We consider the evaluation of the Number of Degrees of Freedom (NDF) of the field radiated by square sources in the far zone. The analysis is performed by employing a Singular-Value Decomposition (SVD) of the radiation operator in the two-dimensional scalar case. To this end, we start the analysis from simple geometries like two parallel strips and angle strips where analytical results can be established. For sufficiently spaced strip sources, the NDF depends only on their total electrical length. Then results for square sources follow on the same line. Finally, the numerical investigation of the case of concentric square sources, leading to an NDF independent on the inner source, opens the way to the discussion of the NDF of a full square source. In this case, it results that it depends only on the source perimeter.

This is a review paper for the analysis and prediction of lung cancer drug resistance. We explore several computational methods, that can provide, biological insights for the analysis, visualization and prediction of lung cancer drug resistance.

Wireless capsule endoscopy is an established medical application for the examination of the gastrointestinal tract. However, the robust and precise localization of these capsules is still in need of further scientific investigation. This paper presents an innovative differential magnetic localization method for capsule endoscopy to prevent interference caused by the geomagnetic field. The effect of changing the orientation of the capsule on the localization process was also examined. Simulations using COMSOL Multiphysics with the superimposed geomagnetic field were performed. The Levenberg–Marquardt algorithm was applied in MATLAB to estimate the position and orientation of the capsule. Comparing the proposed differential method with the absolute magnetic localization method under ideal conditions, the mean position and orientation errors were reduced by three orders in magnitude to less than 0.1 mm and 0.1 ° respectively. Even if sensor non-idealities are considered, the simulationbased results reveal that our proposed method is competitive with state-of-the-art geomagnetic compensation methods for static magnetic localization of capsule endoscopes.The achieved localization accuracy by applying the differential method is not dependent on the rotation of the localization system relative to the geomagnetic flux density under the made assumptions and the impact of the magnet orientation is neglectable. It is concluded that the proposed method is capable of preventing all interference whose components are approximately equal at all sensors with identical orientation.

Adaptive sampling in a resource-constrained mobile robotic sensor network for monitoring a spatial phenomenon is a fundamental but challenging problem. In applications where a Gaussian Process is employed to model a spatial field and then to predict the field at unobserved locations, the adaptive sampling problem can be formulated as minimizing the negative log determinant of a predicted covariance matrix, which is a non-convex and highly complex function. Consequently, this optimization problem is typically addressed in a grid-based discrete domain, although it is combinatorial NP-hard and only a near-optimal solution can be obtained. To overcome this challenge, we propose using a proximal alternating direction method of multipliers (Px-ADMM) technique to solve the adaptive sampling optimization problem in a continuous domain. Numerical simulations using a real-world dataset demonstrate that the proposed PxADMM- based method outperforms a commonly used grid-based greedy method in the final model accuracy.

We demonstrate a broadband and continuously tunable 1×4 optical beamforming network (OBFN), based on the hybrid integration of indium phosphide (InP) components in the silicon nitride (Si3N4) platform. The photonic integrated circuit (PIC) comprises a hybrid InP-Si3N4 external cavity laser, a pair of InP phase modulators, a Si3N4 optical single-sideband full carrier (SSBFC) filter followed by four tunable optical true time delay lines (OTTDLs), and four InP photodetectors. The performance of the OBFN-PIC is experimentally characterized by measuring the link gain, noise figure, and spurious free dynamic range of the microwave photonics links. Moreover, we assess its beamforming capabilities assuming that the OBFN-PIC is part of a wireless system operating in the downlink direction and feeds a multielement antenna array. Using microwave signals at 5 and 10 GHz with quadrature amplitude modulation (QAM) formats at 500 Mbaud, we evaluate the performance of the OBFN-PIC under various configurations. An error-free performance is achieved for all the experimental cases validating the potential of the proposed OBFN-PIC for high-quality beamforming performance. To our best of knowledge, this is the first thorough performance evaluation of a fully integrated OBFN-PIC.

Electrical stimulation has shown to be a promising approach for promoting osseointegration in bone-anchored implants, where osseointegration defines the biological bonding between an implant surface and bone tissue. Bone-anchored implants are used in the rehabilitation of hearing and limb loss, and extensively in edentulous patients. Inadequate osseointegration is one of the major factors of implant failure that could be prevented by accelerating or enhancing the osseointegration process by artificial means. In this article, we reviewed the efforts to enhance the biofunctionality at the implant-bone interface with electrical stimulation using various approaches such as different electrode configurations, power sources, and waveform-dependent stimulation parameters tested in different in vitro and in vivo models. We reviewed and compared studies from the last 45 years and found nonuniform protocols with disparities in cell type and animal model, implant location, experimental timeline, implant material, evaluation assays, and type of electrical stimulation. The reporting of stimulation parameters was also found to be inconsistent and incomplete throughout the literature. Studies using in vitro models showed that osteoblasts were sensitive to the magnitude of the electric field and duration of exposure, and such variables similarly affected bone quantity around implants in in vivo investigations. Most studies showed benefits of electrical stimulation in the underlying processes leading to osseointegration, and therefore we found the idea of promoting osseointegration by using electric fields to be supported by the available evidence. However, such an effect has not been demonstrated conclusively nor optimally in humans. We found that optimal stimulation parameters have not been thoroughly investigated and this remains an important step towards the clinical translation of this concept. In addition, there is a need for reporting standards to enable meta-analysis for evidence-based treatments.

According to recent studies, unmanned aerial vehicles (UAV) can play a game-changing part in terms of cost reduction and speed increase to address the last-mile delivery (LMD) problem and also to attend emergencies. Last-mile delivery services are getting more and more relevant, especially when in times where social distance is required. Given this scenario, our paper introduces a cyber-physical (CPS) system roadmap propose applicable for last-mile delivery drones. The proposed CPS guidelines are based on the concept of system of systems to enable an emerging behavior towards smart cities’ governance. In this paper, we also discuss topics from air space control and reservation throughout communication infrastructure and decentralized control supported on a blockchain.

Convergence and generalization are two crucial aspects of performance in neural networks. When analyzed separately, these properties may lead to contradictory results. Optimizing a convergence rate yields fast training, but does not guarantee the best generalization error. To avoid the conflict, recent studies suggest adopting a moderately large step size for optimizers, but the added value on the performance remains unclear. We propose the LIGHT function with the four configurations which regulate explicitly an improvement in convergence and generalization on testing. This contribution allows to: 1) improve both convergence and generalization of neural networks with no need to guarantee their stability; 2) build more reliable and explainable network architectures with no need for overparameterization. We refer to it as step size self-adaptation.

The present work has been able to relate the magnetic field produced to the basic physical ionization process and derive a general expression for the same starting from the fundamental retarded scalar and vector potentials (Leinard-Weichert potentials).

Generic Angle of Arrival methods for indoor positioning are highly affected by specific antenna and environment scenarios through design impurities or multipathcomponent propagations. Here we acquired a large dataset of four different antenna designs in three different measurement environments with >140000 snapshots obtained from Bluetooth 5.1 receiver. Using the spatial power spectral densities of the PDDA angle of arrival algorithm as feature set for a small Random Forest model, we could show that angle estimation performances for all antennas in all measured environments were significantly improved (PDDA MAE >16 vs RF MAE < 3). Based on the small model size the proposed architecture can be implemented in microcontroller applications for super resolution angle of arrival applications.

Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques

The survivability of microelectronic devices in ionizing radiation environments drives spacecraft design, capability, mission scope, and cost. This work exploits the periodic nature of many space radiation environments to extend device lifetimes without additional shielding or modifications to the semiconductor architecture. We propose a technique for improving component lifetimes through reduced total-dose accumulation by modulating device bias during periods of intense irradiation. Simulation of this “dynamic biasing” technique applied to single-transistor devices in a typical low-Earth orbit results in an increase of component life from 114 days to 477 days (318% improvement) at the expense of 5% down time (95% duty cycle). The biasing technique is also experimentally demonstrated using gamma radiation to study three commercial devices spanning a range of integrated circuit complexity in 109 rad/min and 256 rad/min dose rate conditions. The demonstrated improvements in device lifetimes using the proposed dynamic biasing technique lays a foundation for more effective use of modern microelectronics for space applications. Analogous to the role real-time temperature monitoring plays in maximizing modern processor performance, the proposed dynamic biasing technique is a means of intelligently responding to the radiation environment and capable of becoming an integral tool in optimizing component lifetimes in space.

The paper address the tests required to be conducted for lubrication oil. It investigates the change of properties of new oil after it is being used for flushing. The recommendations of changing flushed oil or keeping it made on different analysis and studies, however, if the oil properties are not changing, then using the flushed oil is recommended as the new oil will not be as clean as flushed oil. The paper listed several oil cleaning methods and highlighted several causes which could result on bearing failure. The bearing lifetime was discussed and how the impact of different parameter could impact the bearing life span if it is generating additional forces, for example, due to the increase of friction force which contribute to the equivalent dynamic bearing load.

The module presents the design and development of the RFID based embedded the GSM module as the processors and the LCD as the display. This system aims at delivering a text message notification to the owner on an employee's entry. In addition, the system also sends a video footage recording to the owner on a guest entry along with text notification. The background of the study, the design approaches for this system and the preliminary evaluation of the prototype are presented in this paper. The evaluation results indicate that the system is useful and easy to use for security purposes.

Magnetic sensors developed for application in magnetoencephalography must meet a number of requirements; the main ones are compactness, sensitivity and response speed. We present a quantum optically pumped atomic sensor with cell volume of 0.5cm3 that meets these requirements and is operable in nonzero magnetic fields. The ultimate sensitivity of the sensor was estimated as (using the criteria of the ratio of the slope of the magnetic resonance signal to the shot noise spectral density) to be better than 5 fT/Hz1/2. The actual sensitivity, measured in a gradiometric scheme, reaches 13 fT/Hz1/2 per sensor. We also present a novel and fast algorithm for optimization of the geometric properties of non-zero field sensor array with respect to maximization of the information transfer rate for cortical sources.

This paper considers applying bit loading to multicarrier non-othogonal multiple access (NOMA) systems. The objective is to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally an NP-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, we propose in this paper an efficient bit loading algorithm for multicarrier NOMA systems and compares the complexity and throughput with OMA. The obtained results show that NOMA has virtual cognition and hybrid modes of operation, NOMA/OMA, that enables NOMA to outperform OMA by 100% for the two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high computational complexity of bit error rate (BER) computation for NOMA. The obtained results show that the NOMA throughput depends on which user is loaded first, and on the initially loaded bits.

Deep learning algorithms are a key component of many state-of-the-art vision systems, especially as Convolutional Neural Networks (CNN) outperform most solutions in the sense of accuracy. To apply such algorithms in real-time applications, one has to address the challenges of memory and computational complexity. To deal with the first issue, we use networks with reduced precision, specifically a binary neural network (also known as XNOR). To satisfy the computational requirements, we propose to use highly parallel and low-power FPGA devices. In this work, we explore the possibility of accelerating XNOR networks for traffic sign classification. The trained binary networks are implemented on the ZCU 104 development board, equipped with a Zynq UltraScale+ MPSoC device using two different approaches. Firstly, we propose a custom HDL accelerator for XNOR networks, which enables the inference with almost 450 fps. Even better results are obtained with the second method - the Xilinx FINN accelerator - enabling to process input images with around 550 frame rate. Both approaches provide over 96% accuracy on the test set.

This paper proposed a novel non-orthogonal multiple access scheme called CSMA that addresses some of critical shortcomings of NOMA that are the stumbling blocks for it to be considered an enabling technique for enhancing the capacity of 5G and 6G wireless communications. We provide some radical insights into the design of signals and detection technique for overcoming multi-user interference that makes it implementable in practice. We provide a sound theoretical analysis of its performance and validate the significance of numerical results.