Bhagwati Sharan

This article presents a novel multiband, biocompatible MIMO nanoantenna for terahertz applications, designed to address the growing demand for faster data transfer rates in future wireless nanocommunications systems. The design process began with a foundational single-element antenna (157.12 × 184.40 × 11 μm³) constructed from a gold patch and ground on a PTFE substrate. This initial element, resonating at 1.041, 1.602, 2.199, and 2.814 THz, was subsequently expanded into a two-port MIMO structure (157.12 × 276.60 × 11 μm³) to enhance channel capacity. The proposed MIMO nanoantenna operates nearly at the same frequencies as the single-element nanoantenna. Still, it delivers better performance, achieving a significantly higher channel capacity (up to 1.163 Tbps at 1.044 THz) and gain (up to 9.06 dBi at 2.214 THz). Furthermore, the MIMO system demonstrates excellent diversity performance, with an ECC close to zero and a consistently high DG of 9.999 dB—indicating effective signal fading mitigation and enhanced reliability. The proposed nanoantennas prove highly effective for multiband operations up to 3 THz, demonstrating significant potential for various advanced applications. These include low-power 6G communications, high-resolution terahertz imaging, in-vivo biomedical sensing, and other high-speed nanoscale communication systems.

This paper presents a terahertz (THz) metamaterial-based nanosensor employing a split ring resonator (SRR) for the detection of NT-proBNP, a cardiac biomarker released in response to increased myocardial pressure and volume overload within the heart. The sensor is designed and simulated in CST Studio to enable real-time detection via changes in the refractive index of NT-proBNP associated with cardiac abnormalities. Validation is performed through equivalent circuit modeling (ECM) using Advanced Design System (ADS). The nanosensor achieves a sensitivity of 1460 GHz/RIU, a Q-factor of 22.06, and a figure of merit (FOM) of 41.71. Assuming minimally invasive placement within the pericardium, signal attenuation is modeled using a path-loss framework that accounts for the serous and fibrous pericardial layers. Transmission line theory is applied to evaluate the intrinsic impedance, reflection coefficients, and attenuation characteristics of THz waves propagating through cardiac tissue. The model estimates the received power at a nanocontroller located at the fibrous layer, and is validated using COMSOL Multiphysics^®. By leveraging refractive index variations induced by NT-proBNP, this nanosensor enables intra-body THz communication as a diagnostic modality. The platform is particularly suited for detecting conditions such as pericarditis, where biomarker fluctuations and pericardial thickening jointly modulate the THz signal.

Parkinson's disease (PD) is a neurodegenerative disorder of the brain that primarily affects motor function. Clinical challenges associated with this condition include accurately diagnosing patients in the early stages of the disease and predicting how the condition will progress. This project aims to enhance PD detection by integrating feature selection and classification using supervised learning techniques. Two publicly available datasets—the speech and PD classification datasets—are utilized to evaluate model performance across diverse features. The proposed work employs class balancing through the Synthetic Minority Oversampling Technique (SMOTE) to address the issue of class imbalance in this highly unbalanced dataset. Subsequently, the Relief algorithm is used for feature selection to identify the most relevant predictors. An ensemble of models is applied using the RF-XGBoost-KNN classifiers due to their superior accuracy compared to other classifier combinations. The RF-XGBoost-KNN model stack achieved classification accuracies of 94.56% and 93.53% for the PD speech dataset and Parkinson's Disease Classification Dataset, respectively, demonstrating its potential as a robust tool for early and accurate PD diagnosis.

In recent years, wireless communications have evolved significantly, and many mobile devices have reduced in size. The antennas used in mobile terminals must be lowered in size to fulfil the downsizing standards. Planar antennas, like microstrip and printed antennas, have a low profile, compact size, and conformability to mounting hosts, making them particularly desirable candidates for achieving these needs. Additionally, planar antennas are also being used in communication devices for 2.4 GHz (2400 – 2484 MHz) and 5.2 GHz wireless local area network (WLAN) systems (5150). In wireless applications, an antenna is a crucial component. At the transmitter, it transforms electrical signals into RF signals, and at the receiver, it converts RF signals to electrical signals. The patch inside the antenna is made of a conducting material such as Cu (Copper) or Au (Gold), and it can be rectangular, round, triangular, or elliptical. Two unique designs of microstrip planar antennas with an operating frequency of 5.2 GHz having S11 parameters as -16.0 dB and -15.7 dB have been offered and their performance has been studied in this research article.

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%.

Vehicular Ad-hoc Networks (VANETs) are the most popular research area of wireless technology. It is offering various types of services such as safety and comfort for drivers as well as for passengers and requires less infrastructure than other technology. VANETs are being used in the concept of making smart cities, and smart traffic management systems all over the world. Due to vulnerability in VANETs, undesired data may attack the network traffic and broadcast false messages in the entire network which causes poor communication among the networks. False data or undesired information is termed malicious data. These malicious data can affect the performance of VANETs it increases vehicle delay, increases fuel consumption and there will be more safety threats. So, there must be a relative study, which will show the variation of network performance metrics with enhancing the number of malicious nodes in the network. This paper had focused to discuss the detection and a deep analysis of affects of malicious nodes on the network performance as throughput, average latency, and packets drop in the network. In this paper, we have discussed a novel approach to detect malicious data with Artificial Neural Networks as well as malicious nodes with Support Vector Machines in VANETs.

The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change.