Camera sensors often struggle to capture images in low-light conditions, leading to reduced brightness, contrast, and color fidelity, and increased noise that degrades the performance. Many methods have emerged for image enhancement but they often require slow processing and blur image, making them imperfect for real-world scenarios. This letter presents the first-ever Y2O3-based transmission gate memristor comparator-based median filter for on-sensor image enhancement in biomedical imaging systems, such as X-ray, computed tomography (CT), and magnetic resonance imaging, designed using Verilog-A. The current system performs front-end noise suppression directly at the sensor output stage, effectively removing salt-and-pepper noise that is introduced during signal acquisition from sensors. The denoised images were reconstructed in MATLAB, and performance was evaluated using quality assessment metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and mean absolute error (MAE). The proposed filter demonstrated superior performance compared to traditional methods, such as adaptive median filter, switch median, and threshold and weighted median filter, achieving PSNR values of 46.36 dB for brain CT and 43.84 dB for COVID-19 X-ray, alongside reduced MSE and MAE values of 1.5 and 29.53 for brain CT and 2.67 and 43.84 for COVID-19 X-ray, respectively. The findings indicate the potential of memristor-based filters for next-generation biomedical sensors.

A comprehensive physical electro-thermal modeling approach is explored to investigate the intricate mechanisms underlying filament formation and the effect of surface perturbation in nanoscale Y 2 O 3 -based memristors. The approach integrates fundamental principles of solid-state physics, electrochemistry, and materials science to develop a detailed physical model that captures the key phenomena governing the operation of Y 2 O 3 memristors. The simulation is carried out in a semiconductor physics-based tool, i.e., COMSOL Multiphysics with a defined MATLAB script, wherein simulation is based on the minimum free energy of the used materials at an applied input voltage. The fundamental processes in filament growth include ion migration, redox reactions, and vacancy dynamics within the Y 2 O 3 lattice. Furthermore, the influence of surface perturbation on the overall device behavior, grain boundaries, and electrode interactions impact on memristor performance is also investigated. The surface perturbations significantly influenced the switching dynamics of the memristor, including variations in switching voltages, ON/OFF current ratio, filament radius, and filament temperature during the switching process. Therefore, the presented findings contribute to a deeper understanding of the physical mechanisms at play in Y 2 O 3 memristors, offering valuable guidance for the design and engineering of these nanoscale devices for next-generation memory and neuromorphic computing applications. This physical modeling approach not only enhances our comprehension of memristor behavior but also paves the way for the development of more efficient and reliable memristor-based technologies.

Abstract This paper presents a new optimal decentralized PIcontroller design based on a modified hybrid whale optimization approach that is addressed for a typical industrial benchmark process control device. This research work determines the gain parameters of the PID controllers using the modified whale optimization algorithm (mWOA), aiming to achieve efficient performance by taking into account the integral time absolute error (ITAE) cost function. Further, the stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainty. A comparison has beenmade between the performance of the proposed decentralized control method and a traditional decentralized PID controllerarchitecture. The obtained results highlight that the proposedcontroller exhibits superior control performance in inevitablepractical scenarios, such as in the face of additive stochasticuncertainties and load perturbation.

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648% improvement in Received Signal Strength Indicator (RSSI), a 32% reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5% increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

Miniaturized, specific, rapid response and economical biosensors are finding applications in biotechnology, environmental studies, agriculture, food inspection and safety, disease diagnosis and medical utilities. Of the many categories of biosensors, optical biosensors have brought about an extra edge in sensing applications due to their selective, rapid and extremely sensitive measurements. Biosensors are analytical tools used to detect specific analytes such as cholesterol, urea, etc. having biomolecules such as nucleic acids, proteins, carbohydrates as key element for detecting these analytes along with a transducer and a data analysis and visualization tool. In case of optical biosensors the analyte is detected using light with either label based or label free techniques. In this paper some of the marked advances in the last decade in the field of optical biosensors have been reviewed with an emphasis on their fabrication approaches and growing application areas. Along with some of the carefully selected article on new developments in optical biosensors through the last decade, a brief historical review of optical biosensors since the breakthrough in optical biosensors in 1970s has also been presented. Another focus of the current review is the classification of biosensors, typical structures along with emerging developments in optical biosensing that are likely to impact the current decade. Major application areas and emerging applications through the last decade have been outlined to present a clear picture on the versatility of optical biosensors. Finally, the review also considers the challenges and future of emerging optical biosensing technologies in the current decade.

In order to keep aquatic ecosystems safe and healthy, it is imperative that cleaning be done frequently. This research suggests the use of autonomous underwater rovers for effective underwater cleaning as a novel approach to this issue. The enhanced sensing and navigational capabilities of the autonomous rovers enable them to independently navigate underwater environments and find and remove underwater garbage and uneaten fish feed which can be recycled. The suggested solution not only does away with the requirement for human divers, but also provides a more effective and affordable technique for underwater cleaning. The paper also examines the creation, testing, and potential of the autonomous underwater rovers.

Autonomous robots can help people explore parts of the ocean that would be hard or impossible to get to otherwise. The increase in the availability of low-cost components has made it possible to innovate, design, and implement new and innovative ideas for underwater robotics. Cost-effective and open solutions that are available today can be used to replace expensive robot systems. The prototype of an autonomous robot system that functions in brackish waterways in settings such as fish hatcheries is presented in this research. The system has low-cost ultrasonic sensors that use a SLAM algorithm to map and move through the environment. When compared to previous studies that used Lidar sensors, this system's configuration was chosen to keep costs down. A comparison is shown between ultrasonic and lidar sensors, showing their respective pros and cons.

Transition metal oxides play a very important role to develop the memristive crossbar array for nonvolatile memory for storage and logic operations. However, the development of a high-density memristive crossbar array for complex applications is restricted due to low device yield and high device-to-device (D2D) and cycle-to-cycle (C2C) variability in device switching voltages. Here, we report the fabrication of a stable, highly scalable, reproducible, Y2O3-based memristive crossbar array of (15 x 12) on silicon by utilizing a dual ion beam sputtering system. The fabricated crossbar array exhibits the intrinsic nonlinear characteristics of the memristive element by displaying a high endurance (similar to 7 X 10(5)cycles), high current ratio (>200), good retention (similar to 1.5 X 10(5) s), high device yield, low device-to-device (D2D) (0.25), and cycle-to-cycle (C2C) (0.608) variability in the SET/RESET voltages of the memristive device, which can be further suitable for analog computation and logic operations.

Speech feature extraction, being the most important step, plays the utmost role in any automatic speech recognition system. In any speech feature extraction technique, the emphasis is on getting more and more accurate and robust features. After decades of research in automatic speaker recognition, there have been several new algorithms and advances, however, there still remains a number of challenges largely due to inconsistencies in speaker's vocal tract over a period of time and health, changing ambience and variations in the performance of speech recording systems etc. Mel frequency cepstral coefficients (MFCC) is an extensively used method in automatic speaker recognition systems. The triangular mel weighing function is a key component of the MFCC feature extraction technique. In this paper various existing modifications in the mel weighing function have been presented and sinc function based mel weighing function has been proposed. The traditional triangular weighing function based as well as Gaussian weighing function based MFCC technique have been evaluated on the TSP speech database. This database contains 11 male and 12 female speakers having utterance of varying lengths. A distortion measure between extracted features established on the least Euclidean distance was utilized for speaker recognition. The success rate of this speaker recognition investigation was estimated to be 92 % in case of traditional triangular mel weighing function and was found to be 96 % in case of Gaussian mel weighing function for the speech segments of 2 seconds.

A rapid rise in the complexity of designs and shrinking device dimensions has recently led to multimillion gate systems run with numerous asynchronous clocks. Traditional digital design testing and verification methods in many cases are unable to detect clock domain crossing (CDC) issues. At structural and functional level, existing methods provide an ad hoc partial verification which are erroneous and take huge time to analyze. If these source of potential error are not addressed and verified early in the design cycle, then the designs may go to silicon with functional errors. Detection of these errors in post-silicon verification is very costly in terms of time and money. Automatic tools can warrant that these multi-clock designs are corrected prior to final tape out of design. But, these tools give errors in the range of thousands if not used properly. Analysis of these reports can be very time-consuming as the number of false errors and warnings (noise) is generally huge. Manual CDC verification techniques only work well for verification but not for detection of issues. In this paper, the key optimization techniques have been reviewed which when used with the automatic tools will reduce the no of noise in analysis report. Reduction in false issues speed up verification cycle thus saving the overall cost.

The idea of reducing the gate length of the metal-oxide semiconductor field-effect transistor (MOSFET) has been the leading stimulus for the growth of the integrated circuit industry. This chapter presents the current CMOS technology and its future advancements. The scaling concept and technology advancements in VLSI are discussed. Additionally, the transistor models and associated device modeling approaches are included. Herein, we discussed various aspects of CMOS integration into analog circuits for various architectures implementation and their desired improvements to work for futuristic 5G applications. The basic MOSFET device structure and modeling of MOSFETs for different signals and on different platforms considering various parasitics effects have been presented to make a way for the advanced research understanding of different works of modeling and proposed architectures in terms of simulation for their implementation for 5G. We expect that the future of CMOS with 5G will accompany different solutions arising with scaling issues and also provide various alternatives to make smooth incorporation of 5G into the wireless world.

From the last decade, the development of a generic model for memristive systems which simulates the biologically inspired nervous system of living beings, is one of the most attracting aspects. More specifically, the develop generic model has capability to resolve the problems in the field of artificial neural network. Here, a generic, non-linear analytical memristive model, which is based on interfacial switching mechanism, has been discussed. The proposed model has the capability to simulate the high-density neural network of biological synapses that regulates the communication efficacy among neurons and can implement the learning capability of the neurons. Further, the proposed model is the parallel connection of the rectifier and memristor which shows better non-linear profile along with non-ideal effects and rectifying nature in its pinched hysteresis loop in the resistive switching characteristics. Moreover, proposed model shows the significant low value of maximum error deviation ∼4.44% for Y2O3-based and ∼4.5% for WO3-based memristive systems, respectively in its neuromorphic characteristics with respect to the corresponding experimental results. Therefore, the proposed analytical memristive model can be utilized to develop the memristive system for real-world applications based on neuromorphic behaviors of any transition metal oxide-based memristive systems.

In this chapter, the requirements and characteristics of the 5G network are discussed at an introductory level. 5G wireless networks operate at much higher frequencies (30–300 GHz) than their 4G predecessor networks. Higher data rates and density, lower latency (~ 1 ms) are some of the essential requirements of a 5G network. 5G communication network has three critical scenarios, such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), Massive Machine Type Communications (MMTC), which constitute the bulk of requirement of a 5G network. A large number of stakeholders are critical issues with the 5G requirements. Lack of cooperation and regulation between stakeholders can lead to a situation of incoherent and paradoxical requirements for any system. There is no single technology that can fulfill the needs of all requirements of different stakeholders. Techniques like beamforming and software-defined networking can achieve such diverse requirements. Effective thermal management is needed to minimize the variations in the output of processing units and power amplifiers, which are used in a 5G network. Thermal management becomes critical at high frequencies as thermal conductivity and thermal coefficient of the dielectric constant of the substrate changes vary rapidly with temperature. The introduction of an active thermal management system reduces dispersion along interconnect due to variations in the dielectric constant. Overall 5G network poses a very complicated set of requirements to circuit and infrastructure designers.

In this chapter, we study factors that dominate the interfacial resistive switching (RS) in memristive devices. We have also given the basic understanding of different type of RS devices which are predominantly interfacial in nature. In case of resistive random access memory (RRAM), the effect of surface properties on the bulk cannot be neglected as thickness of the film is generally below 100 nm. Surface properties are effected by redox reactions, interfacial layer formation, and presence of tunneling barrier. Surface morphology affects the band structure in the vicinity of interface, which in turn effects the movements of charge carriers. The effect of grain boundaries (GBs) and grain surfaces (GSs) on RS have also been discussed. The concentration of vacancies (Ov)/traps/defects is comparatively higher at GBs which leads to leakage current flow through the GBs predominantly. Such huge presence of charge carriers causes current flow through grain boundaries.

A chemiresistive carbon monoxide (CO) gas sensor comprising of an organo-di-benzoic acidified zinc oxide (ODBA-ZnO) nanohybrid material is reported. The ODBA-ZnO hybrid material is prepared via a single-pot hydrothermal method. The electrical resistance of the drop-casted ODBA-ZnO film on interdigitated electrodes increases noticeably upon exposure to CO (5-500 ppm). The resistance increase is attributed to the formation of complex ions at the organic (ODBA)-inorganic (ZnO) interface in the presence of CO. The detailed CO sensing properties of the ODBA-ZnO nanohybrids reveal a remarkable selectivity to CO gas in comparison to other gases like CO2, H2S, and NH3 at 125 degrees C. The maximum response to 100 ppm of CO is observed to be 35% with the achieved selectivity to CO being 88%, which is the best reported CO selectivity result available in the literature to date. The ODBA-ZnO nanohybrid sensor takes nearly 91 s to reach the saturated response to 100 ppm of CO and nearly 175 s to recover from it in a synthetic air environment. A systematic study using field emission scanning electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, nitrogen adsorption-desorption tests, and thermogravimetric analysis reveals that introduction of an organic moiety (ODBA) to ZnO played a key role in achieving improved selectivity and sensitivity toward CO. The present work provides a simple route for fabricating the ODBA-ZnO sensor to achieve better selectivity and sensitivity to CO gas at a relatively low temperature (125 degrees C).

In this article, the effect of the SiO2 layer is demonstrated on dual ion beam sputtered yttria-based memristive devices for the first time. It is found that the effect of thickening of SiO2 layer is extremely detrimental for resistive switching (RS) parameters such as endurance and uniformity of current-voltage characteristics. SiO2 interfacial layer causes the growth of nano stalagmite in the yttria layer. This interfacial layer is also responsible for the origin of pseudo bipolarity in RS characteristics and early failure of the device during endurance testing. The thickness of the SiO2 layer has a positive correlation with deposition temperature and oxygen partial pressure. It is found that the deposition temperature of 300°C and a mixture of Ar:O2 with a ratio of 2:3 shows the best RS characteristics.

I. Abstract Present study explores on the development of low molecular weight π-conjugated organic amine and metal oxide based nanohybrids for room temperature carbon dioxide (CO2) sensing application rather than using conventional polymer of organo-amines [1-4]. In this study, in-situ growth of naphthalene based π-conjugated amine (NBA) and zinc oxide (ZnO) nanohybrids thin-film were grown and their room temperature CO2 sensing performance is investigated. II. Experimental In a typical experimental procedure, equimolar Zn(NO3)2∙6H2O and hexamethylenetetramine (HMTA) were added in a 20 mL of deionized water under constant stirring for 15 min at 65 °C, then NBA (10 mg) was added slowly in the solution under vigorous stirring until the solution becomes uniform reddish color. After that, the solution was transferred into a Teflon lined autoclave where the flexible substrate was placed vertically in the solution and kept at 95 °C for 3 h reaction time. III. Results and discussion Figure 1(a) shows FESEM image of hexagonal NBA-ZnO nanohybrids. EDX pattern of the NBA-ZnO nanohybrids reveals the presence of elemental C, N, Zn, and O. The sensor response to 500 ppm, 1000 ppm, 2000 ppm, 5000, and 10000 ppm CO2 were 9.1, 14.2, 21.4, 30.3, and 36.6, respectively (Figure 1(b)). The sensor response to 23%, 43%, 62%, and 85% RH was 29.9%, 29.2%, 29.4%, and 29.2%, respectively. The response changes were negligible with the increase in the relative humidity, which can be attributed to the hydrophobic nature of NBA (due to the presence of hydrophobic phenyl and naphthalene rings). During hydrothermal synthesis of NBA-ZnO nanohybrids, in the beginning, hexagonal zinc hydroxide nuclei forms from the reaction of reagents (Zn(NO3)2∙6H2O and HMTA) in the solution with the help of temperature treatment at 65 °C and constant stirring. After adding NBA in the solution, the carboxylic groups of NBA create covalent bonds with zinc hydroxide to give structural stability to the nanohybrid architecture. At the same time, the hexagonal zinc hydroxide nuclei get transformed into the nuclei of inorganic-hydroxide phases in the lamellar NBA-ZnO hybrid (Figure 1(c)). Acknowledgment Biswajit Mandal grateful to MeitY for providing fellowship under Visvesvaraya PhD scheme for Electronics and IT. Prof. Shaibal Mukherjee is thankful to MeitY for Young Faculty Research Fellowship (YFRF) under Visvesvaraya PhD scheme for Electronics and IT. This publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya PhD scheme of Ministry of Electronics & Information Technology, being implemented by Digital India Corporation. Authors are thankful to FESEM and EDX facilities, which are integral parts of Sophisticated Instrument Centre (SIC) of IIT Indore. References [1] R. Zhou, D. Schmeisser, and W. Göpel, “Mass sensitive detection of carbon dioxide by amino group-functionalized polymers,” Sensors Actuators B Chem., vol. 33, no. 1–3, pp. 188–193, Jul. 1996. [2] M. S. Nieuwenhuizen and A. J. Nederlof, “A SAW gas sensor for carbon dioxide and water. Preliminary experiments,” Sensors Actuators B Chem., vol. 2, no. 2, pp. 97–101, May 1990. [3] B. Sun, G. Xie, Y. Jiang, and X. Li, “Comparative CO2-Sensing Characteristic Studies of PEI and PEI/Starch Thin Film Sensors,” Energy Procedia, vol. 12, pp. 726–732, Jan. 2011. [4] R. Zhou, S. Vaihinger, K. E. Geckeler, and W. Göpel, “Reliable CO2 sensors with silicon-based polymers on quartz microbalance transducers,” Sensors Actuators B Chem., vol. 19, no. 1–3, pp. 415–420, Apr. 1994. Figure 1

Multiple quantum wells (MQWs) of CdZnO/ZnO are realized, for the first time, by dual ion beam sputtering (DIBS) system at different deposition conditions in terms of ion beam power, substrate temperature, and time cessation between deposition of successive layers. The effects of DIBS deposition conditions are analyzed by secondary ion mass spectroscopy (SIMS) and high-resolution transmission electron microscopy (HRTEM) and discussed systematically. The SIMS analysis has been used for depth profiling of CdZnO/ZnO-based MQWs structure. The deposition of CdZnO/ZnO-based MQW structure performed at 100 °C with time cessation of 30 min between successive layer growth and ion beam power of 14 W has displayed the best results in terms of distinct well and barrier layers formation. This work also includes an analytical study of CdZnO/ZnO-based MQW solar cell (MQWSC), in which a study is performed for solar irradiance dependence of performance parameters to explore the potential use of CdZnO/ZnO-based MQWSC for concentrator solar cell (SC). The short-circuit current density increases from 0.12 to 57.98 mA/cm2, the open-circuit voltage rises from 2.60 to 2.77 V, and the photon conversion efficiency is from 2.85% to 3.04%, as solar irradiance increases from 0.1 to 50 suns. The results show that the performance of SCs can be improved by using concentrators and also explore the possibility of efficiently absorbing short-wavelength photons.