Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Automated Parking System using IoT

The growing urbanization, in conjunction with the more congested urban areas and the greatly increased number of metropolitan vehicles, has resulted in the demand for guarded pull-up locations at schools, shopping malls, and other organizations. In this paper, an autonomous IR sensor based secure and smart parking without the intervention of human beings is discussed by utilizing advanced technologies and low-cost smart devices. this ensures the mission of smart cities to improve the economic growth as well as to improve the quality of people’s lives. The suggested architecture incorporates gizmos like RFID readers and labels, servomotors as an entrance, personal computers, operating systems that are programmed code, and light-emitting diodes as components. As a result, the framework is used in order to take the functioning boundaries and dazzling Light-Emitting Diodes in hand grasp for a variety of applications. The envisioned architecture is adaptable to any setting imaginable. The suggested system would restrict access to the parking places to just those members who have been given permission to do so. Node MCU is an open-source Internet-of-Things (IoT) platform, and the inclusion of this component in the proposed system gives it a vital part to play in the transmission of data. When the RFID scanner reads a vehicle’s tag, the Node MCU transmits information about available slots to the Arduino micro-controller. The micro-controller then assigns a slot to the user, and this information is shown on the LCD screen. The RFID scanner reads the tag on the car. By using this architecture, the school can reduce the risk of a car being broken into. People who are members of the system, such as students are able to have peace of mind about the security of their automobiles. A system that manages the entrance and departure of registered vehicles, users automatically will also make it possible for individual users’ vehicles to be secure.
A 3D-Printed Millimeter-Wave High-Gain Horn Array with a Bandwidth of 48%

A millimeter-wave wideband large-scale horn array is investigated. A 1 × 2 sub-array and E-plane T-junctions with broadband characteristics are designed to enhance the bandwidth of the array antenna. By combining the wideband feed network and waveguide-fed horn elements, a 16 × 16 array is implemented. The proposed horn array has promising performance, involving a bandwidth of 48%, stable unidirectional radiation patterns, and gain of up to 34.5 dBi, which is desirable for emerging millimeter-wave applications. To verify the performance of the proposed array design, a 4 × 4 array is fabricated by employing commercial metallic three-dimensional (3D) printing technology. A measured bandwidth of 47% for |S11| < -10 dB is obtained.
Coupled-Line Directional Coupler with Arbitrary Coupling Level and Adjustable Operating Band

A directional coupler with arbitrary coupling level and adjustable operating band is proposed based on the distinct phase velocities of the odd mode and even mode of spoof surface plasmon polaritons (SSPP). Two SSPP transmission lines are employed to design directional couplers due to the open and confined field distribution of the SSPP. Arbitrary coupling level can be achieved by tuning interval width between the two lines. Besides, phase constants of even and odd SSPP modes are different, and the operating band could therefore be adjusted by both the phase constant difference and length of the coupling section. Two prototype couplers, one with adjustable coupling level in wide band and another with adjustable operating bands, are designed to verify the design principle. This design has capability in wideband application and potential in frequency division duplexing systems.
Impact of RF Frequency Bands on the DC and Large Signal Reliability of a 45nm RFSOI NFET based Power Amplifier Cell

In this work we highlight the impact of RF frequency on the DC and large signal reliability performance of a single nFET based power-amplifier (PA) cell for three different frequency bands, e.g., Ku, K, and Ka. We observe that the overall performance of the PA cell is superior in the Ka-band but the performance degradation due to reliability mechanisms is also more in this band. The impact of large-signal stress is high when PA is stressed in compression or near the P1dB region. This holds true for all frequency bands investigated in this work.
Xiaotian-Hybrid: A Novel Wheeled-Quadruped Platform

Wheeled robots and legged robots are two primary classes of mobile robots, possessing different features and excel in different aspects. Wheeled robots can reach higher speed on flat ground with relatively high energy efficiency, while legged robots can move reliably on uneven terrain and overcome obstacles. In this paper, we design a wheeled-quadruped robot "Xiaotian-Hybrid", which combines the advantages from both wheeled and legged mechanisms and can switch moving mode between driving and stepping to achieve balance between efficiency and traversability. In addition, quadruped robots are always hard to assemble and disassemble due to the complex power transmission system on their legs and non-standard connection parts with hard-to-reach screws. We apply the idea of bottom-up modular design to make each essential part of the robot isolated from others, and therefore improving maintainability.
A Dual-Polarized Planar Ultrawideband Phased Array Antenna with Ultra-Low Profile

A low-profile dual-polarized ultrawideband phased array antenna with a wide-angle impedance matching (WAIM) layer is presented in this paper. This antenna is comprised of the printed tightly coupled dipoles elements without external matching network. The innovative introduction of resistive sheet in multilayer PCBs is helpful in reducing the profile, which is achieved as small as only 3.7mm (0.22). The proposed dual-polarized infinite array achieves 6~18GHz(3:1) when scanning up to ±60° in E/H plane. A dual-polarized 12×12 prototype is fabricated and measured, verifying the validity of the design.
Antenna-Duplexed Passive Beamforming Front-end for Joint Communication and Sensing

Wide scale deployment of wireless devices is making the spectrum a scarce resource. Joint communication and sensing (JC&S) and in-band full duplex (IBFD) communications provide opportunities of spectrum reuse. IBFD communication hardware requires high transmitter-to-receiver (TRx) isolation, making it shareable and reusable with mono-static continuous wave radar in JC&S context. On the other hand, design and development of beamforming phased arrays, required for communication coverage and radar spatial filtering, becomes challenging when a high TRx isolation assurance is required. This paper presents a solution in the form of antenna-duplexed passive beamformer usable for IBFD communication and mono-static continuous wave radars, mitigating two key challenges/requirements of JC&S hardware reuse in co-design approach - TRx isolation and beamforming capability. A beamformer using Butler matrix and a planar quasi-circulator are designed and optimized for X-band (centered at 10 GHz) providing up to 40 dB transmitter leakage suppression using primarily passive circuits.
A Compact Symmetrical BeiDou Antenna with Circular Polarization for Satellite Navigation and Positioning System

In order to miniaturize antenna size and realize circular polarization characteristic, according to the relationship between frequency and length of the patch, this letter validates a compact circular polarized microstrip antenna by introducing a high dielectric constant substrate (εr=16), truncated opposite corners and an X-shape slot on the patch. Through experimental tests, comparison to similar works, it has proved that simulated and measured results of the fabricated antenna are in good agreement. The measured results of the proposed antenna at BD3 band, in terms of impedance matching, circular polarization characteristic, directivity and gain, are well accord with actual requirement for BDS. Therefore, the proposed antenna is a highly qualified candidate for BeiDou satellite navigation and positioning system applications.
Design of Microstrip-to-Microstrip Transition for High-Density Flip-Chip Interconnects

In this paper, the challenge of the microstrip-to-microstrip (MS-to-MS) transition for high-density flip-chip interconnects was addressed, along with a solution. When it uses a Ms-to-Ms transition, it will produce strong resonances between the on-chip and off-chip metal grounds, resulting in subpar transmission performance. In addition, electromagnetic compatibility problems will arise from coupling between RF and IF interconnects. To solve these issues, a U-shaped slot on the off-chip ground in conjunction with shorting bump balls is proposed. 18 flip-chip interconnects were used in a 2x2 mm RF chip, with 4 for RF, 4 for IF, and the remaining 10 for supply and control voltages. Good transmission performance is demonstrated with the proposed structure at RF (55–65 GHz) and IF (1–5 GHz) bands. In the meantime, there is a significant suppression of coupling between RF and IF interconnects.
Pattern-Reconfigurable Annular-Ring Patch Antenna Loaded With Multifunctional Slots for Balanced Performance

This paper presents a slot-loaded annular-ring patch antenna (ARPA) to realize balanced performance between reconfigurable broadside and conical radiation. Initially, the effect of slot loading on the TM21 and TM31 modes of the ARPA is investigated with characteristic mode analysis (CMA). By loading the multifunctional slots to the edges of the ARPA, which respectively represent the magnetic- and electrical-wall of TM21 and TM31 mode, the TM31 mode is significantly manipulated with the least perturbation on the TM21 mode. On the one hand, its conical beam is reshaped into the desired broadside radiation. On the other hand, by appropriately selecting slot loading scheme, equal quality factor and overlapped band of the two modes can be obtained, contributing to consistent performance at the two reconfigurable states. Finally, the proposed ARPA with switchable feeding network is designed and measured to verify the feasibility. The simulated and measured results show that the proposed antenna can achieve measured bandwidth of 7.0% (6.8%), in-band total efficiency of 89.7% (87.4%) for the conical state (broadside state).
ADJOINT CHARGE DEPOSITION AND CAD TRANSPORT IN ITS

We present an adjoint method for calculating charge deposition that has been implemented in the ITS Monte Carlo codes and discuss the extension of the ITS codes to CAD geometries. Using multigroup coupled-electron-photon cross sections in a version of the Monte Carlo code ITS, we demonstrate the effectiveness of the adjoint chargedeposition method by comparing numerical results with experimental benchmarks. The strengths and weaknesses of this method relative to forward calculations are examined. The methods involved in the development of a CAD-geometry capability for ITS are also presented. Using the physics of the ITS codes to perform radiation transport in CAD geometries is more computationally expensive, but may decrease the time involved in problem setup and later geometry modification. Possible applications for these new methods are discussed.
Designing Efficient Pipelined Communication Schemes using Compression in MPI Libraries

The emergence of trillion-parameter models in AI, and the deployment of dense Graphics Processing Unit (GPU) systems with high-bandwidth inter-GPU and network interconnects underscores the need to design efficient architecture-aware large message communication operations. GPU-based on-the-fly compression communication designs help reduce the amount of data transferred across processes, thereby improving large message communication performance. In this paper, we first analyze bottlenecks in state-of-the-art on-the-fly compression-based MPI implementations for blocking as well as non-blocking point-to-point communication operations. We then propose efficient point-to-point designs that improve upon state-of-the-art implementations through fine-grained overlap of copy, compression and communication operations. We demonstrate the efficacy of our proposed designs by comparing against state-of-the-art communication runtimes using micro-benchmarks and candidate communication patterns. Our proposed designs deliver 28.7% improvements in latency, 49.7% in bandwidth, and 36% in bi-directional bandwidth using micro-benchmarks, and up to 16.5% improvements for 3D stencil-based communication patterns over state-of-the-art designs.
Generalizable Deep Neural Network Based Multi-Material Hysteresis Modeling

Over the past couple of decades, dependence on magnetic materials has increased tremendously. Since every kind of magnetic material behaves differently, it is very difficult to create a universal model of these behaviors. While there exist models and simulation tools that can represent the behavior of these materials, it may take days or even an entire week to compute when embedded in an analysis system, which is highly inefficient. With a good machine learning (ML) model, the computation time can be reduced by a significant amount with minimal error. To determine performance parameters such as the efficiency of an electrical machine, the hysteretic behavior of the material is crucial, and the representation used can impact the performance of a simulation tool. The goal of this work is to explore several, different deep learning (DL) network architectures which might both provide a generalized representation methodology for hysteresis problems and reduce the computational effort needed to include the impact of hysteresis in a finite element-based simulation over existing approaches.
Linearity Analysis of FeFET Synaptic Devices considering Random Phase Distributions

A deep neural network (DNN) requires many memories to store and update the synaptic weights. FeFET with partial polarization switching can change the weight for DNN acceleration. FeFET with ideal linearity significantly improves the training accuracy of DNN. This work investigates the linearity variation of FeFET synapses considering the impact of random phase distributions (RPD). Our results show that FeFET with higher ferroelectric (FE) percentage leads to lower conductance variations and improves bit error rate. Moreover, the linearity variation of FeFET synapses can be further improved by proper read voltage design.
Identification and Prediction of Hepatitis B and NAFLD using Machine Learning

Hepatitis B and Non-Alcoholic Fatty Liver Disease (NAFLD) are the most frequent and well-known liver disorders worldwide. Over time, these disorders cause liver cirrhosis, which finally leads to failure and major consequences, which can be fatal. More than a third of the world’s population has Hepatitis B and NAFLD. Blood tests are utilized to identify hepatitis B, and while the liver biopsy is the gold standard in diagnosing NAFLD, ultrasound scan pictures are employed in this work. The proposed method efficiently identifies or predicts Hepatitis B and NAFLD using the findings of this study, namely the application of machine learning and deep learning neural networks. Keyword: Fatty liver, Hepatitis B, Machine Learning, NAFLD.
A 23.9-30.5 GHz Harmonic Extraction VCO With High Subharmonic Suppression

This article presents a broadband third-harmonic extraction structure consisting of a subharmonic suppressed class-F voltage-controlled oscillator (VCO) and a wideband injection-locked buffer (ILB). An 8-shaped inductor is used in the weakly coupled transformer of the VCO to improve the subharmonic suppression and save the area. Meanwhile, the source tank broadens the frequency range of the third-harmonic extraction with no additional tuning circuits. Besides, the ILB cascading after the VCO further suppresses the unwanted harmonics. With the load of the fourth-order transformer, the locking range of the ILB is broadened. As a proof of concept, the VCO is implemented in 65 nm CMOS process. The power consumptions of the core VCO and the ILB are 5 mW and 7.2 mW, respectively. The measured phase noise is -102.5 dBc/Hz at 1 MHz offset with oscillation frequency of 30.5 GHz. Within the frequency tuning range of 24.3% from 23.9 to 30.5 GHz, the fundamental and second harmonic rejections are larger than 30 dBc and 35 dBc, respectively.
Drain Current Variability in 2-levels Stacked Nanowire Gate All Around P-type Field Effect Transistors

An experimental study of drain current statistical characteristics in two vertically stacked nanowire MOSFETs (bottom one: Omega shaped and top one: nanowire) is presented. The most critical parameter variations are identified using an advanced mismatch model that well describes the experimental results, while the impact of channel geometry is examined for every source of process variations.
Cascade Prediction with Recurrent Neural Networks and Diffusion Depth Distributions

A message suddenly in the spotlight can be an innocuous superstar scandal, but it can also be an extreme opinion that destabilizes society or a rumor that distorts the truth. Accurately predicting the future cascade size of information cascade on social networks such as Twitter and Weibo can help focus more attention on suspicious large-scale cascades in advance, assisting managers in making decisions for human intervention. However, early cascade prediction methods either rely on a priori stochastic process assumptions or handcrafted features, leading to limited prediction performance. Moreover, most existing deep learning-based cascade prediction methods use graph neural networks or graph representation learning techniques to model cascaded graphs of information propagation, which faces the dilemma of low graph embedding efficiency. To address these issues, considering the strong positive near-linear relationship between the final cascade size and the diffusion depth, we propose a simple yet effective cascade prediction method called Cas3D that captures the evolution of the diffusion depth distribution via recurrent neural networks and models the complex relationship with future cascade sizes. Extensive experiments on two real-world datasets demonstrate that Cas3D significantly outperforms state-of-the-art approaches with a satisfactory efficiency advantage.
Cybersecurity in Internet of Things Networks using Deep Learning Models

The Internet of Things and global grid that connects millions of sensors and devices have made distributed software and services ubiquitous. IoT’s vast reach and economic value make it a natural target for hackers, making security the industry’s top worry. Privacy has always been studied, but IoT design and new intimidations make most standard safeguards ineffective. Deep learning’s data, odd case technique, and ability to detect unexpected threats may help detect IoT intrusions. The “Internet of Things” includes computers, sensors, network infrastructure, software, networks, users, gateways, and services (IoT). Due to the increasing importance of Internet of Things (IoT) innovations, growth and management are huge. Data protection and privacy have recently caught users’ attention. As social media grows, more people connect. As connections grow, more safe spaces are needed. This article discusses data protection, including deep learning models, Machine Learning (ML) concepts, safety and security, and cybersecurity management. To illustrate IoT network vulnerability, convolutional neural networks (CNN), Long Short-Term Memory (LS TM), and CNN combinations have been studied and compared. We can detect IoT cyberattacks with over 99% accuracy using Modbus network traffic data.
Silicon-based High-Gain Photodetector with A Strong 254-nm Response for Chemical Oxygen Demand (COD) Monitoring in City Sewage Water

Chemical oxygen demand (COD) measurement in city sewage water becomes important for monitoring water pollution. A 254-nm UVC absorption spectrometer is commonly used for such a purpose. In this work, we report on a photodiode-body-biased MOSFET (PD-MOS) based on silicon and as compared with large-area UV-enhanced silicon photodiode being used, the PD-MOS is advantageous in terms of gain and dynamic range, making it a promising candidate for the COD tests.
Prediction of power network planning demand coefficient using eXtreme Gradient Boosting algorithm

In order to solve the problems of inaccurate prediction of demand coefficient in traditional load forecasting based on empirical value, which is easy to cause investment waste or equipment operation safety risks, a refined prediction method of demand coefficient of power network planning based on XGBoost algorithm is proposed. Firstly, the information of power application system is obtained, and the multi-dimensional data such as the capacity and load of power users are obtained from the database tables. Then the refined prediction model of demand coefficient is constructed based on XGBoost. Finally, the demand coefficient is predicted by the newly reported user information. The application results show that the proposed prediction method of demand coefficient based on XGBoost can effectively improve the prediction accuracy, and has good research and practical value.
Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach

The goal of the study presented in this paper is to evaluate the performance of a proposed Reinforcement Learning (RL) power allocation algorithm. The algorithm follows a demand-driven power adjustment approach aiming at maximizing the number of users inside a coverage area that experience the requested throughput to accommodate their needs. In this context, different Quality of Service (QoS) classes, corresponding to different throughput demands, have been taken into account in various simulation scenarios. Considering a realistic network configuration, the performance of the RL algorithm is tested under strict user demands. The results suggest that the proposed modeling of the RL parameters, namely the state space and the rewarding system,is promising when the network controller attempts to fulfill the user requests by regulating the power of base stations. Based on comparative simulations, even for strict demands requested by multiple users (2.5-5 Mbps), the proposed scheme achieves a performance rate of about 96%.
Sensor Individual Non-Orthogonality Correction in Low-Cost MEMS Gyroscopes Using Neural Networks

The research presented in this work compensates non-orthogonality over temperature stress effects in low-cost open-loop MEMS gyroscopes using neural networks (NN) for a sensor individual compensation to improve the sensor performance. The non-orthogonality is included in the sensor cross-axis sensitivity (CAS) of MEMS gyroscopes. Using the model-agnostic meta-learning algorithm (MAML) as a self-calibration algorithm and one initial measurement after soldering, an individual compensation model is generated for each sensor that predicts the non-orthogonality using the MEMS gyroscope's quadrature value as an input. It will be shown that a sensor-individual model outperforms a compensation model that should fit for all sensors at once like linear regression or classic NN and improves the non-orthogonality by 82.7 %, 7.5 % and 70 % for yx-, zx-, and zy-non-orthogonality,
Enhancing Isolation of Two-Port Single Patch Antenna by Shifting Electric Wall

This paper proposes a technique to enhance isolation between two ports in a single patch antenna by shifting the electric wall. The concept and working principle are demonstrated at first, which illustrates that the E-wall can be shifted by superposing the TM20 and TM30 modes of the stub-loaded patch antenna. Then, the electric fields of the two modes can be superimposed by introducing a pair of slits centered-located in the middle of the patch. A practical antenna is designed and measured, whose results show the port isolation could be effectively improved to 11 dB under the initial -1-dB coupling. The radiation patterns of each port achieve the performance of a typical single patch.