Proceedings of Third International Conference on Computational Electronics for Wireless Communications

The medical care industry is seeing a critical change with the coming of Web 3.0 innovations and the combination of blockchain. It investigates the capability of utilizing Web 3.0 and blockchain to foster play-to-acquire applications in the medical services area. Play-to-procure applications encourage people to participate in well-being related exercises and award them with advanced resources or tokens. By uniting the power of Web 3.0, which engages decentralized and secure partnerships, with block-chain's straightforwardness and perpetual nature, these applications might conceivably modify how we approach clinical benefits and empower individuals to expect control over their thriving. We propose an idea of play-to-procure applications and examine how Web 3.0 and blockchain can upset the medical services scene. It investigates the advantages, challenges, and moral contemplations related to fostering these applications. Also, the part features certifiable models and contextual analyses that show the extraordinary force of play-to-acquire applications in advancing sound ways of behaving and engaging people to assume command over their wellbeing.

The functionality and longevity of a wireless sensor network can be negatively impacted by malicious security attacks on the routing protocols. This is more crucial in cluster routing protocols like the low energy adaptive clustering hierarchy (LEACH) protocol, which consists of numerous nodes and a cluster head. Particularly, a complete collection of nodes fails if an attack is successful in taking out the cluster head. In order to defeat security threats and quickly retrieve packets, it is imperative to build reliable recovery techniques. Therefore, employing the LEACH protocol in energy-effective clusters, this research suggests a monitoring and recuperation technique for selective forwarding attacks in modest power-based wireless sensor networks. The suggested approach has almost immediate recovery times and does not involve feedback or subsequent transmissions when under assault.

The four-port MIMO antenna facilitates the use of the characteristics of the C-, X-, and Ku-bands. The four radiating patch elements have been mounted on top of a 20 mm × 20 mm × 1.6 mm FR-4 substrate with a dielectric strength of 4.4. The suggested MIMO antenna functions with isolation between radiating elements exceeding 20 dB at an effective frequency of 7.5 GHz. To enhance the separation of antenna elements, orthogonal placement has been adopted. The suggested MIMO antenna was simulated using the HFSS software. The simulated values of ECC, CCL, and DG are less than 0.5, 0.4 bps/Hz, and near 10 dB, respectively. The multiband responses make the proposed work satisfactory for C-band (4.34 GHz and 6.50 GHz), X-band (9.72 GHz), and Ku-band (13.98 GHz) applications in wireless communication such as satellite, TV, and RADAR applications.

Advancements in the field of science and technology encompass the gradual improvement of worldwide intelligence. These advancements encompass the downsizing of current hardware components to the nanoscale. The utilization of nanotechnology is focused on replacing conventional CMOS-based technologies, facilitating the development of even smaller devices compared to those manufactured using CMOS. There is currently extensive research ongoing in this domain to reduce the size of CMOS chips and address power consumption issues. Hence, the approach encompasses the utilization of Quantum-dot Cellular Automata (QCA) to attain objectives related to component miniaturization and reduction of power consumption. This study explores the fundamental principles of QCA, including its individual cells, majority gates, clocking mechanisms, and the previous works of diverse designs for circuits using QCA technology. Further multiplexer circuits hold significant utility, being employed in communication systems, data transmission from satellites to ground stations via GPS, and computer memory.

Melanoma is a very harmful and disastrous form of skin cancer, has prompted a surge of interest in utilizing deep learning methods, specifically Convolutional Neural Network (CNN), for its early detection through image analysis. However, this endeavor faces challenges, including a scarcity of training data, similarities between various skin lesion types, and variations within the same lesion class, compounded by the need to fine-tune numerous parameters in existing methods. To address these issues, this study introduces an automated framework that leverages pre-trained deep CNN models to extract visual features from skin lesion images, followed by the application of classifiers to identify melanoma. While earlier research has employed pre-trained CNN architectures to extract features, a comprehensive analysis of multiple CNN models for melanoma classification has been lacking. The research demonstrates that Alex Net, in combination with a multi-layer perceptron (MLP), achieves the highest accuracy at 88.5%, Outperforming other CNN models and current cutting-edge techniques in the crucial field of melanoma determination.

The paper describes a rectangular ring-shaped two-element pattern diversity mm-wave MIMO antenna for 5G and IoT applications. The Roggers RT duroid substrate, having a dielectric constant of 2.2 and a height of 0.254 mm is used to design the proposed antenna. Its frequency range of operation is 12.6–35.1 GHz, and its gain is 4.5 dB with fluctuations of fewer than three decibels across the working range. Mutual coupling is less than −17 dB throughout the whole bandwidth, and efficiency is greater than 93%. All MIMO parameters have been computed and verified to fall under permissible bounds. Its small dimension of 1.4 × 1.5 × 0.0254 cm3 makes it economical and suitable for portable devices, 5G base stations, 5G mobiles, IoT terminals, healthcare devices, and a wide range of other mm-wave applications. Performances of the proposed antenna are better when compared to those of reported MIMO antennas.

This work presents and analyzes the two/eight-port MIMO antenna operating in dual-band applications. Band 1 is used for LTE B41, whereas band 2 is for WLAN, downlink defense, Wi-Fi 6E (n96), and C-band. The two-port MIMO antenna is built around diamond-shaped with partially ground-plane on jeans substrate. primarily the antennas are placed side-by-side, to enhance to isolation more than 22 dB a T and I-shaped slots are being carved into the ground. The two-port MIMO antenna's total dimensions are 20 × 34.5 × 1.5 mm3. The first band impedance bandwidth is 2.41–2.97 GHz (20.81%), while the second band impedance bandwidth is 4.22–8.33 GHz (65.5%). The minimum and maximum isolation of Band 1 and Band 2 are 20 dB, more than 23 dB, respectively. The recommended dual-band eight-port MIIMO antenna is made up of four two-port MIMO antennas that are positioned orthogonally to create better isolation (>22 dB) between ports. For entire operating bands, the radiation efficiency is more than 65% and the peak gain of the proposed eight-port antenna is 3.5 dB at 8 GHz. The volume of the eight-port MIMO antenna is 4140 mm3. The antenna provides excellent MIMO diversity performance, as evaluated by metrics such as Envelope Correlation Coefficient (ECC < 0.01), Diversity Gain (DG > 9.98 dB), Mean Effective Gain (MEG < −6 dB), Channel Capacity Loss (CCL < 0.03 bits/s/Hz), and Total Active Reflection Coefficient (TARC < −10 dB). Because of its outstanding MIMO diversity performance, the antenna is a promising solution for a wide range of handheld wireless applications.

Microwave hybrid heating is an efficient method of heating that combines traditional heating techniques with the power of microwaves. In this study, microwaves have been utilized to modify the mechanical properties of tungsten carbide (WC/Co) cutting tool inserts by enhancing the microhardness and wear resistance properties. This result improvement in microhardness is expected to ease the cutting process and reduce tool wear. This research will involve conducting experiments, including assessing the microhardness and tool wear measurement by comparing the performance of microwave-treated tool inserts with untreated tool inserts. The findings from this study could have implications for the manufacturing industry as they offer a solution to enhance machining processes for high-strength materials while prolonging the lifespan of cutting tool inserts. The improved efficiency and reduced costs associated with replacing tools will be among the outcomes obtained from this research ultimately contributing to advancements, in metal-cutting technology.

The aviation sector is dynamic and always evolving, and the fluctuating cost of tickets makes it difficult for travelers to plan their trips affordably and plan a trip economically. The differences in air ticket prices exist so much so that always it has been observed that various travelers travel in the same flight with varied ticket prices. This leads to a situation of underbooked flights and high consumer dissatisfaction which ultimately have a negative financial impact on the airline business. Timely flight price forecasting would aid airlines in planning their operations and assembling the resources essential to impact a particular consumer section level for a specific route. With a motive to find the model with the lowest mean absolute error when forecasting the costs of a journey, this work tries to offer a model that effectively incorporates the variation among many elements determining the price of an airfare. Prices for several airlines’ flight tickets were provided for the dataset in this study, which was obtained from Kaggle. The dataset was engineered efficiently using various feature engineering and selection methods. Afterward, 80:20 data was divided into training and test data, respectively. The model was trained using the Random Regressor Algorithm. The model obtained a Normalized Root Mean Square Error of 0.06. Further, an unseen dataset was fed into the model to predict the flight ticket prices.

This paper comprehensively reviews emotion recognition techniques, spanning various sources such as questionnaires, physical signals (facial expression and speech), and physiological signals (EEG, galvanic skin response, ECG, and eye tracking). It delves into emotion models, elicitation stimuli, and automated recognition systems, analyzing almost 60 articles from journals through PRISMA guidelines. The review identifies challenges in the existing literature and suggests future research directions in this evolving field, crucial for effective computing, healthcare, human–robot interactions, and market research.

The researches show that people experience more mental stress as a result of an increase in workload. Excitation or emotional excitation is considered a “stress” situation and indicates a certain psychological state that might be impacting a person's performance. Stress increases health issues in the human body if it’s experienced for a long time. People went through a variety of stresses during the current epidemic and lockdown, including financial loss in company, family relationships, and joblessness. The long period of stress may be the cause of mental disorders, diabetes, depression, and other kinds of diseases. In this paper, the electroencephalography (EEG) signals are recorded with different mind states like low stress and high stress. The recorded electrical waves are compared with normal state and Low/High stressed states. For this, the database is collected with EPOC X headset. It is a 14-channels headset that is placed in different positions on the scalp. The normal state and low-stress state have almost the same types of brain signals, but the high-stress state has large differences in the detected signals.

In order to carry out high data rate communication using underwater wireless systems, highly efficient underwater antennas are required. The physical characteristics of conventional antennas are affected by lossy media, which are present in most underwater ecosystems. The MIMO antenna under consideration has been specifically engineered to address these identical factors. This multi-input multi-output antenna is designed on Rogers RT/duroid 5880 substrate with thickness h = 1.6 mm, the dielectric constant εr = 2.2, and tan δ = 0.0009. The overall dimension of the proposed antenna is 5 mm × 5 mm × 1.6 mm for a single antenna and 10 mm × 5 mm × 1.6 mm for a MIMO antenna having two elements. The patch of the proposed antenna is tilted by 30°, which provides a wide bandwidth for the UWB antenna. The proposed antenna exhibits an impedance bandwidth spanning from 4.7 GHz to 20 GHz, with port-to-port isolation exceeding 20 dB and peaking at 65.6 dB. Additionally, the simulation analysis explores essential MIMO diversity parameters, including ECC (Envelop Correlation Coefficient), DG (Diversity Gain), and TARC (Total Active Reflection Coefficient), revealing outstanding performance characteristics. This proposed MIMO antenna is an excellent candidate for underwater communication.

Maize is a crucial crop grown in India, and maize leaf diseases often result in significant yield losses. Therefore, it is crucial to identify these diseases and provide remedies for them. In this study, we propose using the Extreme Gradient Boosting (XGBoost) classifier along with extracted Convolutional Neural Network (CNN) features to detect and classify various maize diseases. To enhance the model’s generalization capability, we employ augmentation techniques. For training and testing purposes, publicly available datasets have been used. The CNN model alone achieves a test accuracy of 90.8%. However, by training the extracted CNN features with the XGBoost classifier, the accuracy further improves to 92.5%. Additionally, we introduce an Android-based solution that offers farmers a convenient method to identify crop diseases.

This paper presents the design of splash plate dielectric hat feed for Ka-Band tracking radar applications. Dielectric hat feed (DHF) configuration is used by combining a metallic hat sub-reflector and a circular waveguide with the dielectric rod. Splash plate hat with corrugations is used in order to improve side lob levels of antenna, minimize reflection coefficient and improve circular symmetry of radiation patterns. At input of the feed, circular waveguide is divided into four (2 × 2) rectangular waveguides using a circular to rectangular transition. This feed provides three different radiation patterns as SUM, Elevation DELTA and Azimuth DELTA required for tracking radar applications. EM simulation tool has been used for simulation and optimization of the feed and finally reflector simulation tool is used to simulate the exported feed patterns with reflector antenna. Simulated gain and SLL of reflector with designed feed is better than 44 and 25 dB respectively over the frequency band of 34.8–35.2 GHz.

This paper provides a comprehensive comparison of load-balancing algorithms employed in cloud computing, spanning from the earliest to the most recent developments. Emphasizing the significance of load balancing for system performance, resource optimization, and equitable allocation among users, the paper offers insights into various techniques, their effectiveness across workloads, and associated limitations. With an overview of each algorithm, this study caters to practitioners and researchers in the dynamic field of cloud computing, aiming to contribute valuable perspectives for enhanced system efficiency.

Congestion is one of the biggest hurdles in the networking environment. Efficient communication involves minimizing congestion. One of the important factors in congestion is the improper or overutilization of the network bandwidth. Other reasons such as outdated hardware, low link bandwidth, bandwidth hogs, network device malfunctioning, poor network configuration and the number of devices in the network are also responsible for congestion. It leads to packet loss, delay, performance degradation, timeout, jitter, buffer overflow, Packet retransmission etc. To deal with these issues, researchers need to focus on various parameters to handle congestion efficiently. In this paper, we have considered various parameters such as throughput, fairness, packet loss, packet loss ratio, delay, etc. for performance analysis.

Sensor networks are the backbone of emerging Internet of Things (IoT) ecosystems, serving as critical components in various applications ranging from environmental monitoring to smart cities. However, the Quality of Service (QoS) in sensor networks is compromised by issues such as communication delay, energy consumption, and throughput limitations. To address these challenges, this paper introduces a novel optimization algorithm called Ant Lion Grey Wolf Optimizer (ALGWO). Inspired by the foraging behaviour of ant lions and the hunting strategies of grey wolves, ALGWO aims to optimize both temporal and spatial routing in sensor networks. It uses spatial node features, and temporal network parameters to optimize routing performance under real-time scenarios. The algorithm deploys bioinspired techniques for iterative search and congestion control mechanisms to enhance network performance. Our experiments demonstrate remarkable improvements in QoS parameters: an 8.3% reduction in communication delay, a 4.9% decrease in energy consumption, and a 10.4% boost in throughput when compared to recently proposed routing models. The findings indicate that ALGWO provides a robust and efficient framework for optimizing sensor network performance, holding significant promise for real-world applications.

In modern system-on-chips (SoCs), embedded static random access memory (SRAM) units are vital components that facilitate on-chip memory for fast data storage and access. However, traditional SRAM cells based on metal oxide semiconductor (MOS) designs consume relatively high power, making them less suitable for power-constrained devices. Researchers are tackling the constraints of SRAM technology by investigating refined approaches rooted in carbon nanotube field-effect transistors (CNTFETs) which includes fine-tuning of model Parameters like nanotube diameter, flat band voltage, and CNT density to enhance SRAM cell performance and efficiency and to create more advanced and power-efficient memory solutions. The objective of this work is to design, evaluate and predict the performance of different SRAM cell by incorporating low-power strategies within 5 nm node CNTFET 6 T SRAM cell. These strategies encompass the Sleepy approach, Header approach, Footer approach, Zigzag approach, Leakage feedback approach, Stack approach, Leakage feedback with stack approach, Sleepy keeper approach, Sleepy stack approach, and Sleepy stack with keeper approach. To evaluate their effectiveness, the research paper uses performance metrics such as noise margin, delay, and leakage power.

In this paper, the bandwidth enhancement and improvement of directivity of a composite conical structure in the presence of a circular loop antenna are investigated. In terms of matching properties, the isolated loop has very poor return loss. The improvement of the matching qualities in the presence of a composite conical structure is due to the mutual coupling between the loop and the composite conical structure. A narrow bandwidth conducting cone was previously used to improve the poor matching in return loss characteristics of an isolated loop antenna. The present technique achieved 17.77% –10 dB bandwidth compared with 6.56% –10 dB bandwidth in a conducting conical antenna, producing a 171.00% –10 dB bandwidth enhancement without impacting cross-polarization and improvement in directivity as well. By varying several parameters, including wire diameter, loop radius, cylinder radius, and height of the cylinder from the bottom of the cone, we have been able to observe the return loss, radiation pattern, –10 dB bandwidth, and directivity. We have contrasted the outcomes of our simulations with the measured ones.

This paper presents design of C-Band feed assembly for Doppler weather radar application. The feed assembly consists of a corrugated horn antenna to achieve low cross polarization integrated with an Ortho Mode Transducer (OMT) to achieve dual polarization. The novelty in the design is achieving all design goals with reduction in size in comparison to conventional corrugated horn antenna. Size of the corrugated horn antenna is approximately 100 mm*32 mm (D*L). This design also gives low VSWR & symmetric radiation pattern in Elevation & Azimuth plane over 10% bandwidth.

Ferroelectric FETs have gained popularity as the coming generation of technology since these can function as a synaptic device enabling neuromorphic application as well as a one transistor (1 T) for higher incorporation. In this article, we will look at the latest developments in ferroelectric field-effect transistors (FeFETs). We deal with fundamental operation principles, material properties, and device structures, with a particular emphasis on the usage of FeFET in nonvolatile memory applications. Cycling endurance, retention, and memory window are all important device performance parameters. We also provide a brief overview of current advancements in alternate FeFET applications such as neuromorphic, in-memory computing, and eFLASH devices.

Satellite communications play a vital role in the current era of communications. In directional communications, there is a huge demand for highly efficient directives, and a better antenna for effective communication is required for satellites. This paper primarily focuses on the design and analysis of a reflector parabolic antenna which is used for satellite communication. The proposed design of reflector parabolic antenna achieves higher gain and better directivity than existing works and is found more efficient with better impedance matched using an Ansys HFSS. Along with this, it also focuses on the offset configuration of the parabolic reflector antenna.

In the current context, Orthogonal Frequency Division Multiplexing (OFDM) technology has evolved into OFDM-IM, which is recognized for its superior BER efficiency when compared to OFDM. Our investigation has revealed superior BER performance of OFDM-IM, particularly in single input single output (SISO) configurations under AWGN noise. Our subsequent objective is to scrutinize the performance of OFDM-IM in multiple input multiple output (MIMO) scenarios, employing Millimeter-Wave (mm-Wave) channel mediums with various types of fading. In summary, this assessment offers valuable insights into the performance of both OFDM and OFDM-IM systems under AWGN conditions, shedding light on the advantages that OFDM-IM may bring to forthcoming communication systems.

This research initiative represents a comprehensive effort to unveil latent criminal tendencies by harnessing sophisticated data analytical methods. The focus is on a substantial dataset encompassing criminal activities within the city of Boston from 2017 to 2022. The study utilizes Exploratory Data Analysis (EDA) as a pivotal tool to systematically investigate and extract hidden patterns of criminal behavior, thereby acquiring valuable insights from the extensive dataset. The exploration of concealed crime patterns involves the application of diverse analytical techniques. Firstly, the study delves into temporal trends to discern patterns that may indicate shifts in criminal activity over time. Correlation heatmaps are utilized to uncover potential relationships between various types of criminal incidents, providing a nuanced understanding of their interconnectedness. Cluster mapping is utilized to spatially visualize concentrations of criminal activities, facilitating of high-risk areas. Furthermore, the distribution of crime categories is meticulously examined, shedding light on the prevalence of specific offenses within the dataset. Heatmap visualizations are employed to provide a visually intuitive representation of crime hotspots, contributing to a more accessible interpretation of spatial patterns. By employing these methodological approaches, the research seeks to make a meaningful contribution to our understanding of criminal dynamics in Boston.

Crime hotspot detection is a critical aspect of urban safety and law enforcement. This research employs advanced spatial analysis techniques to predict and identify regions prone to high crime rates. Through the integration of geospatial data and predictive modelling, we present a method that enables law enforcement agencies (LEA) to proactively allocate resources and implement targeted interventions. The study demonstrates the effectiveness of this approach through a comprehensive evaluation of real-world crime data. The findings contribute valuable insights to enhance public safety strategies and reduce criminal activity in urban areas. The accuracy and precision of the proposed model is 82% and 85% respectively.

A UWB antenna design with Split Ring Resonator and defected ground structure is represented in this article. The antenna's design offered better return loss characteristics and a 10 dB bandwidth above 2 GHz. The antenna is designed by using Roger 3003 substrate due to its easily availability and less lossy behaviour. The designed antenna is resonated at the resonating frequency of 5.42 GHz. It takes careful planning and matching of the antenna or transmission line to the source and load impedances to achieve a VSWR of less than 1.5. The antenna implies the VSWR less than 1.5 for the frequency range of 4.86–6.15 GHz. The antenna can be used in Wireless Local Area applications and IoT applications.

In this fast-paced life, many couples find themselves entangled in demanding professional commitments, leaving them with limited capacity to tend to their own well-being, let alone the needs of their newly born offspring. The pressure and demands of work can take a toll on their health and well-being, making it challenging to balance personal and professional life. The arrival of a new-born baby brings immense joy and happiness in the family, but it also comes with a new set of responsibilities and challenges. One of the biggest challenges is that new parents want to ensure the safety and well-being of their baby, especially when they are at the workplace. It can be stressful for parents to leave their baby at home or in the care of someone else, fearing that their baby may not receive the care and attention that they are expected to. Although baby care centres have emerged as a popular solution for working parents but there have been instances where fraud and negligence have been taken place, raising concerns about the safety and security of infants. Therefore, there is a need for a reliable and efficient solution that can provide real-time monitoring and alerts to parents, ensuring the safety and well-being of the infant. To address these issues this paper proposed a Smart Cradle that provides parents with real-time monitoring and alerts, which helps the parents to monitor their infant/baby from the workplace.

This work presents a review of the different types of antenna designs and recent trends for the implementation of planar antennas for different objectives in an IoT-based smart agriculture system is presented. In this work, a review of the different applications for which antennas are being investigated in smart agricultural systems are discussed. The different types of designs and procedures for the applications in agriculture is reviewed.

The process that produces written descriptions that effectively represent the meaning and context of an image is known as image captioning. To integrate visual and textual data, it needs to blend computer vision and natural language processing methods. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), such as long-short-term memory (LSTM) networks, are two methods used for captioning images. High-level visual features from the input image are extracted by the CNN, and the RNN uses those features to generate the matching captions. Residual Network is a deep CNN architecture with exceptional results in a range of computer vision tasks, including the classification of images. ResNet has been used as the foundation for extracting picture features. ResNet and CNN abstracts an image's visual information via feature extraction and uses neural networks with recurrent architecture to provide meaningful, contextually appropriate captions. Machines are now able to comprehend and speak about the visual world in a manner similar to humans because of the integration of computer vision and natural language processing.

This research paper centers on the pivotal role of sugarcane cultivation in India's economic development. Sugarcane, a versatile crop, serves diverse purposes encompassing brown sugar production, animal feed, white sugar, bio-electricity, and bio-ethanol. Addressing the needs of an expanding global populace necessitates augmenting sugarcane yields. However, productivity confronts significant threats from pests and diseases, resulting in substantial economic repercussions. Timely detection of these issues is imperative for proficient pest management and productivity augmentation. The study underscores the imperative for automated detection and early diagnosis of sugarcane diseases, elucidating the limitations of manual visual inspection. To surmount these challenges, the application of image processing algorithms is pivotal in promptly extracting features from sugarcane leaves and discerning ailments in their nascent stages. The research offers an exhaustive overview of diverse image processing techniques and deep learning methodologies for effective sugarcane disease detection and expeditious assessment. Furthermore, it scrutinizes the intrinsic challenges in computational approaches for appraising sugarcane infections and delineates potential future trajectories. Overall, this inquiry accentuates the critical import of precise disease detection in sugarcane crops, accenting the considerable economic ramifications of diseases on agricultural output. Harnessing intelligent computational tools empowers farmers to proactively combat sugarcane diseases, culminating in ameliorated crop quality and augmented yield. This paper furnishes an invaluable resource for researchers, agronomists, and technologists in pursuit of cutting-edge techniques propelling advancements in sugarcane disease detection, thereby laying the groundwork for the formulation of more efficacious and streamlined disease management strategies in the sugarcane industry.

In this paper, a predictive approach for calculating the return loss of a slotted square patch antenna with a defective ground structure (SSPA-DGS) designed for energy-harvesting applications in smart cities is introduced. The strategy makes use of the Gaussian Process Regression (GPR). The designed antenna works from 1.7 to 3.2 GHz. We varied the antenna internal slot radius Ra, patch length la, thickness t, and dielectric constant of the antenna. A total of 125 data samples were taken from the simulation using HFSS software, and all the samples were used in ML models. For validation, 25 data samples are used to test the prepared models for the GPR. The data is compared with the simulated return loss data. The predicted return loss of GPR model has an average error of −0.041785325.

A tunnel FET (TFET) can deliver extremely modest quiescent current (~pA). Minimal ambipolar leakage, restricted subthreshold slope value, and high ION current are a few of the crucial metrics needed to identify TFET features. TFETs have more robust transconductance per bias current than MOSFETs because they undergo a sub-threshold decline of less than 60 mV per decade during the sub-threshold slope process. To gain an overview of different tunnel FET device topologies and their respective accomplishments, this manuscript will be helpful. In order to achieve the intended ION / IOFF, we analyzed and evaluated the outcomes of various TFET device architectures in this article.

Worldwide, oral cancer is one of the most common cancers. Even with easy access to the oral cavity and significant improvements in treatment, oral cancer death rates remain high, primarily due to late-stage diagnoses and less successful treatments. The best way to treat cancer is to detect it early, and oral cancer is among the most expensive types in the United States. Patients’ survival rates are improved, and medical costs are reduced when early diagnosis occurs. Numerous techniques have been investigated by researchers for detecting oral malignancies in the early stages. An extensive review of the various methods analyzed by researchers to detect oral cancer at an early stage is presented in the paper. An overview of algorithms used for each step of cancer detection algorithms is provided, along with a comparison of different methods for cancer identification and classification.

This article describes a design of Teflon substrate based modified micro strip patch antenna. One PTFE polymer that is sold commercially is Teflon. The substrate's loss tangent is 0.0002, and its dielectric constant is 2.1.The suggested design measures 50 by 32 mm2, and the patch has a thickness of 0.05 mm. Two slots with the same height and width were made in this design. For industrial, scientific, and medical (ISM) band applications, the antenna resonant at 2.45 GHz frequency. The specific absorption rate (SAR) was calculated using a flat human body phantom. In CST Microwave Studio, the model is simulated and the SAR value is computed.