[ACCEPTED FOR PUBLICATION IN THE DAIS JOURNAL OF SCIENCE 2023] Intercity travel optimization has always been a goal of every government. Such an optimization would allow efficient and economical development. Optimizing travel would facilitate transfer of ideas and resources and promote economic growth. As such, travel can be optimized on two fronts: cost of arranging for that travel and the ease of travel i.e., the length/time of travel for a person to go from any one city to any other. This study proposes a methodology of optimizing travel pathways in any circumstance through genetic algorithm metaheuristics and network theory representations. This study demonstrates the same to optimize inter-city travel between the 25 cities with the highest population in India.

A computer program was built to model a random walk that represents utilization of a limited set of items by a group of servers for  their customers. An example is the use of ventelators (items) for very sick patients (customers)  by hospitals (servers). Some hospitals amy run out of ventilators while others may have extras if a common pool of ventilators is not employed. The simulation showed a 5% improvement in one of the cases studied. A theoretical model was developed that displayed very accurate prediction of the simulation results.

A lightweight cylindrical Frequency Selective Surface (FSS) using high-pass inductive mesh grid is proposed as a radiofrequency (RF) shield for solenoid coils at 2.84 MHz working in a portable MRI system with a transversal B0-field averaged at 67 mT. Using a solenoid coil of 60 mm diameter as an example, the proposed FSS shield shows good shielding effectiveness at different wall-to-wall distances between the coil and the shield (5-25 mm). It also shows less compromise in B1-sensitivity when compared to the coil with a copper shield. The effects of the FSS shield were examined numerically and validated experimentally. When the wall-to-wall distance is 5 mm, the solenoid coil with the proposed FSS shield is compact, lightweight, and shows B1-sensitivity of more than 50% higher than the copper counterparts with comparable shieding effectiveness. Compared to an unshielded coil, it shows a wider bandwidth which benefits the excitation of an MRI system that has less homogeneous fields and a smaller inductance which indicates less trapping of energy and thus can enable different pulse sequence programming by taking this approach. This study shows that an FSS shield with inductive mesh grids around a solenoid coil is a promising approach for compact shielding without a big shielding box. It will contribute to the compactness of a portable MRI system.

We present a novel theory of the microstructure of deep neural networks. Using a theoretical framework called \textit{statistical teleodynamics}, which is a conceptual synthesis of statistical thermodynamics and potential game theory, we predict that all highly connected layers of deep neural networks have a universal microstructure of connection strengths that is distributed lognormally ($LN (\mu, \sigma$)). Furthermore, under ideal conditions, the theory predicts that $\mu$ and $\sigma$ are the same for all layers in all networks. This is shown to be the result of an arbitrage equilibrium where all connections compete and contribute the same effective utility towards the minimization of the overall loss function. These surprising predictions are shown to be supported by empirical data from six large-scale deep neural networks in real life. We also discuss how these results can be exploited to reduce the amount of data, time, and computational resources needed to train large deep neural networks.

This paper presents a novel method for solving the inverse kinematic problem of capturing human reaching movements using a dynamic biomechanical model. The model consists of rigid segments connected by joints and actuated by markers. The method was validated against a rotation matrix-based method using motion capture data recorded during reaching movements performed by healthy human volunteers. The results showed that the proposed method achieved low errors in joint angles. The angles were comparable to those calculated using the standard marker-based method. The proposed bioinspired method can be used in real-time medical applications, such as rehabilitation, assessment, and prosthesis/orthosis control.

This study demonstrates the utility of combining different methods of Multicriteria Decision Analysis (MCDA) for selecting Unmanned Aircraft (UA) in Electronic Warfare (EW) applications. We begin by emphasizing the significance of EW in military operations and the critical importance of choosing the most suitable UAs for this dynamic arena. Our primary goal is to identify the optimal UA among various options, meticulously considering distinct EW criteria. We implement three MCDA techniques: the Analytic Hierarchy Process (AHP), AHP-Gaussian, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Notably, these methods consistently converge in favor of Alternative 4, substantiating the robustness and minimizing subjective bias. These findings hold profound implications for strategic decision-making within EW operations and offer a sturdy framework for UA selection. This study not only advances the understanding of UA selection within EW but also underscores MCDA’s enduring value as a potent tool for guiding strategic choices. We anticipate that this work will inspire future research in UA selection, including the exploration of more intricate aircraft and the optimization of EW capabilities through sensor allocation and radiation-absorbing materials. Effective UA integration into EW is pivotal for modern military success, and our research provides a comprehensive roadmap to achieve this objective.

We Present a new architecture of the 3D SAG wireless access networks for uplink communication between IoRT and LEO satellite, utilizing several multi-antenna UAV relays. The most important structural features of this network are as follows: 1) Due to the prominence of multi-antenna systems technology as a promising solution to achieve advanced telecommunication systems, multi-antenna UAV relays are considered in our system model. These relays have the capability to communicate with each other using singular value decomposition (SVD) beamforming technique. 2) To control the number of remote connections, only selected UAVs act as gateways to connect to the satellite. 3) The proposed architecture incorporates FSO space communication links to address limitations of the RF spectrum resource and achieve higher data rates for air-space links. We formulate a new joint optimization problem of gateway selection, channel allocation, UAV deployment, and UAV power allocation for multi-tier communication in order to minimize the weighted sum of the number of  gateways and the total UAV power consumption. Our proposed solution includes a two-step approach using constrained k-means clustering for channel allocation between IoRT devices and UAVs, and UAV location, and an algorithm inspired by simulated annealing (SA) for gateway selection, channel allocation between UAVs and gateways, and UAV transmit power optimization. The UAV power optimization is solved using the successive convex approximation (SCA) method. Various simulations are performed to evaluate the performance of the proposed system model and the proposed optimization schemes. The results demonstrate the effectiveness of our proposed scheme in achieving optimal clustering, UAV deployment, and gateway selection. The required number of gateways for different scenarios is also determined. Notably, by implementing the SA algorithm to jointly optimize UAV transmit power, gateway selection, and UAV to gateway channel allocation, our proposed scheme achieves an average performance improvement of 48% compared to the case where only clustering is performed. The results also highlight the impact of the number of UAVs and the number of their antennas on the system performance.

The rapid advancement of blockchain technology has permeated various sectors, heralding a new era of transparency, security, and decentralized control. Education, a cornerstone of societal development, is not exempt from this technological tide. This research embarks on a deductive exploration of the integration of blockchain technology within the educational landscape, aiming to unveil the potential benefits and challenges therein. Through a meticulous review of existing literature, theoretical frameworks, and empirical analysis, this study delineates the transformative potential of blockchain in enhancing deductive reasoning and fostering a conducive learning environment. The research employs a mixed-method approach, encompassing surveys, interviews, and case studies across diverse educational settings. Findings reveal that blockchain technology can significantly bolster the integrity and accessibility of educational resources, streamline administrative processes, and enhance deductive reasoning among learners. Moreover, the study unveils the necessity of a robust regulatory framework and stakeholder education to harness the full potential of blockchain technology in education. The discourse extends to provide pragmatic recommendations for educators, policymakers, and blockchain developers to collaboratively nurture a blockchain-augmented educational ecosystem. This work aspires to contribute to the burgeoning body of knowledge at the nexus of blockchain technology, deductive reasoning, and modern education, propelling further scholarly inquiry and practical initiatives in this multidisciplinary domain.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique used in the treatment of several neurological conditions. The dosage parameter for TMS protocols is the resting motor threshold (RMT) which has been shown to vary between participants with limited understanding. The goal of this study was to investigate how white matter-derived fiber tracts integrated into finite element analysis simulations influence TMS response in the form of RMT. Ten healthy participants were included in this study who underwent TMS, diffusion tensor imaging, and structural magnetic resonance imaging (MRI). Anatomically accurate head models were created and fiber tracts were extracted from Diffusion tensor imaging and integrated into these head models before finite element analysis simulations were performed to model the effects of empirical TMS. Linear mixed effects models were used to evaluate how the induced electric field strength on the fiber tracts (EFSTract) influenced RMT. We found the induced electric field strength along fiber tracts did influence RMT, however the effect of this relationship on RMT is not clinically relevant due to its small magnitude. This suggests finite element analysis of the fiber tracts is not meaningful when tracts are considered a homogenous material and thus lacking physiology. However, tractography provides a valuable framework within which to organize physiological models of signal transmission, and it is likely a combination of this approach with more physiologically detailed modeling would provide more accurate RMT prediction.

A document by Yu Dian Lim . Click on the document to view its contents.

This paper delves into a novel compressed sensing (CS) strategy for estimating the directions of incoming signals in a coherent environment using a lens antenna array (LAA). In comparison to the well-known subspace-based algorithm family, CS techniques, such as the conventional orthogonal matching pursuit (COMP), can effectively address the direction-of-arrival (DoA) estimation problem requiring prior knowledge about the number of signals and offer lower complexity. However, they are susceptible to noise and can be adversely affected by multipath distortion. Leveraging the energy-concentrating property of an LAA, we first introduce the signal covariance matrix-based OMP (SCM-OMP) method that enhances the angular estimation performance, even in low-SNR regions. Subsequently, we propose the multiple sub-covariance matrices-based OMP (MSCM-OMP) to achieve a reduction in computational complexity. Simulation results demonstrate that the MSCM-OMP scheme also outperforms other high-resolution DoA estimation methods.

This research presents an efficient fruit monitoring and orchard management system using RFIP (Radio Frequency IDentification) technology. The new method is expected to replace the existing paper-based manual process. Each tree is attached with a DFID tag, and each fruit, mango in this research is attached with a RFID tag as well. An RFID reader was developed for scanning the RFID tags and sending the treefruit group data to a cloud database. The cloud server runs the process and inventory management. The RFID reader is multi-functional and it is used by the workers in carrying out tasks such as covering fruits with protective bags at the early stage, spraying, and plucking. In the cloud-based management system, the workers are efficiently assigned to these tasks. Each worker has a field device that shows the assigned tasks. When the tasks are accomplished, the status is updated in the cloud database. Using this system, each fruit is monitored from the initial covering state to the final plucking state including timely spraying of agro-chemicals.

The development of advanced prosthetic devices that can be seamlessly used during an individual’s daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including a feedforward neural network with one hidden layer, a feedforward neural network with multiple hidden layers, a temporal convolutional network, and a convolutional neural network with squeeze-and-excitation operations were evaluated in real-time, human-in-the-loop experiments with able- bodied participants and an individual with an amputation. Our results demonstrate that deep learning architectures outperform shallow networks in decoding motor intent, with representation learning effectively extracting underlying motor control information from EMG signals. Furthermore, the observed performance improvements by using deep neural networks were consistent across both able-bodied and amputee participants. By employing deep neural networks instead of traditional machine learning approaches, more reliable and precise control of a prosthesis can be achieved, which has the potential to significantly enhance prosthetic functionality and improve the quality of life for individuals with amputations.

Any endeavors to explain the network behavior, always falls in the network optimizer explanation trap and gets into the complication of algorithm math operation loop without reaching the final decision of how it really works. Weight divergence optimizer, which is a subcomponent of Synthetic Neural Network explains the inner works of neural network in a pure logical operation using only the dot product multiplication to illustrate this behavior and proposed a formula to calculate bias and weights. The new method uses the network divergence theory instead of trial-and-error method by calculating the maximum variation value between weight and its interior class patterns compared with the minimum variation value of the same weight with exterior patterns of other classes, the difference between the two values used in the formula. Synthetic Neural Network deals with many challenges starting from how Neural Network works and get its result, extending to reducing the size of training dataset, huge memory consumption, extensive processor calculation and creditability. The network output shows a promising result in testing and validation stage, it grants an accuracy of 90% to 75% with two and nine classes when using USPS dataset. Also, it produces accuracy of 95% to71% when using RMNIST expanded with the same classes. In audio patterns with MFCC feature extractions it gives accuracy of 73% with 10 classes. All the training operation performed between 0.5 to 15.5 seconds. The proposed method uses the lowest number of neurons that ever used with two levels of Neural Network, that helps to reduce the network size, discards redundant patterns, removes corrupted inputs and makes the network to converge in a few seconds with the lowest number of input dataset used

A document by Belsty Derseh . Click on the document to view its contents.

Quantitative characterizations and estimations of uncertainty are of fundamental importance for machine learning classification, particularly in safety-critical settings such as the military battlefield where continuous real-time monitoring requires explainable and reliable scoring. Reliance on the maximum a posteriori principle to determine label classification can obscure a model’s certainty of label assignment. We develop quantitative scores of certainty and competence based on predicted probability estimates as an effective tool for inferring the verity of positives across different data modalities and architectures. Our theoretical results establish that competent models have distinct distributions of certainty for true and false positives. Our empirical results bear out that there are distinct distributions of certainty scores on training and holdout data, as well as data that is a priori out-of-distribution. Further, we find that the most reliable test for out-of-distribution data is to compare the global True positive certainty score distribution against test data. At least 92.3% of out-of-distribution are successfully identified this way across our two experimental modalities at the tranche level. Further, 100% of the out-of-context images are identified as out-of-distribution using the stochastic form of our out-of-distribution detection test across all five stochastic variants of the ResNet models. Consequently, we find that the use of our certainty framework provides a robust means of detecting out-of-distribution inputs, while also serving as a reliable mechanism for comparing model quality of accurately distinguishing between true and False positives, particularly in safety-critical contexts.

GAN-based tabular synthesis methods have made important progress in generating sophisticated synthetic data for privacypreserving data publishing. However, existing methods do not consider explicit attribute correlations and property constraints on tabular data synthesis, which may lead to inaccurate data analysis results. In this paper, we propose a Controllable tabular data synthesis framework with explicit Correlations and property Constraints, namely C3-TGAN. It leverages Bayesian networks to learn explicit correlations among attributes and model them as control vectors. Such control vectors can guide C3-TGAN to generate synthetic data with complicated property constraints. By conducting comprehensive experiments on 14 publicly available benchmark datasets, we showcase C3-TGAN's remarkable performance advantage over state-of-the-art methods for synthesizing tabular data.

The demand for precise brain tumor detection and analysis in cancer diagnosis and treatment has escalated, necessitating extensive and diverse medical datasets. However, the acquisition of labeled tumor images is impeded by intricate dissimilarities between tumor and non-tumor regions, compounded by the diverse spectrum of brain tumor types. This study addresses a critical research gap by proposing a novel approach for robust and trustworthy brain tumor MRI image synthesis. Leveraging label conditional diffusion models, our approach adeptly captures specific tumor features, resulting in the generation of high-quality tumor images. Additionally, a trustworthiness control mechanism, employing evaluation metrics such as Fréchet Inception Distance (FID) and Inception Score (IS), ensures generated MRI brain tumor images meet exacting quality, accuracy, and clinical relevance criteria. Despite potential limitations in sharpness due to resolution constraints, our framework excels in overcoming challenges posed by image similarities and variations in brain tumor images. This surpasses the performance of conditional generative adversarial networks (GANs) by producing realistic details and textures. This contribution significantly advances the field of brain tumor image synthesis.