Proceedings of Third International Conference on Computational Electronics for Wireless Communications

In several applications where exact computation is not essential, approximate computing is a developing paradigm for sacrificing computing accuracy to save energy and simplify design complexity. This brief presents a low-power, low-area approximate multiplier with a new compressor design. The proposed multiplier handles the component that is least crucial as a fixed compensating term. The second half features a carefully considered hardware–accuracy trade-off that is tremendously powerful. Efficiency is provided. The proposed multiplier is simulated using CADENCE with 18 nm FinFET technology. Compared to the current approximation designs, the proposed design 1 significantly reduces transistor count by 2.05% and power on average by 1.9%, and the proposed design 2 significantly reduces power on average by 3.9% and transistor count by 9.51%. As a result, these designs can be an efficient replacement for exact multipliers in real-world applications that are error-tolerant.

The ability to identify walking conditions correctly is essential for both diagnosing and treating gait abnormalities. This study utilized machine learning algorithms to analyze multivariate gait data obtained from 10 healthy participants walking under three distinct settings, including normal treadmill walking while wearing an ankle brace on the right. In order to increase the precision and efficiency of the ML models, the authors adopted a pipeline technique to handle the data. This research unveils the preeminence of random forest, achieving an impressive (92%) accuracy, surpassing logistic regression, neural network, naive Bayes, and perceptron. It exemplifies the formidable potential of machine learning algorithms for gait classification. The application of the results may be restricted to the particular dataset and walking conditions employed in the study, and the proposed study’s limitations include the use of a constrained number of algorithms and hyperparameter tuning settings. The proposed study has implications for the design of diagnostic tools and assistive devices for people with gait abnormalities and emphasizes the value of paying close attention to hyperparameters and other important model parameters to achieve the highest level of accuracy and performance in machine learning models. Future studies might build on this strategy by utilizing more datasets, additional algorithms, and sophisticated optimization methods to boost the precision of gait categorization.

Moving from lower RF range to high frequency range is highly required technology because of the massive rise in required non-wired transmission subscribers and high data rate required per customer. By providing capacity, simple design and low cost, RoF system has been acknowledged as the foundational technology for 6G networks. This paper presents variation in optical RF power and RF power-to-noise power ratio (SNR) by varying the optical amplifier gain from 20 to 40 dB and fiber distance from 5 to 25 km. The result shows that optical RF signal power decreases with the decrement in the optical amplifier gain and increment in the optical fiber length. Similarly, RF signal power-to-noise power ratio gradually increases with increment in optical amplifier gain and decrement in fiber length.

Speech coding is a widely used technique used for digital telephony and secure communication. The estimation of linear prediction coefficients (LPCs) for the development of synthetic speech is crucial and involves the use of the Levinson–Durbin (LD) algorithm. The computational complexity introduced by this algorithm affects the performance of speech codecs utilizing LPCs. In this paper, an FPGA implementation of the Levinson–Durbin algorithm is proposed for efficient autoregressive model parameter estimation. By harnessing FPGA’s parallel processing capabilities, the algorithm’s performance is accelerated, allowing real-time processing of signals. The work focuses on translating the algorithmic steps into hardware modules, optimizing memory access, and evaluating resource utilization, and power efficiency of the implemented hardware. This research contributes to the field of hardware-accelerated algorithms, showcasing the potential of FPGA platforms in enhancing signal processing tasks.

This paper develops a four space-time dimensional finite element approach for approximately calculating the electromagnetic and Dirac fields within a 3-D cavity resonator taking into account the interaction between the two fields. The idea is to partition the four-dimensional space-time region consisting of the 3-D cavity and the finite time duration over which the fields are to be determined into four-dimensional simplices. Each simplex is defined by five vertex points in four-dimensional space-time and the fields within each such simplex are expressed as affine linear functions of the four simplex coordinates so that the values of the field at the vertices of the simplex coincides with the field value at that vertex. We then substitute these affine linear functions into the action functional of the electromagnetic field interacting with the Dirac field thereby yielding a quadratic function of the simplex vertex field values corresponding to the free field action plus cubic function of the vertex fields corresponding to the interaction action between the Dirac current and the electromagnetic field. By summing up this action over all the simplices into which we have partitioned the space-time region, we obtain a quadratic-cubic function of the field values at all the vertices of the different simplices. The problem of approximately calculating the fields within the cavity then amounts to minimizing this function of a finite number of complex variables corresponding to the field values at the vertices of the simplices. This optimization can be carried out using for example a gradient search algorithm or using perturbation theory for obtaining solutions to a system of nonlinear algebraic equations when the nonlinearity is small.

In this paper, we model the sea of electrons and holes within a semiconductor using the second quantized electron-positron Dirac wave operator field interacting with an applied classical electromagnetic field, a quantum photon field which can be regarded as quantum fluctuations of the electromagnetic field, and a noisy quantum photon field coming from the bath outside the semiconductor. Essentially, in this model, the Dirac wave field interacts and evolves along with the quantum photon field in the given background classical electromagnetic field and the given quantum noisy operator electromagnetic field.

Higher education institutions must modify existing English curricula to fit the needs of today’s learners who grew up with the Internet. This entails raising consciousness and making use of the advantages offered by technology and resources from the Internet+ era. Positive changes have resulted from the present-day college English teaching approach, renewing guidance, modernizing it, and improving effectiveness. In higher education, assessing English instructors is essential since both instructors and students value passing required English exams. Computer-based remote instruction has increased online learning, advancing digitalization and lifetime learning. This article discusses problems with college English instruction, including low efficacy, uneven competency, and a mismatch between grading and instruction.

This research letter describes the optimization of omnidirectional corrugated miniaturized planar inverted-F antenna (PIFA) operating at 401.95 MHz for Microsat. To examine the impacts on the resonant frequency and miniaturization, certain antenna parameters are examined by comparing the reference antenna and the proposed antenna. The PIFA has a very tiny volume of 68 × 68 × 31 mm3 or 0.009λ × 0.009λ × 0.004λ mm3. The antenna layout shows significant miniaturization, i.e., 20% of the reference antenna. The proposed PIFA antenna generates an omnidirectional radiation pattern for Microsat applications.

The Metal Oxide Semiconductor Field Effect Transistor (MOSFET) has been used in integrated circuits (ICs), which are based on MOS technology. So, as we scale below 22 nm, MOSFET has some limitations in terms of speed, power and area but as per the recent requirements of low-power devices force us to look for enhancement of MOSFET. In this paper, Germanium (Ge)-based double gate Tunnel Field Effect Transistor is used over the silicon-based TFET and its uses in low power consumption devices are discussed. Germanium (Ge)-source-based TFET is used to improve the drive current and to get a very good sub-threshold slope. Various hetero-dielectric oxides are also investigated in this project. A simple Biosensor is also designed to sense the Biomolecule present in the given substance/DNA or any other substance.

This paper presents the integrated architecture of carrier-sense multiple access with collision avoidance (CSMA/CA) protocol with Advanced Encryption Standard-128 (AES-128) to securely transmit 128-bits of data over wireless networks. CSMA/CA is a type of CSMA protocol used in the media access control (MAC) layer of data link layer (DLL) for traffic checking in wireless channel before transmitting the data through it. AES encrypts the data before its transmission to maintain its confidentiality and integrity.

Image enhancement has a very crucial role in upgrading the visual quality and interpretability of digital images. In this study, we emphasize on the application of the Butterworth filter based on two different design techniques, namely analog filter design and pole-zero placement method for enhancing the MRI images. Several evaluation metrics are employed, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), for evaluating the performance of the Butterworth filter. These metrics provide quantitative measures of the image enhancement quality and allow for comparison with other enhancement techniques. The Butterworth filter’s parameters, such as the cut-off frequency and filter order, are carefully selected to achieve optimal enhancement results. Experimental results demonstrate the effectiveness of the Butterworth filter in enhancing images. The filter’s ability to selectively enhance desired frequency components while preserving important image details leads to improved image quality and visual perception. The evaluation metrics consistently show significant improvements in terms of image quality, contrast enhancement, and preservation of image details. The outcomes of this research aid in the comprehension of the Butterworth filter's capabilities and its potential applications in image enhancement tasks. The proposed method can be applied in various domains, including medical imaging, remote sensing, and computer vision, to improve image quality, facilitate analysis, and aid in decision-making processes.

In this paper, we introduce and evaluate two novel methodologies for the efficient implementation of polar decoders. Polar decoders are integral components within contemporary 5G communication systems, specifically for error correction in control channels. Although polar codes offer significant advantages, their implementation often poses computational challenges, potentially impacting latency and throughput. To overcome these challenges, we present two innovative decoder designs: a component code-based approach and a specialized node-based design that identifies distinct bit patterns to mitigate decoding complexity. The 16-bit polar decoder, realized through our proposed designs, is synthesized and precisely simulated using Cadence Suite with TSMC 65nm CMOS process. Our findings highlight remarkable enhancements in latency, area, throughput, and power efficiency, rendering them highly suitable for cutting-edge communication systems. Notably, the specialized node-based decoder emerges as the top performer, exhibiting a minimal latency of 0.989 ns and an impressive throughput of 16.17 Gbps, all while occupying a compact area of 787.68 um2 and consuming a power of 0.024 mW. The component code-based decoder also excels with a latency of 1.192 ns, a throughput of 13.42 Gbps, an area footprint of 1292.4 um2, and a power consumption of 0.109 mW. These outcomes underscore the viability of our approaches for efficient polar decoding in advanced communication systems. Furthermore, these methodologies hold the potential to be scaled for higher order polar decoders, offering promising avenues for future research and application.

Many falls happen each year which cause severe medical issues. An early detection could allow the injured toddler to receive medical care right away. Deep learning techniques like long short-term memory (LSTM) are proving to be more efficient than the use of sensors to detect the fall. These new techniques can right away detect the fall and inform the concerned ones in time. We employed a camera-based system along with human pose estimation by using OpenPifPaf to extract the keypoints (elbows, shoulders, knees, etc.) of the toddler, these keypoints are passed on to the LSTM model for classification. The LSTM-based neural network predicts the falls into four classes: ‘Normal’, ‘Fall Warning’, ‘No Fall’, and ‘Fall’. To train this deep learning model, we used the UP-Fall Detection dataset, which consists of videos of various activities. This paper aims to reduce the number of fatal injuries in toddlers by using a deep learning real-time fall detection system that will notify the parents and caregivers as soon as a fall occurs so that the toddler can receive medical care immediately if necessary.

For early-stage tumor detection, microwave imaging is a widely appreciated modality as of now. Development of an efficient microstrip patch antenna with reduced cost and complexity is still a challenging task that must be addressed. In this work, we suggest a low-profile partial ground plane dual band rectangular patch antenna for identifying the presence of early-stage tumor in the muscular region of breast mass. The proposed antenna model is operating on two different frequency bands, i.e., 2.48 and 3.46 GHz. The model is simulated with a partial ground plane and modified feedline with specially designed defected radiating patch. It is optimized by considering the efficiency of the antenna by evaluating different parameters, such as: return loss (S11), radiation pattern, VSWR, and radiation efficiency. Further, the antenna model is experimented using a simulated four-layered breast phantom model with an early stage of muscular breast tumor in side. The detection of tumor is claimed based on the difference in observed antenna parameters compared to those in the absence of tumor inside the breast mass.

In this research, a low-profile metamaterial-inspired dual-band antenna is proposed for 5G communication which covers n78 band of Laptop and Mobile Terminal applications and 5G IOT Wireless Avionics Intra Communication (WAIC) applications. The designed antenna consists of rectangular Split Ring Resonator (SRR) as radiating element and partial rectangular ground plane with defected ground structure (DGS) is achieved by Complementary Split Ring Resonator (CSRR). It has been designed on Rogers RO4350 substrate which has a thickness of 0.8 mm, with the relative dielectric constant value Ɛr = 3.66 and with the loss tangent value tan δ = 0.004. The results indicate that metamaterial structure improves the gain, bandwidth and radiation efficiency of the antenna. The simulated 10 dB impedance bandwidth covers 5G-n78 band with the center frequency of 3.5 GHz and WAIC band with the center frequency of 4.3 GHz. The proposed antenna operates with high radiation efficiency, better gain and covered required bandwidth with linear polarisation. A reduced dimension of 0.25λ0 × 0.35λ0 × 0.009λ0 is achieved.

This paper presents a design of a low-profile, small, compact, flexible, bio-compatible microstrip fed AMC based Anti-symmetric dual L-shaped antenna that produces resonances at 2.44 GHz (ISM band) and 5.9 GHz for WBAN application. To make the proposed antenna bio-compatible, the antenna is incorporated with a 3 × 3 Artificial Magnetic Conductor array (AMC) that serves the purpose of Front to Back ratio (FBR) to increase and thus enhance the antenna parameters when kept in contact with the living human tissues. The proposed antenna and the AMC are designed on a Polyimide substrate (PI) to make it flexible. The antenna has a volume of 0.28 $$\lambda_{0}$$ λ 0  × 0.26 $$\lambda_{0} $$ λ 0  × 0.013 $$\lambda_{0} \;{\text{mm}}^{2} $$ λ 0 mm 2 and that of the antenna with AMC surface is 0.612 $$\lambda_{0}$$ λ 0  × 0.612 $$\lambda_{0}$$ λ 0  × 0.050 $$\lambda_{0}$$ λ 0 $${\text{mm}}^{2}$$ mm 2 . From the simulation of the proposed Anti-symmetric dual L-shaped antenna with AMC, the result indicates the improved parameters of the antenna gain is 5.87 dB and 5.34 dB, bandwidth of 260 MHz and 1.06 GHz, Front to Back ratio is 58.8 dB and 37.25 dB with a radiation efficiency of 98.4% and 98.7% in 2.44 GHz and 5.9 GHz respectively. The proposed antenna having flexibility, improved gain and improved FBR makes it good for wearable antenna applications.

In the present work, the design of a C-band filtenna array is designed using computer simulation technique (CST) using filtenna and power divider is presented. The radiating element consists of a microstrip patch antenna, the filtering function is carried out by a compact microstrip resonant cell (CMRC) which is acted as a band-pass filter (BPF) integrated on the antenna panel. An effective technique for the rejection of spurious resonances is also described which is achieved by removing the corresponding harmonics of the radiating element and filter.

Approximate parallel prefix adders are a type of circuit that can perform addition operations on binary numbers with high speed, low area, and less power. These circuits are designed to provide approximate results, which means that they sacrifice accuracy for efficiency. This trade-off makes them ideal for use in applications where speed is more important than precision. In recent years, there has been a growing interest in the improvement of these circuits, as they have the potential to revolutionize the field of digital signal processing. Kogge–Stone (KS) adder is a kind of parallel prefix adder that has the strong point of quickest addition primarily based on design time. Approximation Kogge–Stone (AxKS) adder is a variant of basic Kogge–Stone Adder that introduces a level of approximation in its operation. In this paper, a new approximate KS Adder is proposed with high-speed performance and less area. The KS Adder and its AxKS Adder are compared for bits 8, 16, 32 using software tool Xilinx Vivado.

Reliable data transfer is a tedious task for various applications that involve users in motion. In vehicular communication systems, for reliable and seamless data transfer an accurate estimation strategy is needed. The principal objective involves construction of the predictive models that can estimate error probabilities on parameters like signal strengths, timestamps, and sender identification outcomes. To establish a proper strategy for vehicle scenarios while maintaining the system’s effectiveness, we present an analysis of our system with error probabilities, exploring the relationship between signal strength, neural network (NN) architectures, and error prediction accuracy. Data generated is used to simulate communication messages. Furthermore, this paper boards to calculate the effectiveness of different NN architectures, including single and multi-layer perceptrons, interpreting key relations embedded in data and providing precise error probability predictions. Through a series of insights, we observed signal strength and NN impact on error predictions. Our analysis improves the complex error pattern occurrences and identifies the most suitable architecture for accurate predictions.

Flexible electronic (FE) systems have been at the forefront of rapidly transforming and fast-growing e-industries. With upcoming enhanced technologies, rigid devices are now been replaced with emerging ultra-thin, flexible, and portable systems. The hardwiring in on-boards is also getting updated with stretchable interconnect. The stretchable interconnects are becoming one of the most eminent parts of FE systems and need high research attention. Correspondingly, to attain efficient stretchable interconnects performance analyses such as its varying structures, stretchability, parasitic impedance extraction, power, and delay are computed in this work. Five different stretchable interconnect structures viz, straight (St), zigzag (Zz), serpentine (Sp), horseshoe (Hs), and rectangular (Rt) have been considered. The different interconnect geometries are parameterized to varying strain and stress effects on ANSYS workbench. For all the considered geometries, their RLC parasitics are extracted using ANSYS Q3D tool. Further, the signal integrity and performance of all the stretchable interconnect structures have been analyzed at varying frequencies using Cadence Virtuoso EDA tool.

This paper introduces a slotted antenna configuration integrated within a millimeter wave substrate. The antenna is meticulously crafted to function across two distinct frequency ranges: 31.4 and 38 GHz. The individual antenna element comprises a substrate-integrated waveguide (SIW) cavity with longitudinal slots etched each in the ground plan and radiating patch, respectively. These slots are designed to resonate at frequencies of 31.4 and 38 GHz. The simulated outcomes demonstrate that the antenna showcases excellent performance in relation to return loss and radiation pattern within the dual-band range of 31.4 and 38 GHz, respectively. The return loss exceeds −10 dB (−26.1528 dB @ 31.4 GHz and −21.3605 @ 38.0 GHz) and a gain of 9.0809 dBi is achieved with a suitable bandwidth range of nearly 2.0074 GHz (from 30.6926 to 32.7000 GHz) and nearly 1.6673 Ghz (from 37.0714 GHz to 38.7387 GHz), respectively. The antenna structure is constructed using Roger RT5880 substrates, possessing a permittivity of 2.2 and a loss tangent (tan δ) of 0.002. Hence, this research article presents a novel SIW antenna arrangement that provides the capability of operating in two frequency bands, maintains a compact form factor, and exhibits an optimized structure.

Most of the antennas that are implanted into the human body are widely used for biotelemetry and hyperthermia purposes. In this paper, a miniaturized microstrip implantable patch antenna design is introduced which is used for Medical Implant Communications Service (MICS, 402–405 MHz) band biomedical applications. Since MICS band antennas operate in low frequency as compared to Industrial Scientific and Medical (ISM, 2.4–2.48 GHz) band and the size of the antenna is comparably large, miniaturization techniques are used to reduce the physical size of purposed antenna. A shorting pin and four ground slots are used for miniaturization purpose of antenna. The purposed miniaturized implantable antenna has two rectangle patches and one square shape outer ring element, in which all elements are electrically connected by metallic pad with a total dimension of 14 × 14 × 2.54 mm3. The proposed implantable patch antenna provides approximately 9.86% impedance bandwidth with improved dBi gain in the MICS band. Return loss characteristics, radiation pattern, bandwidth, and parametric study of some important parameters are analyzed of the purposed antenna.

The front-end amplifier is an essential component in neurological monitoring systems for signal detection and preprocessing, affecting besides the quality of the biosignal but additionally detector size and usage of power. In this study, a unique dual feedback loop-controlled method is presented to account for both signal leakage through the input bias network and leakage currents produced by low-noise amplifiers when they are implemented as integrated circuits. The Front-End Amplifier (FEA) is guaranteed to retain a significant input impedance despite all manufacturing and operating changes thanks to this loop design. Results from simulations using the 65 nm CMOS technology are given. This FEA uses 3.14 μW and accomplishes an input impedance of 2 TΩ and input referred noise of 23 pV/√Hz.

The quick advancement of wireless communication technology, especially the introduction of 5G networks, has made it necessary to design sophisticated and flexible antenna systems. Reconfigurable antennas are particularly valuable in wireless communication systems, where they can enhance system performance, increase spectral efficiency, and support multi-mode or multi-band operations. A patch antenna was designed by analyzing various antenna structures. By embedding the varactor pin diode into the antenna structure, two frequencies—24.68 GHz and 26.181 GHz—can be excited and switched when the diode operates in ON and OFF states. During ON condition, the antenna operates at 24.68 GHz with a gain of 8.143 dB, while in OFF state, the antenna operates at 26.18 GHz with a gain of 9.1 dB, respectively. With the use of Arduino-based control, this paper provides a novel method for creating reconfigurable antennas for 5G millimeter-wave applications. The integration of reconfiguration and Arduino control provides the way for enhanced adaptability and performance in future wireless communication systems, contributing to the realization of efficient and reliable 5G networks.

In this paper, a compact dual-channel microstrip diplexer is presented and a step impedance resonator (SIR) is used. The citation terminal is used for coupling lines and the two sets of different lengths and widths of SIR are joined for making a dual-channel diplexer. It is resonant at 1.7 GHz for GSM and 2.41 GHz for IEEE 802.11. Further benefits of the presented diplexer include minimal insertion losses (IL) of less than 0.93 dB and higher than 32 dB isolations between the ports. The primary and secondary passband group delays are less than 2.75 ns and 2.8 ns, respectively. For verification, the designed dual-diplexer is built and tested. The analysis proves that the measured and modeled results are close to each other.

This research study proposes a unique deep learning classifier using palm hand’s principle lines extraction approach for the palmprint recognition system. A Deep Convolution Multifractal Analysis Model for Palmprint Recognition (DCMAPR) is proposed to reveal the novelty. In pre-processing, the principle line feature is extracted using morphological operations and edge detection algorithm. For the feature extraction technique, multifractal analysis is used. To perform the techniques, Box-counting and Gliding-Box algorithms are performed for multifractal analysis. To more accurately authenticate the real person of the captured palmprint, classify this feature vector using Convolution Neural Network (CNN) classifier technique. The multi-spectral 2D-PROI image database used in this study came from POLYU, the Hong Kong Polytechnic University in Hong Kong. The proposed scheme has undergone scrutiny and evaluation using numerous criteria, and it has been determined to have 99.25% authentication accuracy.

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities.

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis.

Bandpass filters that operate in the microwave spectrum play a crucial role in the realm of radio frequency, as they are instrumental in suppressing undesired signals outside the desired frequency range. This paper investigates the various design procedure of bandpass filters (BPFs) used in 5G communication systems, covering both mmWave and sub-6 GHz frequency bands. Within this examination, the study explores various aspects, including different designs of resonators and methodologies involving structures with defective ground. These aspects are thoroughly analyzed, as they are critical in shaping the performance characteristics of microwave bandpass filters used in wireless communication systems across different frequency bands. The core of the research focuses on the evaluation and comparison of three distinct bandpass filters obtained from an extensive review of literature. This evaluation involves a comprehensive assessment of key parameters, such as cut-off frequencies, high selectivity, low insertion loss, significant fractional bandwidth, and high return loss. The findings shed light on the crucial role played by resonator design and optimization, specifically tailored to the frequencies of interest. This optimization serves as a crucial element in enhancing the overall performance of microwave bandpass filter designs, making them highly practical for the advancement of wireless communication systems in today's dynamic and demanding landscape.

The development of integrated circuit (IC) technology is accelerating to improve circuit performance and enhance system density. For the past few years, scaling CMOS devices has presented some difficulties. QCA (quantum dot cellular automata) is an innovative nano-electronic technology that could solve such transistor-based CMOS problems. Ripple Carry Adder (RCA) has been recognized and used as a basic component in designing several complex circuits, such as BCD adder, microprocessors, calculators, and signal processing applications. The delay of RCA turned out to be a major drawback in those circuits. This work involves the design of efficient mirror adder which proved as a potential alternate for RCA. The simulation of the proposed design has been achieved with QCADesigner-E and power dissipation analysis through QCAPro tool. The proposed adder reports less area, minimal delay, and reduced cell count.

This communication examines a multi-directional, compact pattern reconfigurable microstrip antenna. The proposed antenna has a straightforward design and is made up of a circular patch slotted with three T-shaped, three PIN diodes as a switch, and a microstrip feed microstrip antenna. The suggested antenna can realize four radiation pattern modes, at the frequency of 2.4 GHz by varying the switching situations of the PIN (switch) diodes. The designed antenna's operational frequency range, meanwhile, ranges from 1.8 GHz to 10.7 GHz cover the complete ultra-wideband. The radiation beam of the designed antenna can direct in omnidirectional, bidirectional, unidirectional, broadside in the E and H plane providing a gain of approximately 3 dBi at 2.4 GHz. It would be an excellent option for a wireless communication system that can be used for Bluetooth, WLAN, Wi-Max, and 5G communication along with ultra-wideband and cognitive network applications.

A voltage-controlled oscillator (VCO) is a pertinent constituent of numerous electronics gadgets in the present era. VCOs are used in a variety of applications, namely frequency synthesizer, function/signal generator, keypad tone recognizer, building of PLL and various other areas. The implementation and analysis of a VCO having high-frequency and low-power dissipation is presented in this study. The design employs current-starving technique, in which the amount of current for each inverter stage is limited. This technique helps to control power consumed by VCO and provides an efficient tuning of VCO oscillation frequency. The presented VCO provides an output frequency ranging from 749.07 MHz to 2.41 GHz when operated at 1.8 V supply voltage and consumes a total power of 1.5 mW. Tanner EDA tools have been used to analyse and implement the VCO design for the 180 nm CMOS technology node.

The condition of the images is degraded in some environmental situations, such as haze, fog, and mist. In several computer vision (CV) applications, such as remote sensing, border surveillance, and automated driver-assisted systems, image quality plays a significant role. Therefore, it is necessary to design a procedure to eliminate the haze effect, thereby improving the image quality. Several techniques are proposed earlier for haze removal. However, most of these techniques produce under-exposure and halo artifacts. A brightness enhancement called low-brightness image enhancement (LBIE) scheme is developed to address the under-exposure issue. For dehazing purposes, the dual transmission maps (DTM) are implemented in this work which overcome the halo-artifacts problem. The application of LBIE and DTM methods improves the dehazing performance when compared to state-of-the-art haze-removal methods.

In the last few years, combining multiple algorithms to improve the performance of machine learning models has been a common practice. However, its application to stress detection still needs to be explored. This paper uses a stacking ensemble technique to introduce a novel stress classification approach using electroencephalogram (EEG) signals. Specifically, the study evaluates the effectiveness of various classifiers, including Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Adaboost (AB), individually and in combination with RF as the base model, for stress classification tasks. The paper uses the SAM40 Dataset consisting of 32-channel EEG data to extract features in time and frequency domains for stress classification. The results show that the stacked classifiers outperform single classifiers, with RF + KNN providing the highest accuracy of 98.55%. The findings suggest that a stacked classifier is a promising approach for stress classification, as it can leverage the strengths of different algorithms and improve generalization performance. It holds promising future applicability in personalized stress management, healthcare interventions, and addressing societal concerns related to mental well-being.

Non-orthogonal multiple access (NOMA) stands out as an auspicious technique aimed at improving throughput, enabling maximum data rates (reaching 100+ MBPS), and reducing latency within the context of fifth-generation (5G) wireless communication systems, particularly in scenarios involving massive Internet of Things (IoT) connectivity. NOMA enables users to efficiently utilize shared frequency and time resources through power domain multiplexing. NOMA operates primarily based on two fundamental techniques. The first technique is superposition coding and the second method is successive interference cancelation(SIC). Within this context, the employed modulation schemes encompass binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK). An evaluation and simulation of several key metrics in the NOMA system, such as bit error rate (BER), signal-to-interference noise ratio (SINR), channel capacity, and outage probability, are conducted. Specifically, our study focuses on the power domain downlink NOMA system, and we observe its performance under different channel conditions, including Rayleigh fading, Rician fading, and Additive White Gaussian Noise (AWGN) channel, involving two users. Furthermore, our investigation includes a comparative analysis of channel capacity and outage probability through simulation, contrasting the performance of MIMO NOMA with that of NOMA. The primary goal of NOMA is to promote spectrum sharing, a concept reminiscent of cognitive radio networks. NOMA has the potential to enhance channel capacity in the context of 6G communication systems.