capacity, xii) bandwidth of the channel, and xiii) number of time/frequency slots. Analytical and simulation results show that critical packet delay is smaller for TDMA compared to FDMA. Lastly, an Intelligent Distributed Channel-Aware Medium Access Control (IDCA-MAC) protocol for wireless HAN using Distributed Coordination Function (DCF) is presented. The protocol eliminates collision and employs Multiple Input Multiple Output (MIMO) system to enhance system performance. Simulation results show that critical packet delay can be reduced by nearly 20% using MA-Aware protocol compared to IDCA-MAC protocol. However, the latter is superior in terms throughput. A wireless mesh backbone network model for Neighbourhood Area Network (NAN) is presented for forwarding critical packets received from HAN to an identified gateway. The routing suggested is based on selected shortest path using Voronoi tessellation. CSMA/CA and CDMA protocols are considered and closed{form upper and lower bounds on critical packet delay are derived and examined as functions of i) signal-to-noise ratio, ii) signal interference, iii) critical packet size, iv) number of channels, v) channel interference range, vi) path loss components, vii) channel bandwidth, and viii) distance between MRs. The results show that critical packet delay to gateway using CDMA is lower compared to CSMA/CA protocol. A fiber optic Wide Area Network (WAN) is presented for transporting critical packets received from NAN to a control station. A Dynamic Fastest Routing Strategy (DFRS) algorithm is used for routing critical packets to control station. Closed-form expression for mean critical packet delay is derived and is examined as a function of: i) traffic intensity, ii) capacity of fiber links, iii) number of links, iv) variance of inter-arrival time, v) variance of service time, and vi) the latency of links. It is shown that delay of critical packets to control station meets acceptable standards set for smart grid

ResMem-Net: memory based deep CNN for image memorability estimation

Image memorability is a very hard problem in image processing due to its subjective nature. But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. In this paper, we propose a novel deep learning architecture called ResMem-Net that is a hybrid of LSTM and CNN that uses information from the hidden layers of the CNN to compute the memorability score of an image. The intermediate layers are important for predicting the output because they contain information about the intrinsic properties of the image. The proposed architecture automatically learns visual emotions and saliency, shown by the heatmaps generated using the GradRAM technique. We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: ‘‘What makes an image memorable?“. The model is trained and evaluated using the publicly available Large-scale Image Memorability dataset (LaMem) from MIT. The results show that the model achieves a rank correlation of 0.679 and a mean squared error of 0.011, which is better than the current state-of-the-art models and is close to human consistency (p = 0.68). The proposed architecture also has a significantly low number of parameters compared to the state-of-the-art architecture, making it memory efficient and suitable for production

Scenario-based investigation on the effect of partial shading condition patterns for different static solar photovoltaic array configurations

This paper presents an in-depth analysis and investigation on the performance of static photovoltaic (PV) array configurations subjected to various partial shading conditions (PSCs). Under PSCs, the electrical characteristics of the PV modules are critically monitored and reasons for their behavioral changes are highlighted. By doing so, this study aims to improve the efficiency of PV systems by minimizing mismatch losses and determining the optimum array configuration which is characterized by the highest maximum power and lowest relative losses under PSCs. Besides, this study complements and carries forward the previous studies through the detailed analysis of each configuration subjected to various practically probable PSCs. Three different PV array sizes ( 5×4 , 5×5 , and 3×10 ) are used to analyze the results and performance under considered shading scenarios. MATLAB/Simulink platform is used to model and simulate the PV array using the single diode (5-parameters) model. In-depth analysis of current flow across cross-ties and bypass diodes activation shows that the diagonal shading pattern leads to lower power loss (PL). Besides, the Total Cross-Tied (TCT) configuration demonstrates superior performance under most of the PSCs compared to other configurations. These results provide valuable information about the performance of PV array which may lead to better estimation and prediction of global maximum power (GMP) generation of a PV system

Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar

Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved

A Systematic Literature Review for Software Portability Measurement: Preliminary Results

Software developers agree that software portability is a desirable attribute for their software quality. Software portability is mostly acquired by ad-hoc techniques when trying to port existing products. There is a lack of unified measuring approach of software portability in most computing platforms. This paper presents preliminary results of a systematic literature review, conducted to collect evidence on measuring software portability. The evidence was gathered from selected studies and based on a set of meaningful and focused questions. 49 studies of these were selected for data extraction performed against the research questions. We provide an overview of used\proposed measurement metrics of software portability. Our results suggested that there are scattered efforts to understand measurement of software portability, and no census has been achieved