Electronics Research Institute

The effect of CuO on the structural, optical, dielectric, and radiation shielding properties of copper fluorobarioborate (CFBB) glasses synthesized by the melt-quench technique with a general composition xCuO–(50 − x)B2O₃–35BaO–15MgF₂ (0–0.5 mol%) was investigated. XRD measurements indicated that all the samples, except those with higher values of CuO, were amorphous in nature. FTIR studies exhibited stable BO₃, BO₄, and B–O–B units, indicating an insignificantly modified borate network. The UV–Vis spectral studies showed enhanced UV absorption with the increase in the concentration of CuO. The value of both the direct and indirect optical band gaps is found to be almost the same (3.48–3.60 eV). Dynamic measurements of dielectric properties showed strong frequency dispersion, characterized by decreased ε′ and ε′′ and conductivity behavior based on hopping mechanisms. The radiation shielding parameters show high attenuation at low photon energies and improved shielding for CFBB glasses with increased CuO content. Hence, CFBB glasses have sufficient promise for optical transparency coupled with stable dielectric behavior and effective γ-ray attenuation. The novelty of this study is in simultaneous investigations concerning optical clarity, dielectric stability, and shielding efficacy in low CuO-doped fluorobarioborates, emphasizing their potential usage in multiple ways due to their multifunctionality.

Generative artificial intelligence (GAI), a specialized branch of artificial intelligence, has developed as a dynamic discipline that drives innovation and creativity across several domains. It is concerned with creating models that can autonomously produce novel text, images, videos, music, 3D, code, and more. GAI is distinguished by its capacity to acquire knowledge from extensive datasets, identify recurring patterns, capture distributions, and produce novel content demonstrating similar features. This review presents a comprehensive historical overview of the development and progression of GAI techniques over the years. Various essential methodologies employed in developing GAI are also discussed, including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), transformers, and diffusion models. Moreover, a detailed overview of the technologies used in GAI is provided for generating images, videos, music, code, and text, such as ChatGPT, DALL-E, Midjourney, Claude, Bard, GitHub Copilot, and others. The research subsequently introduces the different datasets used to train the GAI models and the evaluation metrics for evaluating their performances. Ultimately, the research investigates the diverse applications of GAI across various domains, challenges, and ethical implications.

Wind energy is an effective renewable energy source. However, when it comes to harnessing its power because of its variability and nonlinearity, traditional controllers have limitations. This work proposes the design of two nonlinear maximum power point tracking (MPPT) methods to track the maximum power point for stochastic wind in the below-rated wind speed zone. These methods are the sliding mode controller (SMC) and the double integral sliding mode controller (DISMC). A benchmark model of a 4.8 MW wind turbine (WT) is subjected to random wind profiles in the MATLAB/Simulink environment. The equilibrium optimizer (EO) is used here and contrasted with particle swarm optimization (PSO) and grey wolf optimizer to achieve a good design of the controller in the sliding plane and change the switching control in sliding mode. The proposed optimization methodology and DISMC improved the smoothening of the control of angular speed, and specifically, the EO outperformed the rest of the techniques.

Dependency on software technologies or hybrid systems in computer vision systems lacks integrability on chips. It also suffers from insufficient throughput for real-time processing, which is critical for autonomous driving. This paper proposes a pure-hardware computer vision system based on the state-of-the-art YOLO algorithm, specifically LPYOLO. All the network operations as well as the essential post-processing are purely hardware implemented. To the author's knowledge, this is the first software-independent YOLO inference. The FINN framework is used to build the main intellectual property (IP) core of the network. The post-processing module (PPM) for the neural network's raw output is implemented using Verilog HDL. The system introduces an inference rate of about 67 fps on the ZCU102 FPGA board. It provides reliable results with mAP50 0.84 and mAP50-95 0.34 upon being trained on the car detection Kaggle dataset. The quantization bit precision is 4W4A. Moreover, the overall system consumed 16.7% from the ZCU102 LUTs.

A successive approximation register analog-to-digital converter (SAR ADC) is a promising approach used in biomedical applications due to its energy-efficiency architecture with less complex hardware implementation. The core building blocks of SAR ADC are sample-and-hold switch (S/H), comparator, logic control register, and digital-to-analog converter (DAC). To enhance the overall performance, a high-isolation CMOS bootstrap S/H switch has been used. The SFDR of proposed ADC has increased by up to 2.2 dB. Also, we propose a double-tail single-ended dynamic latch comparator with extra pair PMOS transistors that save power by up to 7.5% as compared to the traditional double-tail dynamic comparator. Moreover, after adding these pair of transistors into conventional double tail dynamic comparator without any calibration cost, the SNDR has increased by more than 2 dB and 0.3bit improvement of ENOB. Furthermore, a synchronous modified SAR logic control register based on low-power D flip-flops (FFs) is proposed. A metal-isolator-metal capacitor (MIM) with a modified capacitance reduction configuration is used to improve the active area of the capacitive DAC (CDAC) compared to the conventional CDAC with 36.7% saving power. The proposed ADC has been implemented using a 65-nm TSMC CMOS process, 1.2 V supply voltage with a sampling rate of 1 MS/s. An active area of 0.00585 mm2 with a total post-result power consumption of 5.75 µW has been accomplished for the proposed fully integrated ADC.

A lot of interest has been focused on carbon allotropes, especially graphene and reduced graphene oxide. This is due to their outstanding electrical and mechanical properties, which enable their use in many electronic applications. In this work, graphene and reduced graphene oxide (RGO) samples were prepared by different techniques. Graphene was prepared by a physical sputtering method, whereas RGO was prepared by the improved Hummer’s chemical method followed by chemical reduction. The prepared samples were characterized using Raman, X-ray diffraction, and energy-dispersive X-ray spectroscopy techniques. The graphene and RGO samples were qualified and compared to similar published works. A comparable quality factor value as high as 0.63 was obtained for the graphene compared to 0.09 for the RGO. The electrical conductivity of both graphene and RGO samples was also calculated from the I–V curves. A relatively high electrical conductivity of 153 S/cm was obtained for the graphene sample compared to 1.3 × 10–4 S/cm for the RGO sample. Graphene thin film shows higher electric conductivity relative to RGO thin film, which is in agreement with the proposed quality factor results.

In this paper, we present a novel scheme to train a deep neural network (DNN) for a cosecant-squared (csc2) radiation pattern synthesis speedily and accurately without relying on numerical optimization methods. Our approach is to train a DNN using a physics-informed loss function that minimizes the deviation between the desired shaped beam pattern and the actual one. The physics-informed deep neural network (PIDNN) makes array synthesis effective because it can efficiently achieve two desired features, namely, low sidelobe level (SLL) and small deviation (ripples) in the shaped beam region. To obtain a desired csc2 pattern with the SLL constrained, PIDNN optimizes the excitation amplitude and phase weights of the array elements. To illustrate the effectiveness of the proposed method, the beam pattern with specified characteristics is obtained for the same array by using genetic algorithm (GA) and particle swarm optimization (PSO).

The swift advancement of fifth-generation (5G) wireless technology brings forth a range of enhancements to address the increasing demand for data, the proliferation of smart devices, and the growth of the Internet of Things (IoT). This highly interconnected communication environment necessitates using multiple-input multiple-output (MIMO) systems to achieve adequate channel capacity. In this article, a 2-port MIMO system using two flipped parallel 1 × 2 arrays and a 2-port MIMO system using two opposite 1 × 4 arrays designed and fabricated antennas for 5G wireless communication in the sub-6 GHz band, are presented, overcoming the limitations of previous designs in gain, radiation efficiency and MIMO performance. The designed and fabricated single-element antenna features a circular microstrip patch design based on ROGER 5880 (RT5880) substrate, which has a thickness of 1.57 mm, a permittivity of 2.2, and a tangential loss of 0.0009. The 2-port MIMO of two 1 × 2 arrays and the 2-port MIMO of two 1 × 4 arrays have overall dimensions of 132 × 66 × 1.57 mm3 and 140 × 132 × 1.57 mm3, respectively. The MIMO of two 1 × 2 arrays and MIMO of two 1 × 4 arrays encompass maximum gains of 8.3 dBi and 10.9 dBi, respectively, with maximum radiation efficiency reaching 95% and 97.46%. High MIMO performance outcomes are observed for both the MIMO of two 1 × 2 arrays and the MIMO of two 1 × 4 arrays, with the channel capacity loss (CCL) ˂ 0.4 bit/s/Hz and ˂0.3 bit/s/Hz, respectively, an envelope correlation coefficient (ECC) ˂ 0.006 and ˂0.003, respectively, directivity gain (DG) about 10 dB, and a total active reflection coefficient (TARC) under −10 dB, ensuring impedance matching and effective mutual coupling among neighboring parameters, which confirms their effectiveness for 5G applications. The three fabricated antennas were experimentally tested and implemented using the MIMO Application Framework version 19.5 for 5G systems, demonstrating operational effectiveness in 5G applications.