This paper investigates the performance of a framework for low-outage downlink non-orthogonal multiple access (NOMA) signalling using a coordinated direct and relay transmission (CDRT) scheme with direct links to both the near-user (NU) and the far-user (FU). Both amplify-and-forward (AF) and decode-and-forward (DF) relaying are considered. In this framework, NU and FU combine the signals from BS and R to attain good outage performance and harness a diversity of two without any need for feedback. For the NU, this serves as an incentive to participate in NOMA signalling. For both NU and FU, expressions for outage probability and throughput are derived in closed form. High-SNR approximations to the outage probability are also presented. We demonstrate that the choice of power allocation coefficient and target rate is crucial to maximize the NU performance while ensuring a desired FU performance. We demonstrate performance gain of the proposed scheme over selective decode-and-forward (SDF) CDRT-NOMA in terms of three metrics: outage probability, sum throughput and energy efficiency. Further, we demonstrate that by choosing the target rate intelligently, the proposed CDRT NOMA scheme ensures higher energy efficiency (EE) in comparison to its orthogonal multiple access counterpart. Monte Carlo simulations validate the derived expressions.

The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What’s more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%.

A document by Antonio Ruggia . Click on the document to view its contents.

This is the final part of the three part series paper where the novel idea of representing flyback transformer losses as loss functions using hyperbolic secant spline interpolation,determination of the flyback transformer core saturation and instability in the control feedback loop from the loss function using Lyapunov method has been presented with scientific proof and validity. In this paper, the comparative analysis of 36 different flyback transformers from 30W to 85W is being presented.These two-winding transformers are operated in continuous/discontinuous mode,switched from MOSFET controlled by microcontroller from 60 kHz to 97 kHz. Eighteen wattage’s which are used internationally at different voltage and current ratings have been analyzed on fifteen different cores - EEL22, EE25, EEL25,EF25, EE28, EER28, EER28L, EE35, RM8/I, ETD29/16/10,ETD34/17/11, E25/13/7, PR30x19, ATQ27/18, RM12/I.

Corrected version of 19-12-10 (YR-Month-Day) of Part I 2D IPO’s for constant speed Lines, Curves, NURBS. Mainly corrected typos and some clearer formulations. The manuscript (44 pages) “Constant Speed Lines – Curves – NURBS Reference Pulse IPOs (Part I)” of Valere Huypens has been accepted for publication in “The International Journal of Advanced Manufacturing Technology” of Springer

Datasets containing class noise present significant challenges to accurate classification, thus requiring classifiers that can refuse to classify noisy instances. We demonstrate the inability of the popular confidence-thresholding rejection method to learn from relationships between input features and not-at-random class noise. To take advantage of these relationships, we propose a novel null-labelling scheme based on iterative re-training with relabelled datasets that uses a classifier to learn to reject instances that are likely to be misclassified. We demonstrate the ability of null-labelling to achieve a significantly better tradeoff between classification error and coverage than the confidence-thresholding method. Models generated by the null-labelling scheme have the added advantage of interpretability, in that they are able to identify features correlated with class noise. We also unify prior theories for combining and evaluating sets of rejecting classifiers.

To overcome the shortage of traditional flight mechanism, we propose a type of anti-gravity engine based on General Relativity. Natural anti-gravity phenomenon is found under special spacetime background by analysis geodesic motion of spacecraft and vanishment of Riemann curvature locally, which demonstrates the feasibility of artificial anti-gravity with the aid of electromagnetic field. To implement artificial anti-gravity under Schwarzschild background, we choose current loop as axisymmetric field source and obtain exact solution of Einstein-Maxwell field equation using Killing symmetry and Ernst generation technique. An implementation with superconductor shield is given according to the Meissner effect, calculation implies that existing nuclear fusion engineering capability could meet the strength requirements of the field source.

The present paper proposes a novel device defined as an intelligent electronic fuse (iFuse) meant to be connected in series with any current-bidirectional voltage-unidirectional active switch present in a given converter. The iFuse duty is to isolate its series- associated switch from the rest of the converter circuit immediately after detecting that said switch has failed in short circuit. Nonetheless, it maintains the reverse (free- wheeling) current path originally offered by the failed switch. The failure detection is performed when the failed switch causes a shoot-through event. Therefore, the iFuse is designed to be able to block the elevated current occurring in such event. The iFuse allows increasing the fault-tolerant capability and the reliability of power converters where such qualities are hindered by switch short-circuit failures, as in converters featuring parallelized switches, neutral-point-clamped multilevel topologies, or redundant legs. The feasibility of the iFuse device is verified through experimental tests, proving that the device is able to detect the failure of its associated switch and isolate it from the rest of the converter circuit in 6 μs, while stopping short-circuit currents of up to 1 kA without incurring in harmful di/dt values.

The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since, most of the people daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT enabled technologies in health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated.

The Split-Radix Fast Fourier Transform has the same low arithmetic complexity as the related Conjugate Pair Fast Fourier Transform. Both transforms have an irregular datapath structure which is straightforwardly expressed only in recursive forms. Furthermore, the conjugate pair variant has a complicated input indexing pattern which requires existing iterative implementations to rely on precomputed tables. It however allows optimization of the memory bandwidth as it requires a single twiddle factor load per radix-4 butterfly. In existing algorithms, this comes at the cost of using additional precomputed tables or performing recursive function calls. In this paper we present two novel approaches that handle both the butterfly scheduling and the input index generation of the Conjugate Pair Fast Fourier Transform. The proposed algorithm is cache-friendly because it is depth-first, non-recursive and does not rely on precomputed index tables. In order to achieve this, we relate the butterfly execution pattern of the Split-Radix and Conjugate Pair FFTs to the binary carry sequence. Based on this finding, we describe how common integer arithmetic and bitwise operations can be used to perform input reordering and depth-first traversal of the transform datapath with O(1) space complexity.

The Internet of Things (IoT) is permeating our daily lives where it can provide data collection tools and important measurement to inform our decisions. In addition, they are continually generating massive amounts of data and exchanging essential messages over networks for further analysis. The promise of low communication latency, security enhancement and the efficient utilization of bandwidth leads to the new shift change from Mobile Cloud Computing (MCC) towards Mobile Edge Computing (MEC). In this study, we propose an advanced deep reinforcement resource allocation and securityaware data offloading model that considers the computation and radio resources of industrial IoT devices to guarantee that shared resources between multiple users are utilized in an efficient way. This model is formulated as an optimization problem with the goal of decreasing the consumption of energy and computation delay. This type of problem is NP-hard, due to the curseof-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. Additionally, an AES-based cryptographic approach is implemented as a security layer to satisfy data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead by up to 13.2% and 64.7% in comparison with full offloading and local execution while scaling well for large-scale devices.

When designing a power or CPU constrained device where a four-axis robotic arm is required and access to the Robot Operating System (ROS) is not an option, finding an efficient state space controller for a four-axis arm can be an obstacle. In this paper, I explore a method to optimize the computing power required for a computer algebra system (CAS) to compute linear quadratic regulator (LQR) matrices by precomputing the gain matrix for different states. Example C++ code is provided on Github, along with ideas for further exploration.

Objective: The heart sound signals captured via a digital stethoscope are often distorted by environmental and physiological noise, altering their salient and critical properties. The problem is exacerbated in crowded low-resource hospital settings with high noise levels which degrades the diagnostic performance. In this study, we present a novel deep encoder-decoder based denoising architecture (LU-Net) to suppress ambient and internal lung sound noises. Methods: Training is done using a large benchmark PCG dataset mixed with physiological noise, i.e., breathing sounds. Two different noisy datasets were prepared for experimental evaluation by mixing unseen lung sounds and hospital ambient noises with the clean heart sound recordings. We also use the inherently noisy portion of the PASCAL heart sound dataset for evaluation. Results: The proposed framework showed effective suppression of background noises in both un?seen real-world data and synthetically generated noisy heart sound recordings, improving the signal-to-noise ratio (SNR) level by 5.575 dB on an average using only 1.32 M parameters. The proposed model outperforms the current state-of-the-art U-Net model with an average SNR improvement of 5.613 dB and 5.537 dB in the presence of lung sound and unseen hospital noise, respectively. LU-Net also outperformed the state-of-the-art Fully Convolutional Network (FCN) by 1.750 dB and 1.748 dB for lung sound and unseen hospital noise conditions, respectively. In addition, the proposed denoising method model improves classification accuracy by 38.93% in the noisy portion of the PASCAL heart sound dataset. Conclusion: The results presented in the paper indicate that our proposed architecture demonstrated a robust denoising performance on different datasets with diverse levels and characteristics of noise. Significance: The proposed deep learning-based PCG denoising approach is a pioneering study that can significantly improve the accuracy of computer-aided auscultation systems for detecting cardiac diseases in noisy, low-resource hospitals and underserved communities.

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

Recently, the development of pre-trained language models has brought natural language processing (NLP) tasks to the new state-of-the-art. In this paper we explore the efficiency of various pre-trained language models. We pre-train a list of transformer-based models with the same amount of text and the same training steps. The experimental results shows that the most improvement upon the origin BERT is adding the RNN-layer to capture more contextual information for short text understanding. But the conclusion is: There are no remarkable improvement for short text understanding for similar BERT structures. Data-centric method[12] can achieve better performance.

The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, which limits the size of the problem that can be solved, and also creates a high communication overhead since all the data is transferred to the central controller. These limitations are addressed by a proposed consensus-ADMM (alternating direction method of multiplier) based distributed optimization algorithm, which decomposes the optimization problem into sub-problems. The distribution feeder is partitioned into low coupling sub-networks/regions, which solves the sub-problem locally and exchanges information with the neighboring regions to reach consensus to solve for the global update. The information exchange and synchronization between sub-networks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a virtual aggregator for synchronization in distributed computation. The blockchain-based distributed optimization problem’s effectiveness is tested for 0.5-MW laboratory microgrid for one hour ahead and day-ahead for IEEE 123-bus and EPRI J1 test feeder, and results are compared with a centralized solution.

The increasing penetration of power-electronics interfaced resources brings new challenges regarding the small-signal stability of power systems. To address this issue, grid-forming controlled converters have emerged as an alternative to their conventional grid-following counterparts. This paper investigates the mechanisms behind converters driven stability and quantifies the stabilizing effect of grid-forming controls. The linearized state space model of different combinations of control strategies is analyzed in a multi-infeed system considering various operating points. Through a parametric sensitivity study and an examination of the participation factors of key eigenvalues of the linearized models, it is confirmed that grid-forming controls contribute to system stabilization. Moreover, this paper demonstrates that this stabilizing effect varies significantly depending on the specific grid-forming control implemented: whether a current control loop is used or not, notably impacts stability.

To Whom It May Concern Members of The Scientific Community Dear colleagues: I hope my letter finds you well. My name is REFAT MOHAMMED ABDULLAH ESHAQ (https://orcid.org/0000-0002-6448-4054). I have created a new algorithm, namely Proportional–Integral–Derivative–Cumulative Neural Networks (PIDC-NN), also called MinerNet. This algorithm work based on the PID controller that was created by the inventor Elmer Sperry in 1910. The code has been released on GitHub, see https://github.com/REFATESHAQ/PIDC-NN_MinerNet, and https://github.com/REFATESHAQ/PIDC-NN_MinerNet-Pro. The data (Coal and Gangue Infrared Images in BMP file format) has been released on IEEE Dataport. https://dx.doi.org/10.21227/v3m7-dk11 Although convolutional neural networks (CNNs) have achieved great successes in computer vision and pattern recognition, they have some shortcomings. In this article, a novel deep learning algorithm for binary classification is proposed to distinguish between coal and gangue infrared images. First, a Proportional–Integral–Derivative–Cumulative (PIDC) algorithm is created, which works based on the concept of a PID controller, in order to quickly extract features from infrared images and also to control the performance of Artificial Neural Networks (ANNs). Second, an ANN is designed for binary classification tasks (coal/gangue). Third, the PIDC algorithm and the ANN algorithm are connected to create a new learning system, namely, the Proportional–Integral–Derivative–Cumulative Neural Network (PIDC-NN), also called MinerNet. The proposed PIDC-NN architecture works without any traditional layers of deep CNNs such as convolutional layers, nonlinear activation functions layers, batch normalization layers, polling layers, or dropout layers. The results of the training and test processes demonstrate that the proposed PIDC-NN architecture alleviates the oscillation and overfitting problems of existing CNNs. Moreover, it solves the problem of dead neurons and big data that are required to train CNNs. Additionally, it provides robust and resilient control by tuning the gain coefficients KP, KI, and KD; the sampling time (dt); and arbitrary value (AV). A comparison between the proposed PIDC-NN architecture and state-of-the-art CNNs proves the effectiveness of the proposed method in accelerating both the training and test processes with competitive loss and accuracy. I emphasize that this algorithm (PIDC) that I created through my own effort, can provide optimal control to any system (not only ANN) whether linear or nonlinear with multiple inputs. Furthermore, this algorithm (PIDC) can control multiple complicated random inputs and make the system linear even with inputs, their amounts, and values are huge numbers (goes to infinity). The code is licensed under GNU Affero General Public License Version 3 (GNU AGPLv3); for more information, see https://www.gnu.org/licenses/agpl-3.0.en.html. The dataset (Coal and Gangue Infrared Images in BMP file format (Data.rar)) is licensed under a  Creative Commons Attribution 4.0 International (CC BY 4.0) License. For more information, see https://creativecommons.org/licenses/by/4.0/. This work has been supported by my livelihood and my family’s aid. The code and data is connected to article, entitled “Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)” TechRxiv (10.36227/techrxiv.23266301). Note that, the article is under review. The question is, why do I make contact with you?  As the average review time in top journals for the first round of submission may exceed one year, they encourage the authors to submit their papers to TechRxiv, IEEE’s preprint server, and publish the code in GitHub platform in order to quickly disseminate their work to a wide audience and gain community feedback. Therefore: · If you are a researcher in medicine, you can use and develop this code to detect the cancers Benign and Malignant Tumors by using the dataset of breast mammography images as inputs to the PIDC-NN algorithm. · If you are a researcher in Ecological and Environmental Engineering, you can use and develop this code to study geological and climate changes by using Remote Sensing Images as inputs to the PIDC-NN algorithm. · If you are a researcher in Mining Engineering, you can use and develop this code to detect and explore the resources by using images you collected as inputs to the PIDC-NN algorithm. · If you are a researcher in Mechanical or Industrial Engineering, you can use and develop this code to remove unwanted material from production lines of factories or to detect defects in equipment. Also who work in Fluid Mechanics and Gas Dynamics, you can use this code in order to study the behavior of fluid by using infrared images of the movement of the fluid through the pipes and heat exchangers. · If you are a researcher in Mechatronics Engineering, you can use and develop this code and insert it in Robots as visual sense algorithm such as Robotic Arms (Manipulators), Mobile Robots, and Drones and so on. Finally, there are a lot of benefits from this algorithm (PIDC) to control thermal, electrical, and mechanical processes as long as you understand how the PIDC algorithm deal with multiple random complicated inputs to produce one or multiple stable outputs. To clarify, the PIDC algorithm can not only apply to control ANN but also to any system needs to be stable. Yours faithfully ESHAQ ResearchGate: https://www.researchgate.net/profile/Refat-Eshaq Google Scholar: https://scholar.google.com/citations?user=_mmSzykAAAAJ&hl=en Author’s Email: refateshaq1993@gmail.com;  refateshaq@hotmail.com;  fs18050005@cumt.edu.cn;