Video Surveillance-Based Intelligent Traffic Management in Smart Cities

Visualization of video is considered as important part of visual analytics. Several challenges arise from massive video contents that can be resolved by using data analytics and consequently gaining significance. Though rapid progression in digital technologies resulted in videos data explosion that incites the requirements to create visualization and computer graphics from videos, a state-of-the-art algorithm has been proposed in this chapter for 3D conversion of traffic video contents and displaying on Google Maps. Time stamped visualization based on glyph is employed efficiently in surveillance videos and utilized for event detection. This method of visualization can possibly decrease the complexity of data, having complete view of videos from video collection. The effectiveness of proposed system has shown by obtaining numerous unprocessed videos and algorithm is tested on these videos without concerning field conditions. The proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data

Classification of Phonocardiogram using an Adaptive Fuzzy Inference System

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Towards Low Latency and Resource-Efficient FPGA Implementations of the MUSIC Algorithm for Direction of Arrival Estimation

The estimation of the Direction of Arrival (DoA) is one of the most critical parameters for target recognition, identification and classification. MUltiple SIgnal Classification (MUSIC) is a powerful technique for DoA estimation. The algorithm requires complex mathematical operations like the computation of the covariance matrix for the input signals, eigenvalue decomposition and signal peak search. All these signal processing operations make real-time and resource-efficient implementation of the MUSIC algorithm on Field Programmable Gate Arrays (FPGAs) a challenge. In this paper, a novel design approach is proposed for the FPGA-implementation of the MUSIC algorithm. This approach enables a significant reduction in both FPGA resources and latency. In more detail, the proposed design enables the estimation of DoA in real-time scenarios in 2μ sec with 30% to 50% fewer resources as compared to existing techniques.The work of Pedro Reviriego was supported in part by the Architecting Intelligent Cost-effective Central Offices to enable 5G Tactile Internet (ACHILLES) through the Spanish Ministry of Economy and Competitivity under Project PID2019-104207RB-I00, in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with Universidad Carlos III de Madrid (UC3M) in the line of Excellence of University Professors under Grant EPUC3M21, and in part by the Context of the V Plan Regional de Investigación Científica e Innovación Tecnológica (V PRICIT) (Regional Program of Research and Technological Innovation)

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction

A novel algorithm for link adaptation using fuzzy rule based system for wideband networking waveform of SDR

Adaptive and high speed communication with changing user requirements and environment conditions are the future needs of wireless networks. This is achieved by using the available resources optimally to increase the overall throughput by satisfying specific constraints. In this paper, a link adaptation algorithm for a hybrid multicode CDMA/adaptive TDMA based wideband networking waveform of SDR is proposed. The requirement of link adaptation increases when the transmissions are in the form of network packets. A low value of bit error rate can drastically increase packet error rate and thus packet re-transmissions, if the signal and protocol parameters are not dynamically changed according to the channel conditions and tolerable bit error rate. A constrained optimization problem has been formulated under the Quality of Service and throughput constraints. The optimization problem has been solved using fuzzy rule based system by dynamically changing the modulation technique and the number of multicodes assigned to each user. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm in achieving better throughput by efficiently reducing the packet re-transmissions overhead

Cooperative spectrum sensing for cognitive radio networks application: Performance analysis for realistic channel conditions

ABSTRACT Recent research shows that more than 70% of the available spectrum is not utilized efficiently. The bandwidth becomes expensive due to a shortage of frequencies. Therefore for efficient utilization of spectrum, we need to sniff the spectrum to determine whether it is being used by primary user or not. The term cognitive radio refers to the adoption of radio parameters using the sensed information of the spectrum. There are various spectrum sensing techniques proposed in the literature but still there is room for researchers in this field to explore more sophisticated approaches. There are three major categories of spectrum sensing techniques; transmitter detection, receiver detection and interference temperature detection. This thesis presents a survey of techniques suggested in the literature for spectrum sensing with a performance analysis of transmitter-based detection techniques. iii ACKNOWLEDGMENT