Imran Javed

Concealed weapon detection has gained widespread interest in recent times due to prevalent law and order situation. There is an ever‐increasing need of detection of body‐worn harmful objects from a safe distance to save precious human life and to cause minimal damage to infrastructure. Most of the conventional weapon detection schemes require proximity to target for detection. To alleviate the problem of close proximity, WiFi‐based wireless sensing has recently emerged as a promising technique for remote detection and sensing in different applications. The focus of this research is to implement a low cost and robust WiFi‐based wireless detection system for body‐worn concealed objects. The methodology is based on utilizing low‐cost WiFi sensors to acquire Channel State Information (CSI), smoothening/filtering of CSI data, extraction of different statistical parameters and building a model to differentiate among two cases of body‐plus‐weapon and body only. Probability density functions of the variance of CSI features are computed under metal and non‐metal scenarios, that are non‐overlapping, which validate the effectiveness of proposed approach to separate metal and non‐metal scenarios. Furthermore, heatmap images are generated and a deep learning model is trained to automate the detection process. Our deep learning‐based automated detection methodology has achieved an overall accuracy of 90.5% and 87.5% on test samples, respectively, for the detection of a metallic plate and a real gun in indoor setting at 20 ft distance from the antenna.

This research aims to design a modified P&O algorithm for the efficient tracking of maximum power point (MPPT) for standalone and grid-connected systems. The proposed research work modifies the P&O algorithm for the dc-dc converter where the fixed step size P&O algorithm is translated into variable step size with the help of ant colony optimization (ACO) to generate optimal parameters for the PID controller to generate a variable step size in the P&O algorithm. This variable step size is dependent upon the error that is the difference between the generated power and desired power. By doing this it improves the efficiency of the P&O algorithm and its limitations are overcome. Furthermore, the PV is extended to connect with a grid where the inverter is controlled by a fuzzy logic controller (FLC) so that the combined structure of variable P&O and fuzzy helps to achieve MPP efficiently. The robustness of the proposed work is compared with other state-of-the-art controllers to justify the effectiveness of the proposed work. Finally, a stability test of the system is carried out to verify the overall stability of the power system.

Summary: Data recovery under collision of primary/secondary users in cognitive radio networks is largely an unaddressed problem. Existing approaches require either (a) retransmissions, (b) interference‐free alignment of transmission parameters, or (c) prior knowledge for data recovery. In contrast to the existing approaches, the paper proposes estimation of channel state information based on received collided frames and recommends techniques for spectrum sharing, collision detection, and data recovery. Specifically, based on estimated channel vector, interference‐resilient spectrum‐sharing protocol and techniques are proposed for (a) identification of primary user (PU) activity before (or during) secondary user's (SU's) transmission, (b) MIMO channel estimation under collision, (c) optimal pilot permutation, and (d) interference cancellation (with/without receiver diversity). Results are reported for different baseband modulation techniques under frequency selective/flat fading channel scenarios that demonstrate the efficacy of a proposed work.

Summary: This research work introduces some novel techniques for interference‐resilient OFDM wireless communication. Firstly, novel schemes for spatial multiplexing and interference cancelation based on signal subspace estimation are proposed. Secondly, the OFDM system is designed such that it meets three main objectives simultaneously, namely, (i) interference‐resiliency, (ii) throughput maximization, and (iii) energy consumption minimization. Interference‐cancelation techniques in prior art mostly consider maximization of throughput without any focus on energy consumption. On the other hand, the literature in energy minimization is limited to interference‐free environments. Most of the previous techniques also have a drawback that they cannot operate in smart or dynamically changing interference environments. The focus here is on interference‐resilient communication with equal attention to both throughput and energy‐efficiency maximization in dynamic hostile environments. The optimization problem is formulated, and then optimal policies and switching thresholds are found for throughput and energy‐efficient interference‐resilient communication. Methodologies for estimating the channel and jammer conditions and then adapting the transmission strategies accordingly are proposed. Moreover, to have an effective defense against smart jamming scenario, a constant‐payoff scheme is also introduced. Simulation results are compared with previous techniques that demonstrate the efficacy of proposed research. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, a reference time synchronization method [1] is adopted with introduced changed and improved fine and refined time estimation step. The communication system on which it is tested is MIMO OFDM based wireless local area network system. The WLAN standard adopted is of IEEE 802.11n, each data packet starts with a preamble, which consists of short as well as long training symbols. Simulation is implemented with C++ eclipse and tested for AWGN, Exponential Rayleigh Decaying and TGN fading channels. The plots generated by the simulation clearly show the improved performance of the introduced scheme