Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of Aloha and CSMA for LoRaWAN Network

Due to characteristics, like low power usages, long range and low cost, Long Range Wide Area Network (LoRaWAN) is the finest choice for many Internet of Things (IoT) applications. The Long Range Wide Area Network (LoRaWAN) reconcile with simple MAC layer protocol called Aloha which helps to reduces the battery lifetime but all the transmissions in the network will occur at same time which increases collisions between the packets and decreases network performance heavily. Because of this we evaluated the performance of LoRaWAN with Carrier Sense Multiple Access (CSMA) for coalition avoidance. We have simulated LoRaWAN-Aloha and LoRaWAN-CSMA/CA in multiple networking conditions by changing network load, spreading factors, distance between gateway and sensors to investigate the network performance and energy consumption. The simulation result let us evaluate and compare between CSMA and Aloha on collision ratio, network success probability and energy consumption per node. The performance evaluation shows, the LoRaWAN-CSMA/CA could be better choice, compare to LoRaWAN-Aloha, for large IoT network with strong scalability requirement. Whereas LoRaWANAloha would be a better choice for a small scale and static IoT network.
Variant Characteristic Mode Equations Using Different Power Operators for Material Bodies

This paper is concerned with the electromagnetic theory of characteristic modes for general homogeneous material bodies using surface integral equations (SIEs), which are computationally more efficient than using volume integral equations (VIEs). However, the generalized eigenvalue equations (GEEs) based on SIEs are prone to generate spurious or extra modes, unlike their counterparts based on VIEs. In this work, several variant SIE-based GEEs are proposed to eliminate the spurious/extra modes and improve the numerical performances. These variant GEEs share a common point that the reactive power operator related to the material domain should be removed from the left-hand sides of GEEs by explicitly enforcing the boundary condition, because it seems sensitive to incur unwanted modes. These variant GEEs are diverse in choosing the active power operator related to the material domain on the right-hand sides to suit different medium parameters, which even can be dropped directly for lossless cases. Numerical results show that these variant GEEs do not generate spurious/extra resonant modes in observed frequency range, and at least one of the variants would have acceptable numerical accuracy.
Heating Effect of the Electromagnetic Interaction with Metallic Microparticles

The heating effect of electromagnetic radiation is of practical interests because of the possibility of heating small particles like metallic microparticles, e.g. metallic microparticles, opening prospects for many applications such as drug delivery and remote triggering of electromagnetic microsystems. In this work, the interaction of electromagnetic waves with metallic microparticles has been investigated using numerical simulations. COMSOL Multiphysics is used to demonstrate the thermal effects of the electromagnetic radiation at 2.4 GHz on metallic microparticles of iron powder, iron, aluminum, copper and silver. The results demonstrated that the rise in temperature around the microparticles is proportional to the input power. The total rise in maximum temperature was 33.83 K near the iron powder microparticles at 1W power in 30 minutes. Further investigation also revealed the contribution of both the electric and magnetic field components of the electromagnetic field towards the total heating effect.
Rain Drop Shapes and Scattering Calculations: A Case Study using 2D Video Disdrometer Measurements and Polarimetric Radar Observations at S-band During Hurricane Dorian Rain-Bands

On 9 September 2019, rain-bands of category-1 Hurricane Dorian passed over a ground instrumentation site in Delmarva peninsula, USA. Drop shapes derived from 2D Video Disdrometer measurements at this site were used to compute the S-band radar cross sections (RCS) for horizontal and vertical polarizations for each drop with equi-volume diameter > 2 mm. These are combined with RCS for the smaller drops assuming equilibrium shapes. Radar reflectivity (ZH) and differential reflectivity (ZDR) are calculated for each of the 3 minutes throughout the event which lasted for more than 8 hours. These are compared with simultaneous observations from an S-band polarimetric radar 38 km away. The comparisons highlight the impact of large amplitude drop oscillations on ZDR.
Non-invasive Thermometry During Hyperthermia Using Differential Microwave Imaging Approach

The evaluation of non-invasive microwave thermometry using a differential microwave imaging approach is investigated in an experimental setup. The setup consists of a monopole microwave tomographic system, a muscle phantom, and a circulation/heating system. The saline solution is used as the heated medium inside the muscle phantom. Based on Born Approximation and regularization by TSVD we reconstructed the distribution of both dielectric parameters, relative permittivity and electrical conductivity inside the investigation area. A promising agreement between measured and reconstructed electrical conductivity was achieved and some specifics for future improvement of microwave thermometry were identified.
Market Making With Signals Through Deep Reinforcement Learning

Deep reinforcement learning has recently been successfully applied to a plethora of diverse and difficult sequential decision-making tasks, ranging from the Atari games to robotic motion control. Among the foremost such tasks in quantitative finance is the problem of optimal market making. Market making is the process of simultaneously quoting limit orders on both sides of the limit order book of a security with the goal of repeatedly capturing the quoted spread while minimizing the inventory risk. Most of the existing analytical approaches to market making tend to be predicated on a set of strong, naïve assumptions, whereas current machine learning-based approaches either resort to crudely discretized quotes or fail to incorporate additional predictive signals. In this paper, we present a novel framework for market making with signals based on model-free deep reinforcement learning, addressing these shortcomings. A new state space formulation incorporating outputs from standalone signal generating units, as well as a novel action space and reward function formulation, are introduced. The framework is underpinned by both ideas from adversarial reinforcement learning and neuroevolution. Experimental results on historical data demonstrate the superior reward-to-risk performance of the proposed framework over several standard market making benchmarks. More specifically, the resulting reinforcement learning agent achieves between 20-30% higher terminal wealth than the benchmarks while being exposed to only around 60% of their inventory risks. Finally, an insight into its policy is provided for the sake of interpretability.
A Parcel-Based Deep-Learning Classification to Map Local Climate Zones From Sentinel-2 Images

Local climate zones (LCZ) describe urban surface structures, supporting studies of urban heat islands, sustainable urbanization, and energy balance. The existing studies mapped LCZs from satellite images using scene-based classification, which trained deep-learning classifiers by labeled image patches, segmented satellite images into patches by sliding windows to match the size of training data, and finally classified the segmented patches to obtain LCZ maps. However, sliding windows are different from the real footprints of LCZs, which leads to large errors in classification. To address this problem, this article proposes a parcel-based method for LCZ classification using Sentinel-2 images, road networks, and elevation data. First, the Sentinel-2 images are segmented by the road network to obtain the land parcels as classification units. Second, each image parcel is standardized to match the training dataset, So2Sat LCZ42. Third, the trained convolutional neural network (CNN) is used to classify the standardized parcels into LCZs. Finally, the building height information derived from elevation data is used to refine the LCZs by a rule-based classifier. The results of the four test sites show that the overall accuracy of our method is 0.75, higher than the sliding-window-based method's accuracy of 0.47. Additional simulation experiments demonstrated that parcels derived from road networks can reduce the mixture effect in image patches, and parcel standardization can ensure the transferability of the CNN model trained by regular image patches. Considering that the road network and elevation data are widely available, the proposed method has the potential of mapping LCZs in large areas.
An Enhanced Data Plane for Network Event Processing in Software Defined Networking

Software-defined networking (SDN) is one of the key enablers of 5G networks. With a 5G application, the interaction between the SDN controller and data plane nodes will affect the performance requirements in delay-sensitive scenarios (e.g., URLLC) and increase the network signalling overhead in other scenarios (e.g., mMTC). However, the current data plane of SDN has no capability to handle network events unless inquiring the controller. To solve this problem, we propose an enhanced SDN data plane model to enable the network with data plane response capability for network events. Specifically, the delegation of various control plane functions along with the data path can enable the delay-sensitive network event processing without the interaction with the SDN controller. This can in turn minimize the interaction between the controller and data plane nodes. Experimental results show that the proposed enhanced SDN data plane can reduce the delay and signalling overhead during network events processing, especially in delay-sensitive applications of 5G networks.
Higher-Order Spectral Analysis of Radio Pulsar Bursts using MeerKAT

The discovery of new radio pulsars is a key science goal for many next generation radio telescopes. The achievement of this objective relies both on the use of improved instrumental sensitivity and also on novel signal processing techniques. The MeerKAT is an example of a next generation radio telescope, emissions from a pulsar are recorded using one of its receivers and are tested for activity in the bispectrum. The test confirms bispectral activity and serves as a proof of concept for future pulsar detection algorithms based on non-Gaussian statistics.
Measurement Method of Akkermansia Muciniphila by Graphene-Based Transistor for Diseases Diagnosis

<italic>Akkermansia muciniphila</italic>, as a mucin-degrading bacterium of the phylum Verrucomicrobia, associates with several disease states for its’ concentration in the human intestinal tract. To accuracy and rapidly measure the concentration of <italic>A. muciniphila</italic> would be a promising candidate for rapid diagnosis of these diseases. Herein, we present a new method to label-free acquire the concentration of <italic>A. muciniphila</italic> by designing a graphene-based transistor. Relative permittivities of the transistor were varied by adding <italic>A. muciniphila</italic> with different concentrations. Various conductance changes, which were recorded by the graphene-based transistor, were generated, and, in turn, characterized the concentrations of <italic>A. muciniphila</italic>. The experimental results indicated that the approach can significantly distinguish different concentrations of <italic>A. muciniphila</italic> and has certain fault tolerance when the samples containing a small amount of other intestinal bacteria and impurities. The work offers a general measurement method of the concentration of <italic>A. muciniphila</italic>, and provide a novel and promising method for the diagnosis of several diseases associating with <italic>A. muciniphila</italic>.
MAMAP: Congestion Relieved Memetic Algorithm based Mapping Method for Mapping Large-Scale SNNs onto NoC-based Neuromorphic Hardware

Spiking neuron networks (SNNs) have been widely adopted to solve complex tasks such as pattern recognition, image classification, and natural language processing. Simulating SNNs on many-core neuromorphic hardware has been proved to be powerful and energy-efficient. Constrained by hardware resources, it is difficult to map a complete SNN on a single neuromorphic core. The SNNs need to be segmented and placed in different neuromorphic cores. The Network-on-Chip (NoC) connection strategy for interconnecting multiple neuromorphic cores has been widely used in modern neuromorphic hardware. However, the enormous inter-core communication from large-scale SNNs will lead to congestion of NoC and dramatically reduces performance. In this paper, we propose a congestion relieved memetic-algorithm-based mapping method (MAMAP) to find an efficient mapping solution that minimizes the latency and power consumption of neuromorphic hardware by maximizing bandwidth utilization. Besides, our proposed memetic algorithm combining the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the TABU, can find the best mapping solution fast. Compared with the state-of-art SpiNeMap, the experimental results show that MAMAP can reduce the average latency by 63% and average energy consumption by 69%.
Relating OCEAN (Big Five) to Job Satisfaction in Aviation

this paper examines the relationships between job satisfaction and selected personality traits. The main task of this study is to emphasize the role of personality traits in the employee’s job satisfaction and to study the impact of personality traits such as neuroticism, extraversion and psychoticism on job satisfaction. The variables include big five personality traits and job satisfaction. The main focus of this work reveals that there is no substantial link within the following big five factors and the job satisfaction. The above-mentioned factors would importantly impact on job satisfaction. The examination of linkage between factors like neuroticism, extraversion, psychoticism and job satisfaction was carried out using the correlation analyzing. This study was conducted to collect the main data using the purposive sampling method. The distinctive feature of this study is the collection, processing and analysis of consolidated information from personal questionnaires depending on the type of professional activity of the sixty employees in aviation industry. Two questionnaires i.e. Eysenck Personality Questionnaire-revised (EPQ-R) and Job Satisfaction Survey were used to make an assessment of the impact of personality traits on the overall level of job satisfaction. The future impact of this study can support to increase the productivity level of the employees and also, spread awareness about the positive impact of extraverted personality traits on the job satisfaction.
Bias-Compensated Sign Algorithm for Noisy Inputs and Its Step-Size Optimization

Employing the traditional least-mean-square (LMS) algorithm to estimate the weight vector of an unknown system will result in an estimation bias when the input signal of the adaptive filter is corrupted by noise. This paper proposes a bias-compensated sign algorithm (BC-SA) to address this problem. Specifically, an unbiasedness condition is employed to develop a compensation term for the classical sign algorithm (SA) to reduce the estimation bias caused by noisy inputs. The proposed BC-SA can not only reduce the estimation bias but also exhibit robustness against impulsive noise. Then the mean and mean-square performance of the BC-SA is analyzed based on Price's theorem under some frequently used statistical assumptions. Moreover, the step-size of the BC-SA is optimized based on the developed theoretical mean-square deviation (MSD). Simulation results are finally provided to evaluate the convergence performance of the proposed algorithm and to examine the theoretical findings.
Micro Humidity Sensor Based on a GaN Chip With Silica Opal

The fabrication of a micro humidity sensor based on a GaN chip with silica opal is reported. The GaN chip containing InGaN/GaN multi-quantum well provides the two key functions of light emission and detection and the emitted light can be directly coupled into and out of the humidity-sensitive opal through the transparent sapphire substrate. The novel chip-scale integration scheme fully eliminates the complex assembly of external optical elements. The measured photocurrent signal quantitatively reflects the humidity change and has a linear relation of 0.24 $\mu \text{A}$ /% over a wide humidity range of 10-90 %. The developed micro-sensor possesses the advantages of compact size, low cost, high repeatability, and ease of integration and operation, which paves the way for its widespread adoption in humidity sensing.
Dataset and Network Structure: Towards Frames Selection for Fast Video Deblurring

Beyond the underlaying unrealistic presumptions in the existing video deblurring datasets and algorithms which presume that a naturally blurred video is fully blurred. In this work, we define a more realistic video frames averaging-based data degradation model by referring to a naturally blurred video as a partially blurred frames sequence, and use it to build REBVIDS, as a novel video deblurring dataset to close the gap between naturally blurred and synthetically blurred video training data, and to address most shortcomings of the existing datasets. We also present DeblurNet, a two phases training-based deep learning model for video deblurring, it consists of two main sub-modules; a Frame Selection Module and a Frame Deblurring Module. Compared to the recent learning-based approaches, its sub-modules have simpler network structures, with smaller number of training parameters, are easier to train and with faster inference. As naturally blurred videos are only partially blurred, the Frame Selection Module is in charge of selecting the blurred frames in a video sequence and forwarding them to the Frame Deblurring Module input, the Frame Deblurring Module in its turn will get them restored and recombine them according to the original order in a newly restored sequence beside their initially sharp neighbor frames. Extensive experimental results on several benchmarks demonstrate that DeblurNet performs favorably against the state-of-the-art, both quantitatively and qualitatively. DeblurNet proves its ability to trade between speed, computational cost and restoration quality. Besides its ability to restore video blurred frames with necessary edges and details, benefiting from its small size and its video frames selection integrated mechanism, it can speed up the inference phase by over ten times compared to existing approaches. This project dataset and code will be released soon and will be accessible through: https://github.com/nahliabdelwahed/Speed-up-video-deblurring-
Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants

Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently. In this study, we investigated a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory, involving healthy adults. Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation, as neurofeedback, yielding a strong targeting effect. In the experiment, 10 participants (test group) received three cognitive training sessions of 15 min using fNIRS-based frontoparietal FC as neurofeedback, and another 10 participants served as the control group. Frontoparietal FC was significantly increased in the test group (<inline-formula> <tex-math notation="LaTeX">$p =0.03$ </tex-math></inline-formula>), and the cognitive functions (memory and attention) were significantly promoted compared with the control group (accuracy of 3-back test: <inline-formula> <tex-math notation="LaTeX">$p =0.0005$ </tex-math></inline-formula>, reaction time of 3-back test: <inline-formula> <tex-math notation="LaTeX">$p =0.0009$ </tex-math></inline-formula>). After additional validations on long-term training effect and on different patient populations, the proposed method exhibited considerable potential to be developed as a fast, effective, and widespread training approach for cognitive function enhancement.
Comparison of Lattice Boltzmann and Finite Volume method for flow past sphere

Three dimensional computations have been carried out using Lattice Boltzmann Method (LBM) and Finite Volume Method (FVM) for the case of flow past a stationary sphere. The Reynolds number of the mean flow have been varied from 50 to 300 during which flow transitions from steady state to unsteady state. The LBM simulations were carried out using the open source solver Palabos developed in the University of Geneva while the FVM computations were performed using the commercial solver ANSYS Fluent 17.2. Qualitative results like streamlines and three dimensional vortex structures in the wake of the sphere, obtained from the two different techniques have been compared alongside results reported in literature. Quantitative results such as, variation of velocity in the sphere wake and length of wake bubble have also been compared. The present study also tabulates the computational time required to complete the flow simulation for the both the solvers while utilizing the same computational resource. The results from the present study highlights the advantages of LBM solver for simulation of unsteady three dimensional flow but also indicate that FVM technique has definitive benefits when it comes to steady flows.
Ultrasonic Locust Repellent Circuit

Locusts are insects that form enormous swarms that spread and destroy the crops and livestock. These cause a high level of agricultural damage. In 2020, locusts have caused damages in many countries namely Kenya, Ethiopia, Somalia, Uganda, Eritrea, India, Iran, Yemen, Oman and Saudi Arabia. This year is not going as per anyone’s plan. It saw an outbreak of this swarm, which nearly destroyed 3.5 lakh hectare of crops in the regions of Rajasthan and Gujarat. Crops of mustard, wheat and cumin were eaten up by them and thus causing great amount of losses to the farmer, so we have come up with a system that repel locust when exposed to ultrasonic frequency (more than 30 KHz this have been achieved by using basic electronics components and we have tried to make it cost effective for ground level implementation. This is going to help a lot of farmers which will save crores of rupees, this idea is going to be the foundation of upcoming electronic components made using this idea.
Reliability of a DME Ru Semidamascene scheme with 16 nm wide Airgaps

We evaluate the reliability of a semidamascene BEOL scheme with direct metal etched (DME) Ruthenium and 16 nm wide air gaps (AG). First, we show that Ru can be barrierless independent of the type of deposition (ALD, CVD, PVD) using planar capacitor structures with a metal-etch-based flow. We present TDDB results of semidamascene Ru +AG showing Vmaxto be above 1.5 V for TTF 0.1% of 3 km long lines at 100°C (using power law model). We show no change in resistance after >1200 h during electromigration tests at 330°C with 5 MA/cm2 stress. We identify increased Joule heating as a reliability concern.