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This paper presents a two-element MIMO antenna system using two dual-beam EMSIW antenna elements. The dual-beam pattern is achieved by creating two weak field areas using EMSIW. The antenna elements are placed in the weak field areas of each other, which results in −31.65 dB measured isolation between the two adjacent antenna elements. Different scenarios for the placement of the elements are predicted, one case is illustrated in this paper. The high isolation, omission of decoupling networks, and use of dual-beam antenna element make the antenna simple, compact, and suitable for MIMO applications.

In this work, a switchable bandstop filter is used as a decoupling structure for developing a miniaturized reconfigurable multiple input multiple output (MIMO) antenna. Initially, a dual band ((2.43-2.60 GHz and 3.51-3.79 GHz)) single monopole antenna structure is developed on FR4 substrate. Then the single monopole antenna and its replica are accommodated in a small space with an edge to edge separation distance of 11 mm to form a 2 port MIMO antenna. Now, a switchable bandstop filter is used as a decoupling network between two closely spaced monopole antenna elements to prevent mutual coupling and reconfigure the antenna characteristics. The dual pole switchable bandstop filter is configured in such a way that one of its poles lies at 2.5 GHz in one state (Mode 1) and at 3.68 GHz in another state (Mode 2) under the switching action of two PIN diodes. Controlling the ON/OFF states of the PIN diodes in the bandstop filter, high isolation is achieved alternately in lower (2.43-2.60 GHz) and upper (3.51-3.79 GHz) frequency bands of the MIMO antenna. Also, stub network is used to improve impedance matching in the upper frequency band. The proposed isolation technique helps the antenna to yield high isolation (>30 dB), fair gain (>2.97 dBi), reasonable radiation efficiency (>86.8 %), low envelope correlation coefficient (< 0.16), high diversity gain (DG >9.88 dB), low Mean effective gain ratio (MEG 1/MEG 2 < 0.05 dB) and low channel capacity loss (CCL < 0.06 bits/s/Hz) for both the operating frequency bands. The overall dimension of the antenna is restricted to 44mm $\times $ 22mm ( $0.36\lambda _{o}\,\,\times \,\,0.18\lambda _{o}$ ) for its easy integration in compact wireless devices. This type of reconfigurable MIMO antenna is best suited for cognitive radio communication, which promotes efficient spectrum utilization.

The electric grid has already been transitioned towards a more flexible, intelligent, and interactive grid system, i.e., Smart Grid (SG) for load management, energy prediction, higher penetration of renewable energy generation, future planning, and operations. However, there is a huge gap between energy demand and supply due to the rise of different electric products and electric vehicles. Renewable Energy Harvesting (REH) plays a critical role in managing this demand response gap, where energy is generated from various renewable energy resources such as Solar PhotoVoltaic (SPV) and wind energy. Several research works exist in this regard. However, they have not yet been exploited fully. So, this paper proposed AI-RSREH approach, i.e., the AI-empowered Recommender System for REH in residential houses. The main goal of the proposed AI-RSREH approach is to predict energy generation based on SPV accurately, and this study aims to minimize the gap between the actual generation of energy and the predicted energy generation along with a recommender system for SPV installation. An exploratory residential house-wise data analytics is conducted for the demand response gap. AI-RSREH uses a stacked Long-Short Term Memory (LSTM) model to predict energy generation with a recommender system based on the energy generation prediction result. The obtained results show the efficacy of the proposed approach compared to the existing methods with respect to parameters such as SPV installation in residential houses and prediction accuracy.

A fully integrated GPS receiver system can enable unique system capabilities by synthesizing both receiver front end and baseband on the same chip, leading to lower area overhead and higher integration. A survey on the GPS receiver system that focuses on front end design is presented in the paper. The first section discusses the various global navigation satellite system (GNSS), followed by the GPS working section. Afterwards, the paper discusses the previous works on the GPS receiver front end design. It provides a detailed survey and classification of various receiver architecture, including the LNA, mixer, filter, and ADC topologies. Besides, several image rejection techniques are presented for more than 50 GPS receivers. The various performance parameters of the GPS receiver front end in the literature are presented with the graphical view in the state-of-the-art discussion section. This literature survey provides the most extensive compilation to date of the various topologies, techniques and explains their implementation in the GPS receiver system. A new figure of merit that includes all the system parameters is proposed. The FOM can be an excellent reference to enhance the research work in the field of GPS receivers. In the end, the paper describes the possible research scope and challenges associated with the design of the GPS receiver front end.

This work investigates the cumulative dose 60Co gamma ( $\gamma$ ) – ray irradiation effects on enhancement mode HEMT devices inheriting 3D – Electron and Hole Gases for dosimeter applications. The devices are irradiated through a 60Co source and demonstrate the enhancement in the drain current metrics. To elucidate, the said devices were irradiated through different mechanisms and the Compton effect was investigated through contour plots via TCAD simulations. The degradation of Schottky Gate contact and insulator charging affects the 2D – Hole Gas at the GaN cap and thereby affects the bottleneck at the 3DEG sheet. This significantly affects the OFF – state leakage components of the said device and can therefore be exploited for potential use in sensing and dosimeter applications. The leakage components can be exploited further to improve the linearity of the dosimeter by considering different grading profiles of the AlGaN layer. In this regard, a workflow for optimizing the sensitivity and linearity of the dosimeter through different graded profiles is also presented. Amongst all, gaussian graded profiles have been identified as the best – case scenario considering the sensitivity and exhibiting a linear operation.

Anomaly detection is an integral part of a number of surveillance applications. However, most of the existing anomaly detection models are statically trained on pre-recorded data from a single source, thus making multiple assumptions about the surrounding environment. As a result, their usefulness is limited to controlled scenarios. In this paper, we fuse information from live streams of audio and video data to detect anomalies in the captured environment. We train a deep learning-based teacher-student network using video, image, and audio information. The pre-trained visual network in the teacher model distills its information to the image and audio networks in the student model. Features from image and audio networks are combined and compressed using principal component analysis. Thus, the teacher-student network produces an image-audio-based light-weight joint representation of the data. The data dynamics are learned in a multivariate adaptive Gaussian mixture model. Empirical results from two audio-visual datasets demonstrate the effectiveness of joint representation over single modalities in the adaptive anomaly detection framework. The proposed framework outperforms the state-of-the-art methods by an average of 15.00 % and 14.52 % in AUC values for dataset 1 and dataset 2, respectively.