This article introduces non-orthogonal frequency and time index modulation multiple access (NFT-IMMA) for sixth-generation (6G) communications. The proposed NFT-IMMA increases the number of communication resources by utilizing power levels, frequency, and time slots as transmission entities, which are selected through a predefined index activation pattern derived from partial user input data. NFT-IMMA increases spectral efficiency (SE) by employing index information and classical constellation symbols as methods of data transmission. Notably, it does not require prior scheduling at the base station, which simplifies the coordination process. Additionally, the use of three-dimensional indices for resource selection helps reduce user collisions. Performance evaluation at the physical layer, focusing on SE and bit error rate, indicates that NFT-IMMA outperforms conventional index modulation (IM) multiple access and IM-based orthogonal frequency division multiple access. At the MAC layer, NFT-IMMA demonstrates superior performance in terms of throughput and packet loss rate when compared to ALOHA and other competing techniques.

With rapid digitization and digitalization, drawing a fine line between the digital and the physical world has become nearly impossible. It has become essential more than ever to integrate all spheres of life into a single Digital Thread to address pressing challenges of modern society – accessible and inclusive healthcare in terms of equality, and equity. The techno-social advancements and mutual acceptance have enabled the infusion of digital models to simulate social settings with minimum resource utilization to make effective decisions. However, there is a significant gap in feeding back the models with appropriate changes in real-time. In other words, an active behavioral modeling of modern society is lacking, that influences community healthcare as a “whole”. By creating virtual replicas of physical systems, digital twins can enable real-time monitoring, simulation, and optimization of urban dynamics. This paper explores the potential of digital twins to promote inclusive healthcare for evolving smart cities. We argue that digital twins can be used to:Identify and address disparities in access to healthcare servicesFacilitate community participationSimulate the impact of urban policies and interventions on different groups of peopleAid policy making bodies for better access to healthcareThis paper proposes several proposals for stitching the actual and virtual societies using digital twins. Several discussed concepts within this framework envision an active, integrated, and synchronized community aware of data privacy and security. The proposal also provides high- level step-wise transitions that will enable this transformation.

This study presents the development and implementation of a facial emotion recognition system designed to detect mental stress in students through a kiosk-based approach using Raspberry Pi. The primary objective of this system is to enhance the real-time monitoring and management of mental health by integrating various electronic components and machine learning models to identify emotions indicative of stress. The kiosk system utilizes an RPI 5 & ESP32 microcontroller due to its high-speed data transmission capabilities and extensive interfacing options, essential for connecting multiple sensors and modules. The MAX30100 sensor monitors heart rate through photoplethysmography, while the MPU6050 sensor tracks physical activity and sleep patterns. Each sensor is carefully selected for its functionality and compatibility with the ESP32, ensuring reliable data collection and processing. The kiosk's design features a user-friendly interface for real-time feedback, and data management is facilitated through an SD card module. This research introduces an advanced facial emotion recognition system tailored to detect and classify seven distinct emotional states-Neutral, Surprised, Sad, Happy, Scared, Disgust, and Angry-to assess mental stress in students. The system employs a kiosk-based approach built on the Raspberry Pi 5, equipped with a webcam for real-time emotion detection and processing. By combining image capture, machine learning models, and a user-friendly interface, the system aims to provide accessible and effective mental health monitoring solutions, particularly in academic environments where stress levels can significantly impact well-being. The kiosk utilizes a webcam to capture facial expressions, which are processed using a Convolutional Neural Network (CNN) trained on a dataset encompassing seven emotional states. The CNN model identifies the seven emotions with high precision by extracting facial features and mapping them to corresponding affective states. Real-time classification results are displayed on a 10.1-inch screen integrated into the kiosk, allowing users to receive immediate feedback on their emotional state. This system combines Random Forest algorithms for physiological data analysis and CNN models for facial expression classification, ensuring a holistic evaluation of emotional and stress indicators. This innovative facial emotion recognition system not only classifies emotions but also offers a comprehensive tool for managing mental stress, paving the way for scalable, technology-driven mental health interventions in educational and professional settings. ENHANCEMENTS: In addition to facial emotion recognition, the system incorporates a Galvanic Skin Response (GSR) sensor to enhance the detection and classification of the seven stress-related emotional states-Neutral, Surprised, Sad, Happy, Scared, Disgust, and Angry. The GSR sensor measures changes in skin conductivity, which correlate with physiological arousal and emotional intensity. By integrating GSR data, the system captures real-time stress indicators, offering deeper insights into emotional states. The sensor data is preprocessed and analyzed using machine learning models to associate specific conductivity patterns with each of the seven emotional levels. This multimodal approach, combining GSR with facial analysis, strengthens the system's ability to accurately monitor and interpret emotional and stress responses, providing a more comprehensive understanding of user affective states.

In this paper, we report the performance of an engineered beam utilizing spatial and polarization diversity to make the beam robust against propagation over a turbulent channel. Additionally, we observe the modification in the intensity fluctuations distribution of the received signal intensity with the engineered beam compared to the Gaussian beam. We simulated the beam propagation over a different turbulent channel using a multiple-phase screen and split-step method to measure the intensity fluctuations distribution. The measured intensity fluctuation distributions are closely matched with the generalized gamma distribution (GGD) over various turbulence regimes for our considered simulation model. Moreover, we measured intensity fluctuations and scintillation index; we found that the strength of turbulence experienced by the engineered beam is lesser than the Gaussian beam for different turbulence regimes. Furthermore, we used the GGD to derive analytical expressions for critical performance metrics, including average capacity, biterror rate (BER), and outage probability. The study investigated free-space optical (FSO) systems that used both engineered and Gaussian beam transmissions, employing direct detection and heterodyne detection techniques across various turbulence regimes. Furthermore, we extend our study and measure the BER performance of the FSO system using OOK modulated signal over different turbulence regimes using Gaussian and engineered beams. In conclusion, the paper demonstrates the feasibility and advantages of employing engineered beams for FSO communications, particularly in scenarios characterized by medium to strong turbulence levels.

Generic elbow prostheses often fail to meet the specific anatomical needs of patients, leading to high misalignment rates in Total Elbow Arthroplasty (TEA). This misalignment negatively impacts surgical efficiency, patient satisfaction, and critical functional outcomes, including range of motion and load-bearing capacity. Although personalized prostheses could address these challenges, current practices rely on ad-hoc intraoperative adjustments that frequently fall short of meeting individual anatomical requirements. A key obstacle to achieving truly personalized designs is the absence of a quantitative method for evaluating alignment. To tackle this issue, we developed a 3D computational model to simulate TEA and introduced the Alignment Improvement Metric (AIM), a novel quantitative tool for assessing prosthesis alignment. Using AIM, we designed three personalized prosthesis approaches optimized for individual, group, and cohort scenarios. Computational validation on 120 clinical participants demonstrated an average alignment improvement of up to 30% with just 3 prostheses, highlighting the potential of this AI-driven solution to enhance TEA outcomes and patient satisfaction.

This paper introduces a new portable impedance measurement system for Electrical Impedance Spectroscopy (EIS) and continuous impedance monitoring of various flexible-wearable electrodes and sensors. The system comprises a low-power, highly sensitive Electro-impedance Front End (EFE) and a Bluetooth Low Energy (BLE) microcontroller. Impedance measurements are conducted onchip and transmitted wirelessly via BLE to a nearby mobile device or PC application. As a case study, the implemented prototype interfaces flexible sensors designed for measuring changes in impedance. Preliminary results demonstrate the sensors ability to perform EIS and detect impedance variations caused by sensor stretching.

Spam SMS often appears similar to legitimate (ham) messages but contains potential threats or malicious links designed to hack personal information such as bank account details, passport details, etc. Many mobile users have been affected because they unknowingly access spam messages and provide information through the link given, and they have lost a huge amount of money. Many industries are working on this to prevent such spam messages from entering the user's inbox using advanced machine or deep learning techniques. Any Artificial Intelligence technique requires enough data to train the model to perform better during real-time testing. Recently, there have been enough benchmark spam SMS datasets available for English; however, there are fewer for Indian languages. This paper provides an overview of an SMSDHL dataset comprising Dravidian languages-Tamil, Telugu, Kannada, and Malayalam with Hindi and English. The dataset can be utilized for various applications in Information Extraction, Information Retrieval, and Natural Language Processing (NLP).

This paper proposes a deep joint source-channel coding (DJSCC) to minimize the age of information (AoI) for image transmission. A new content-based AoI metric called age of misclassified information (AoMI) is introduced to estimate the freshness of the information in an image classification system. AoMI is a critical metric in timely information delivery, measuring the age of the most recently received and correctly classified image at the receiver. The proposed system leverages a deep neural network at the transmitter to map image pixels directly to channel input symbols, eliminating the need for separate source and channel coding. At the receiver, the channel output is processed to perform image classification. To analyze the AoMI performance of the system, a stochastic hybrid systems (SHS) approach is employed. Closed-form expressions for the average AoMI (AAoMI) are derived, providing insights into the impact of system parameters on the AoMI. Simulation results demonstrate the effectiveness of the proposed DJSCC-based system in achieving lower AoMI compared to traditional separate source and channel coding schemes. The findings highlight the potential of deep learning techniques to maintain the freshness of the information in wireless communication systems. This work paves the way for the design of wireless communication systems that prioritize the freshness of delivered information-this is crucial in applications such as real-time monitoring, surveillance, and control systems.

Several lessons were learned from the post-analysis of the 2021 Winter Storm Uri’s devasting impact on Texas’ electricity grids. Among the lessons was the need for a greater granularity and flexibility by which Texas electricity utilities can shed load during emergencies as directed by the local Independent System Operator (ISO), while protecting supply to critical loads and minimizing impact on customers. This paper, following a review of principal lessons learned following the storm, summarizes the work, concepts, and findings from a transmission and distribution circuit segmentation study performed on a Texas electricity utility’s system.

Breast cancer is still one of the most common and deadly types of cancer that affect women worldwide, hence improvements in diagnostic techniques are required for early identification and treatment. This review paper discusses the latest developments in the identification and analysis of breast cancers in medical imaging, as well as the comparative effectiveness of different techniques for segmentation and machine learning algorithms. We systematically analyze research that use advanced machine learning techniques including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models in addition to more conventional segmentation techniques like thresholding, region-based, and edge detection methods. In comparison to traditional segmentation techniques, the synthesis of findings from the reviewed literature highlights the superiority of machine learning algorithms, especially deep learning techniques, in obtaining improved accuracy, sensitivity, and specificity in breast tumor detection. Moreover, combining segmentation methods with sophisticated machine learning models appears to be a viable way to improve tumor delineation and classification efficiency. In order to improve breast cancer diagnosis and treatment, this study intends to give researchers and medical professionals a thorough assessment of the advantages, disadvantages, and potential future directions of using these technologies.

This study investigates the mechanical properties of three common infill patterns-grid, gyroid, and cubic-constructed using fused filament fabrication. Test specimens were printed with polylactic acid, and stiffness, strength, and specific energy absorption were evaluated under compression across varying relative densities. The results demonstrate the superior performance of the grid pattern in terms of stiffness and energy absorption, although this advantage is direction-dependent. The gyroid pattern exhibited competitive energy absorption with improved isotropy, while the cubic pattern showed the lowest mechanical properties across all metrics. These findings provide actionable insights for optimizing 3D-printed components for load-bearing applications by balancing material efficiency and mechanical performance.

Tibetan's low-resource status has hindered its digital preservation, particularly in Handwritten Character Recognition (HCR). The field lacks foundational models aligned with modern technological advances as well as effective data augmentation methods. For online HCR, the primary challenge is appropriately leveraging temporal information. To address these challenges, we propose TSTN, a spatiotemporal network model tailored for Tibetan online HCR. TSTN excels at capturing both temporal and spatial features from online samples. Using the MRG-OHTC dataset with 583 Tibetan characters, experimental results demonstrate TSTN's superiority, achieving a highest accuracy of 89.26% and a stable average accuracy of 85.13%. To further mitigate data scarcity, we implement geometric data augmentation to enhance accuracy and validate TSTN's robustness in trajectory sequence feature extraction. Furthermore, we generate new samples by segmenting and recombining Tibetan character components, demonstrating the reliability and effectiveness of this approach in boosting recognition performance. Our structureaware data augmentation SplitMix methods further improve performance by a maximum of 1.75%. Our code will be made publicly available soon.

This paper presents the analysis and effects of frequency diversity on finite uniform circular array for generation of range dependent Orbital Angular Momentum (OAM) modes. The analysis reveals that the array factor expression in terms of Bessel's function for a circular array with finite no. of elements possess a phase error in the azimuthal domain at any transverse plane due to which the range dependency of OAM modes is affected. In this report a detailed analysis of the phase distribution on concentric circles for an exact array factor expression without considering any assumption or approximation has been carried out. Effect of changing parameters such as total number of elements and frequency increment value is analyzed and compared with the approximated array factor expression. Fourier analysis in the azimuthal domain reflects the presence of desired modes at a specific range for finite array cases. Generated phase and amplitude profile validate the occurrence of range dependent OAM modes at specified distance. Analysis of phase error for different frequency increment for finite no. of elements is also done. A prior knowledge of the available modes at a specified distance away from the antenna is crucial for improving the spectral efficiency of a communication system or radar.

Radar systems are increasingly critical in various applications, from automotive safety to air traffic control and military operations. However, radar interference remains a significant challenge, impacting the reliability and accuracy of radar systems. This review paper provides a comprehensive examination of radar interference mitigation techniques, emphasizing their practical applications, advantages, and limitations. Specific examples such as automotive radar experiencing mutual interference in dense traffic or weather radars facing clutter from wind turbines are discussed. We explore classical approaches such as filtering and time-domain gating, alongside advanced methods leveraging machine learning and adaptive signal processing. The discussion synthesizes current research trends and identifies gaps, offering insights for future development in the field.

Optimization techniques are pivotal in neural network training, shaping both predictive performance and convergence efficiency. This study introduces Foxtsage, a novel hybrid optimisation approach that integrates the Hybrid FOX-TSA with Stochastic Gradient Descent for training Multi-Layer Perceptron models. The proposed Foxtsage method is benchmarked against the widely adopted Adam optimizer across multiple standard datasets, focusing on key performance metrics such as training loss, accuracy, precision, recall, F1-score, and computational time. Experimental results demonstrate that Foxtsage achieves a 42.03% reduction in loss mean (Foxtsage: 9.508, Adam: 16.402) and a 42.19% improvement in loss standard deviation (Foxtsage: 20.86, Adam: 36.085), reflecting enhanced consistency and robustness. Modest improvements in accuracy mean (0.78%), precision mean (0.91%), recall mean (1.02%), and F1-score mean (0.89%) further underscore its predictive performance. However, these gains are accompanied by an increased computational cost, with a 330.87% rise in time mean (Foxtsage: 39.541 seconds, Adam: 9.177 seconds). By effectively combining the global search capabilities of FOX-TSA with the stability and adaptability of SGD, Foxtsage presents itself as a robust and viable alternative for neural network optimization tasks.

The present work explores the geomorphotectonics of the Kathiawar (Saurashtra)-Kutchch Outlier in Gujarat, India north of the SONATA lineament. Using spatial statistics, the author explores intricate links between spatially resolved data, reveal patterns and correlations that drive the region's geological history. The work addresses an underlying issue, i.e., that of finding the spatial position of entities under study, which is a central issue in spatial analysis. The methodology combines several spatial analytic methods in order to derive a detailed representation of the geomorphotectonics of the area. Results of this study would make an important contribution to the ongoing task of understanding the geological history of the Kathiawar (Saurashtra)-Kutchch Outlier, providing insight to the complex processes that have created this peculiar geological unit.

This study evaluates the effectiveness of a 6-hour Calculus for Engineering preparation course designed for high school seniors (Group A) and incoming first-year engineering students (Group B), utilizing advanced educational technologies. Unlike the traditional 9-15 hour approach at the Engineering Faculty of Rajamangala University of Technology Krungthep (RMUTK), which begins with foundational topics such as numbers, function values, limits, and continuity, our course focuses directly on differentiation using curve slopes and symbolic mathematics. The course employs GeoGebra and SymPy Live web applications to facilitate learning. Paired sample t-tests revealed significant improvements in both groups: Group A (n = 35) showed a mean increase of 4.97 (t(34) =-10.62, p < 0.001), and Group B (n = 27) showed a mean increase of 2.93 (t(26) =-4.05, p < 0.001). An independent samples t-test indicated a significant difference in improvement between the groups (t(60) = 2.47, p = 0.016). The results suggest that the 6-hour calculus preparation course effectively enhanced the calculus competency of both high school seniors and incoming first-year engineering students, with Group A showing greater improvement than Group B. This provides a more efficient alternative to the traditional methods used at RMUTK.

This paper introduces a novel approach for jointly performing image denoising and 3D point cloud reconstruction from single noisy medical images. By leveraging a joint learning framework, our model improves the accuracy and robustness of 3D reconstructions, particularly under noisy conditions. The model employs a two-branch architecture: a denoising branch utilizing convolutional layers with dropout regularization and a reconstruction branch that predicts 3D point clouds. Quantitative experiments on the LIDC-IDRI dataset demonstrate the model's superior performance compared to sequential baselines, achieving a 15% improvement in PSNR and a 20% reduction in Chamfer Distance. These results highlight the potential clinical applications of our framework in surgical planning and anatomical modeling.