Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Demand Current Pulse Activation of RF Monopole Antenna Biosensor Arrays with Nitrocellulose Membranes

Continuous glucose monitors currently on the market are expensive and uncomfortable due to their short operational lifespans of 14 days or less limited by biofouling. To address this problem, we propose a biosensor array skin patch that would increase the device functional lifetime significantly. Each sensor, based on a quarter wave monopole antenna, is housed in a PDMS well. The sensors read changes in voltage reflection coefficient (dB). Combustible nitrocellulose membranes prevent early contact of sensors with the interstitial fluid. A current pulse passed through a metal filament sufficiently disintegrated the membrane on top to allow interstitial fluid to enter thereby activating a new sensor.
Image Segmentation Model of Pear Leaf Diseases Based on Mask R-CNN

Image segmentation of pear leaf diseases is a crucial method to scientifically determine the occurrence degree of diseases. Pear leaf diseases sizes differ a lot affected by the occurrence period and degree. Convolutional neural network has a fixed receptive field when extracting pear leaf diseases images, which makes it difficult to fully extract diseases features of different sizes. At the same time, continuous down-sampling reduces the resolution of the feature map and loses a lot of spatial information, resulting in rough mask of the image restored by upsampling. To solve this problem, a pear leaf diseases image segmentation model based on Mask R-CNN is proposed. In the feature extraction stage, the dilated convolution is used to control the expansion rate of the convolution kernel to gain different sizes of the receptive field. Under the condition of retaining the resolution of the feature map, multi-scale context information is obtained to improve the accuracy of mask prediction. The experimental results show that the MPA and MIoU of the proposed algorithm are 91.49 % and 88.55 % respectively, and the segmentation effect is better than that of Mask R-CNN.
Compass and Kitaev models -- Theory and Physical Motivations

Compass models are theories of matter in which the couplings between the internal spin (or other relevant field) components are inherently spatially (typically, direction) dependent. Compass-type interactions appear in diverse physical systems including Mott insulators with orbital degrees of freedom (where interactions sensitively depend on the spatial orientation of the orbitals involved), the low energy effective theories of frustrated quantum magnets, systems with strong spin-orbit couplings (such as the iridates), vacancy centers, and cold atomic gases. Kitaev's models, in particular the compass variant on the honeycomb lattice, realize basic notions of topological quantum computing. The fundamental inter-dependence between internal (spin, orbital, or other) and external (i.e., spatial) degrees of freedom which underlies compass models generally leads to very rich behaviors including the frustration of (semi-)classical ordered states on non-frustrated lattices and to enhanced quantum effects prompting, in certain cases, the appearance of zero temperature quantum spin liquids. As a consequence of these frustrations, new types of symmetries and their associated degeneracies may appear. These intermediate symmetries lie midway between the extremes of global symmetries and local gauge symmetries and lead to effective dimensional reductions. We review compass models in a unified manner, paying close attention to exact consequences of these symmetries, and to thermal and quantum fluctuations that stabilize orders via order out of disorder effects. We review non-trivial statistics and the appearance of topological quantum orders in compass systems in which, by virtue of their intermediate symmetry standard orders do not arise. This is complemented by a survey of numerical results. Where appropriate theoretical and experimental results are compared.
Wideband Printed Bowtie antenna for 5G and millimetric wave Applications

A compact wideband bow-tie antenna (CWBA) developed with dimensions 10X13X0.25 mm for millimetric wave applications is reported in this work. The antenna is found to be a favorable candidate for full duplex communications in the 5G wireless systems. It is based on a novel structure composed by integrated balun and folded microstrip line. A rectangular slot was made at ground to make Defective Ground. An optimized input impedance of 50 ohm was given as feeding for the CWBA antenna using microstrip feeding technique. Experimental results have been shown in excellent agreement with numerical simulations carried out using CST. The antenna provides wide band from 32.47 to 40.9GHz with a variable gain from 5-6dbi. The simulated CWBA impedance bandwidth was 8.43Hz, which is 21%fractional bandwidth. This band completely covers the 5G communication band and millimetre-wave communication band.
DOTA 2 Win Loss Prediction from Item and Hero Data with Machine Learning

Video gaming has become a titan in the overall market over the past decade, culminating in an estimated worth almost 180 billion US dollars by 2021. Aside from its growing influence in the overall market, video games have also created a new competitive format called eSports, a format where highly skilled players of certain video games play against each other in a tournament to see who the most skilled are and win a prize at the end. ESports are just one of many reasons why people have become interested in the idea of being able to predict the outcome of any given match between players. In this study, We conducted research on the importance of certain factors in determining the win or loss of any given Defense of the Ancients 2, better known as DOTA 2, match. We found that Item and Hero choices play a large role in winning any given match. From this, we concluded that we would be able to predict a match’s outcome solely based off of these two factors and created models to predict the outcome of any given match. In this study, we will be employing the use of Decision Tree, Random Tree and XGBoost classifiers in order to create our models. In the end, the XGBoost model ended up being our best model, with an accuracy of roughly 93% which can predict an outcome in roughly one minute.
Dynamic Service Deployment Strategy using Reinforcement Learning in Edge Networks

Edge computing has lately appeared as a practical entrance to intensify the capabilities of the industrial networks by transferring the computation data to the edge of the networks. However, the challenge here is to locate processing devices and provide services through the shortest path by making a proper trade-off between latency and energy of the processing. These demands can be solved using complex optimization problems within a confined deadline, which is barely feasible with traditional statistical techniques. Thus, we propose a heuristic-based task controlling mechanism that makes service deployment decisions by eliminating the need to solve combinatorial optimization problems. Moreover, to achieve the least possible delay and find the best possible path towards the goal, we adopt Graph Reinforcement Learning (GRL) technique. Extensive numerical results illustrate that our proposed strategy generates near-optimal solutions within the given time-bound.
A Feasibility Study on Integrated Sensing and Communication in Cooperative Network

Exploiting the sensing function in the wireless communication system has attracted many research interests for the benefits of hardware reduction and great opportunity to create novel applications. In this paper, we make a feasibility study on Integrated Sensing and Communication in Cooperative (C-ISAC) network, where the source node transmits the signal with dual functions of sensing and communication to the cooperative relay node and the destination node. This paper investigates the joint design of beamforming on the signal with dual functions, aiming to maximize the sensing performance measured by the difference between peak and sidelobe (DPSL), while guaranteeing the communication capacity requirement in the relay-based cooperative network. Semi-definite relaxation (SDR) is introduced to solve the formulated non-convex problem. Compared with traditional non-cooperative ISAC network, the simulation results show that the proposed C-ISAC network can achieve better communication capacity and sensing performance.
Light trapping and reflection control with tilted pyramids and grooves

Texturing the surface of a cell to produce crystallographically defined pyramids and grooves is a well-established technique for improving the performance of crystalline solar cells. The double bounce experienced by reflected light with such textured surfaces reduces reflection to low levels even without antireflection coatings. A simply implemented approach based on tilting these geometrical features is shown to produce quadruple bounces in encapsulated cells. This reduces reflection from the cell surface from 3-4% without antireflection coatings to below 0.2%. Tilting of the geometrical features allows very effective light trapping within the cell when combined with a planar rear surface. The improved reflection properties are relevant to all cells which use texturing, regardless of their design. <<ETX>>
Analysis of Variability in Transconductance and Mobility of Nanowire Transistors

This work presents a comparison between the variability in junctionless nanowire transistors and inversion-mode nanowire transistors, looking at the transconductance, low-field mobility, linear and quadratic mobility degradation coefficients. To extract these parameters, the Y-Function method has been used. The obtained results shows differences in mobility and transconductance matching coefficients, indicating that mobility influence is not the only source of transconductance variation.
Investigating a Portable Low-Cost Target Simulator for Doppler Radars

This article presents a study on employing a single-sideband (SSB) mixer as a target simulator for Doppler radar sensors. The SSB mixer modulates the continuous-wave radio frequency (RF) signal transmitted by the Doppler radar, similar to a moving target. To prove the effectiveness of the proposed target simulator, the response of a real human target performing a hand gesture is emulated using the target simulator. The spectrogram of the human hand gesture and the simulator-generated gesture have been compared for performance evaluation. A 5.8-GHz prototype of the proposed target simulator and the obtained experimental results are presented in the paper.
Hydrodynamics of correlated systems. Emptiness Formation Probability and Random Matrices

A hydrodynamic approach is used to calculate an asymptotics of the Emptiness Formation Probability - the probability of a formation of an empty space in the ground state of a quantum one-dimensional many body system. Quantum hydrodynamics of a system is represented as a Euclidian path integral over configurations of hydrodynamic variables. In the limit of a large size of the empty space, the probability is dominated by an instanton configuration, and the problem is reduced to the finding of an instanton solution of classical hydrodynamic equations. After establishing a general formalism, we carry out this calculation for several simple systems -- free fermions with an arbitrary dispersion and Calogero-Sutherland model. For these systems we confirm the obtained results by comparison with exact results known in Random Matrix theory. We argue that the nonlinear hydrodynamic approach might be useful even in cases where the linearized hydrodynamics fails.
Temperature influence on Operational Transconductance Amplifier designed with triple gate TFET

This paper presents the temperature influence on Operational Transconductance Amplifier (OTA) designed with triple gate Tunnel-FET (TFET). To simulate the OTA circuit, it was used the lookup table approach in Verilog-A language using experimental data at room temperature. The circuit was designed with TFET in inversion region with gm/ID value of 6V−1. The circuit analysis was conducted under the temperatures of 300K, 360K and 420K and it was verified the impact of using a current mirror bias circuit with or without thermal compensation. From the results it is possible to imply that the TFET transistors are very interesting for this kind of circuit due to that they have a very low power consumption and a high voltage gain when comparing to OTA using conventional MOS transistors. Another important observation is that by using a current bias circuit with temperature compensation, the total gain of the circuit slightly increases instead of decreasing with the higher temperature.
An Enhanced GPR FWI Scheme with Low-Frequency Data Extrapolated by Progressive Transfer Learning

The ground penetrating radar (GPR) is an important technique for subsurface detection. The details of the geological model could be reconstructed by full waveform inversion (FWI) based on GPR data. Due to the device limitation, the low-frequency components are absent in the observations. These low-frequency components help GPR FWI avoid local minima. To extrapolate the low-frequency information from the band-limited observations, we propose a robust progressive transfer learning method. Benefiting from the combination between the physics-driven GPR FWI and the deep learning approach, our method shows a stronger generalization ability. We validate our method with synthetic data. Experiment results show that our method achieves fast convergence speed and high accuracy when the size of the training set is limited. The subsurface details are successfully reconstructed with extrapolated low-frequency data.
Optimal Aperture and Path Design for Passive UAV SAR System with Geosynchronous Illuminator

Due to the superior flexibility and cost-efficiency, unmanned aerial vehicle (UAV) is becoming an indispensable platform for advanced remote sensing applications. In this paper, the passive UAV synthetic aperture radar (SAR) system with geosynchronous illuminator (PUAV-SAR) is studied. The UAV platform passively reuses the backscattered signal of a GEO-SAR, and achieves bistatic imaging and data communication. Firstly, the aperture and path design problem is modeled as a constrained multiobjective optimization problem. Then, a path design method is presented to generate a UAV path with optimized mission performance. Numerical simulations are conducted to verify the effectiveness of the proposed method.
Online 3D Placement for UAV-aided Free-space Optical Communication under Shadowing

This paper studies 3-dimensional (3D) placement for an unmanned aerial vehicle (UAV) to build a free-space optical (FSO) communication links to relay the signals for two users that are shadowed by obstacles. The key challenge of FSO communication is the possible signal blockage due to obstacles. UAV can be adopted as a relay to build connection with two users, and it is essential to find a good relay position. Existing work is either based on heuristic methods or to optimize the UAV relay position on 2D, which cannot fully unleash the potential of UAV relay. In contrast, as note that the globally optimal relay position potentially lies on the equipotential surface, this paper develops UAV search strategies to build wireless relay communication channels for two ground users that are probably shadowed by obstacles. As the two users may have different transmission power, it may not be the best choice to search on the middle perpendicular plane. Based on this insight, this paper develops a strategy to search on a selected perpendicular plane to the ground and finds a sub-optimal UAV service position. The maximum length of search trajectory being linear to the geographical scale. Numerical experiments are conducted over a real-world urban topology and demonstrate that the performance of the proposed search is very close to the globally optimal one found by brute force search.
Modeling Quantum Confinement in Multi-Gate Transistors with Effective Potential

Particle-based Monte Carlo device simulators are an efficient tool to investigate the performance and reliability of transistors. The semiclassical theoretical model employed in the Monte Carlo device simulator is unsuccessful to describe some aspects of the multi-gate transistors that come from the quantum behavior of charge carriers. To take into consideration the space-quantization effects in these simulators, a quantum correction is necessary. We propose to include an effective potential in the Monte Carlo device simulator to address the wave-like behavior of electrons in n-type silicon FinFET and n-type silicon nanowire transistors. The effective potential has a unique parameter, which can be adjusted to find a line density using an Effective Potential-Poisson solver that matches with the line density calculated using a Schrodinger-Poisson solver. We demonstrated that using the effective potential model, the effect of the electron confinement is well described.
Here You Go: An Intelligent Mobile Application Featuring Immediate Assistance for the People of Bangladesh

The immediate help assistance system is considered as a useful solution to ensure emergency and adequate support for victims and pupils all around the world. Nowadays, people face lots of unwanted situations/problems every day that require immediate assistance. Currently, many organizations provide emergency support to the citizens. Most of the existing emergency assistance works depend on the manual system rather than the digital application. Thus, the existing system suffers from huge delays and uncertainties. To mitigate these issues, there is a critical need for an intelligent mobile application that offers coordination among the companies or services. To enhance the current situation, in this paper, we have presented an intelligent mobile application featuring immediate help assistance for the Bangladeshi people. Our application provides several features that include user and service provider registration features, service selection and confirmation, service delivery, and user feedback, among others. Our proposed application covers multiple immediate help assistance areas that include utility problems, shelter, crime reporting, food delivery, online shopping, health, and transport rent. The user review results indicated that the proposed application features are useful for user’s.
An Adaptive Detection Method for Early Smoke of Coal Mine Fire Based on Local Features

Aiming at the particularity of coal mine conditions, and in order to improve the effectiveness of smoke detection method in actual early detection, an early fire image smoke detection method based on adaptive histogram algorithm is proposed in this paper. Firstly, the principle of adaptive histogram algorithm for smoke image is explained, and the calculation method of line by line sliding window histogram is introduced. Then, the smoke detection process is given based on image adaptive histogram feature to realize coal mine smoke detection. Finally, the method is used to realize target detection in different smoke scenes. In the experiment, the traditional edge detection operator, smoke detection based on global histogram and the adaptive histogram smoke detection in this paper are used to detect smoke image and flame smoke respectively. The results show that this method can better reflect the motion characteristics of smoke at the edge, and contains large information entropy, which shows that this method shows good performance in smoke detection.
Sample-Efficient Cross-Domain WiFi Indoor Crowd Counting via Few-shot Learning

WiFi-based indoor crowd counting (ICC) has raised increasing interest in wireless sensing applications due to its low hardware cost and privacy preservation in sensing data collection. However, most existing WiFi-based ICC models that are fined-tuned in one domain suffer from dramatic performance degradation when applied to dissimilar domains, e.g., change of device and background deployments. In this case, a large amount of data samples need to be collected to retrain the ICC models, which is costly and indeed infeasible in many practical applications. In this paper, we propose a sample-efficient cross-domain $W$ iFi ICC method based on few-shot learning (FSL) techniques, which achieves robust ICC performance under different application setups. Specifically, we first obtain a convolutional neural network (CNN) based CSI amplitude feature extractor using sufficient data samples in the source domain. When performing ICC task in a new target domain, the proposed feature extractor can efficiently generate a low-dimensional feature map of the input CSI measurements. By doing so, we show that a lightweight logistic regression (LR) classifier suffices to produce high ICC accuracy even under a very limited target domain dataset. Experimental results show that the proposed method could achieve 78.85% and 94.18% accuracy for 0–8 people cross-domain ICC tasks under 1-shot and 5-shot learning cases, respectively.
Arithmetization of Functional Program Execution via Interaction Nets in Halo 2

We sketch a method for creating a zero-knowledge proof of knowledge for the correct execution of a program within a model of higher-order, recursive, purely functional programming by leveraging Halo 2. To our knowledge, this is the first ZKP for general purpose computation based on purely functional computation. This is an attractive alternative to using a von Neumann architecture based zero-knowledge virtual machine for verified computing of functional programs, as compilation will be more direct, making it more easily verifiable and potentially more efficient. Interaction nets are a natural setting for recursive, higher-order functional programming where all computation steps are linear and local. Interaction nets are graphs and traces for such programs are hyper-graphs. Correctness of a trace is a simple syntactic check over the structure of the trace represented as a hyper-graph. We reformulate this syntactic check as a Halo 2 circuit which is universal over all traces
Evaluation of the Symmetrical and Asymmetrical Causality Relationship Between Bitcoin Energy Consumption and Stock Values of Technology Companies

Energy production is a phenomenon that has always preserved its importance for the history of humanity, as well as where the energy is spent and its consumption are also important. In this study, the causality relationship between Bitcoin energy consumption and stock values of technology companies (Apple, Dell Technologies, Lenovo Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise) was examined. In the analysis, weekly price data for the period 12.02. 2017-07.02. 2021 were used. Toda and Yamamoto (1995) symmetric causality test and Hatemi-J (2012) asymmetric causality test were used to determine the relationship between Bitcoin energy consumption and technology companies’ stock values. According to the results of the Toda and Yamamoto (1995) causality test, it has been found that there is a causality from Bitcoin energy consumption to Apple’s stock value; according to the Hatemi-J (2012) asymmetric causality test results, it has been determined that there is a causality from Bitcoin energy consumption positive shocks to Apple, Dell Technologies, Lenovo Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise stock values negative shocks and from Bitcoin energy expenditure negative shocks to Hewlett Packard Enterprise negative shocks. According to the results of the study in general, it is seen that the change in Bitcoin energy consumption has an effect on the company returns of the companies that sell the necessary tools for Bitcoin energy production. From this, it can be commented that Bitcoin mining has an effect on the stock returns of technology companies as well as many financial factors.