Miracle-dz/UniFAR-FTUs huggingface dataset:
An iterative approach for solving fractional optimal control problems

In this work, the variational iteration method (VIM) is used to solve a class of fractional optimal control problems (FOCPs). New Lagrange multipliers are determined and some new iterative formulas are presented. The fractional derivative (FD) in these problems is in the Caputo sense. The necessary optimality conditions are achieved for FOCPs in terms of associated Euler–Lagrange equations and then the VIM is used to solve the resulting fractional differential equations. This technique rapidly provides the convergent successive approximations of the exact solution and the solutions approach the classical solutions of the problem as the order of the FDs approaches 1. To achieve the solution of the FOCPs using VIM, four illustrative examples are included to demonstrate the validity and applicability of the proposed method.
A Framework for Online and Offline Programming of Multi-Robot Cooperative Motion Planning

A multi-robot system has more flexibility than a single robot, but cooperative path planning between multi-robot systems is still laborious. Offline programming(OLP), which can automatically generate robotic programs based on workpiece models, is a key technology for accelerating the intelligent manufacturing revolution. Most of these OLP softwares support a limited number of robots and lack support for multi-robot coop- erative programming and reality interaction, which prevents the robot’s advantages from being utilized and results in a common user programming experience. Therefore, this paper proposes a general offline programming framework based on SolidWorks, which enables the robot to be configured for programming following user needs. This framework provides a path-tracking function that, when combined with SolidWorks assembly and motion study, greatly simplifies the process of cooperative path planning. To increase the effectiveness of offline programming, a topic communication mechanism is used to establish a method for the framework to communicate with external devices and to obtain sensor data to help program through teaching and other means. Then, a dual robots cooperation experiment is conducted to confirm the viability and simplicity of the framework.
A new back-to-back graded AlGaN barrier for complementary integration technique based on GaN/AlGaN/GaN platform

A novel composite barrier layer with back-to-back graded AlGaN in a GaN/AlGaN/GaN epitaxial structure for high performance n- and p-channel devices on the same platform is proposed. By adjusting the relative thicknesses of the two graded layers, we obtain a spread in the width and concentration of carriers in the 3D slabs. The best barrier amongst those studied, enhances the on-current $(I_{ON})$ by 24.4% in low voltage n-channel devices, 32.2% the p-channel devices whereas the figure of merit of the power device is higher by 3 times, in comparison to the conventional platform.
Towards Efficient Cache Allocation for High-Frequency Checkpointing

While many HPC applications are known to have long runtimes, this is not always because of single large runs: in many cases, this is due to ensembles composed of many short runs (runtime in the order of minutes). When each such run needs to checkpoint frequently (e.g. adjoint computations using a checkpoint interval in the order of milliseconds), it is important to minimize both checkpointing overheads at each iteration, as well as initialization overheads. With the rising popularity of GPUs, minimizing both overheads simultaneously is challenging: while it is possible to take advantage of efficient asynchronous data transfers between GPU and host memory, this comes at the cost of high initialization overhead needed to allocate and pin host memory. In this paper, we contribute with an efficient technique to address this challenge. The key idea is to use an adaptive approach that delays the pinning of the host memory buffer holding the checkpoints until all memory pages are touched, which greatly reduces the overhead of registering the host memory with the CUDA driver. To this end, we use a combination of asynchronous touching of memory pages and direct writes of checkpoints to untouched and touched memory pages in order to minimize end-to-end checkpointing overheads based on performance modeling. Our evaluations show a significant improvement over a variety of alternative static allocation strategies and state-of-art approaches.
A 90-100GHz Passive Phase Shifter with Transistor-Based Capacitor-loaded Technique

This paper presents a 6-bit 90-100GHz passive phase shifter with 6-bit phase resolution for W band wireless applications. It is composed of three π-type units with a high phase-shifting step, two shunt cells for low-step phase shifting, and a double-pole double-throw switch to reverse signal polarity. To minimize the impact of temperature variation on the phase performance, a transistor-based capacitor-loaded structure is proposed to realize the two low-step phase-shifting cells. According to the measurement results, the phase shifter achieves a root-mean-square (RMS) below 10.5° with an insertion loss of 18dB±2dB at 94GHz. The input and output return losses are smaller than 12dB and 9dB across 90-100GHz, respectively. The core chip size is only 360μm× 535μm.
Guaranteed Gaussian Process Predictive Control for Lipschitz Nonlinear System with Input and State Constraints

Though model predictive control can systematically handle the control problem of multi-input multi-output system under state and input constraints, it heavily depends upon the model of the controlled system. The non-parametric nature of the Gaussian Process endows it with the flexibility of modeling various practical systems and, more importantly, the ability to provide the level of confidence in its predictions, which makes it preferable to the model predictive control. Therefore, in this paper, these two approaches are combined into a whole scheme, called the Gaussian Process Predictive Control, which can make the system avoid some relatively uncertain areas, through incorporating the variance into the objective function. Then with some mild assumptions, the closed system stability and recursive feasibility is guaranteed theoretically, and validated by a standard simulation example.
Automated Driver Drowsiness Detection System using Computer Vision and Machine Learning

In recent years, road safety has become an increasingly pressing issue due to the increase in the number of fatal accidents throughout the world. Accidents are a major threat to road safety, and fatigue and drowsiness are the Primary causes of such incidents. In order to prevent such accidents, it is essential to detect and aware of signs that cause drowsiness or fatigue in a driver, and issue a timely warning. A range of technologies have been created to detect drowsy driving; however, computer vision and image processing technology can efficiently detect the drowsiness using facial expressions. As described, a system has been proposed that continuously monitors the driver’s facial expression by analyzing the eyes and mouth movement in order to detect any signs of drowsiness or emotional shifts. If any such changes are detected, the system will alert the driver and helps in avoiding potential danger. This system provides a low-cost and resource-efficient approach to monitoring driver behavior and it utilizes facial landmark analysis to assess a driver’s performance without the need for additional sensors or equipment.
Framework for Simulation Models and Algorithms in ISAC Networks

In Integrated Sensing and Communications (ISAC), radar and communications functionalities share the same radio channel and radio resources. This sharing allows the deployment of both functionalities at a low cost without additional frequency bands and infrastructure. Because radar and communications have different radio resource requirements, new radio access and radio resource management (RRM) methods are required in a shared system. To develop and evaluate new ISAC methods, this paper presents a modular simulation framework for the system level and link level of ISAC networks. The main components of the framework are the data storage, the simulators, and the algorithms. This paper presents the framework's architecture, the integrated simulation models for channel and signal simulation, and the integrated algorithms for multiple radar target localization and data transmission.
Experimental results of adjusting membrane stress and sacrificial layer thickness for targeting the pull-in voltage of capacitive MEMS microphone structure

In this paper, a general capacitive structure was proposed to target the pull-in voltage (VP) of the microphone, and also report the Vp change level by stress of the poly membrane and thickness of TEOS used as sacrificial layer for release in this structure. This paper reports that changing the target of process parameters such as deposition thickness and etching depth can be a simple method to save time and cost to adjust the VP of the Micro Electro Mechanical Systems (MEMS) microphone without modifying the mask layout or changing the material.
Target Detection Approach for UAVs via Improved Unbiased Teacher

The efficient target detection approach of UAVs is an attractive way to improve the detection precision without hardware redesign. This paper investigates a semi-supervised target detection approach for UAVs using improved unbiased teacher (IUT). Considering the scarcity of balanced labeled data, a lot of unlabeled data is utilized based on the semi-supervised learning framework. To enhance the target detection accuracy with unlabeled data, an improved unbiased teacher model is proposed, where multi-channel enhanced data is used for teacher model training, and good pseudo-labeling labeled data via fusion non-maximum suppression module is obtained and employed for student model learning. Comparative experiments with supervised-based design, self-training and augmentation driven consistency regularization, and standard unbiased teacher illustrate the effectiveness and higher detection accuracy of the proposed IUT approach.
Parallel Vertex Color Update on Large Dynamic Networks

We present the first GPU-based parallel algorithm to efficiently update vertex coloring on large dynamic networks. For single GPU, we introduce the concept of loosely maintained vertex color update that reduces computation and memory requirements. For multiple GPUs, in distributed environments, we propose priority-based ordering of vertices to reduce the communication time. We prove the correctness of our algorithms and experimentally demonstrate that for graphs of over 16 million vertices and over 134 million edges on a single GPU, our dynamic algorithm is as much as 20x faster than state-of-the-art algorithm on static graphs. For larger graphs with over 130 million vertices and over 260 million edges, our distributed implementation with 8 GPUs produces updated color assignments within 160 milliseconds. In all cases, the proposed parallel algorithms produce comparable or fewer colors than state-of-the-art algorithms.
Fuel Cell based Grid Connected Two-Level Inverter

This study introduces a grid-connected inverter powered by fuel cells (FC). Though comparable to a battery, the fuel cell does not store energy. DC voltage is continuously supplied to the fuel cell. Oxygen(O2)and hydrogen (H2) are used as fuel inputs. In this research study, fuel cell is connected to a two-level inverter, and the inverter output is linked to a segregation transformer, whose output is connected to the grid. The primary goal of this study is to use renewable energy sources to lessen air pollution. There has been a lot of recent study on EVs powered by fuel cells. MATLAB/ Simulation software is used to validate the fuel cell results.
3D NAND Memory Operation of Oxide-Semiconductor Channel FeFETs and the Potential Impact of In-Plane Polarization

We have explored 3D NAND memory operation of oxide-semiconductor (OS) channel FeFETs by TCAD simulation with a multi-transistor NAND-string model. Key challenges in 3D NAND memory devices, such as (1) pass voltage disturb, (2) interference from adjacent cells, and (3) inhibit operation of unselected bitlines, are addressed. For a target device structure, operation voltages can be optimized to satisfy the requirement of (1)-(3). We have also studied the potential impact of in-plane polarization under the spacer. A comparative study shows that in-plane polarization under the spacer may lead to unexpected characteristics of OS-channel FeFETs in 3D NAND memory operation. This paper will provide insights on the feasibility of 3D NAND FeFETs for high capacity storage memory.
Voice Assistant and Gesture Controlled Virtual Mouse using Deep Learning Technique

The Gesture Controlled Virtual Mouse makes it simple to communicate with a computer using voice commands and hand gestures. The computer requires almost little direct physical contact. All input and output processes might potentially be managed digitally by combining voice instructions with both static and dynamic hand gestures. This study employs state-of-the-art Machine Learning (ML) and Computer Vision (CV) algorithms to recognize hand gestures and verbal commands, and it works without the usage of any additional hardware. It takes use of CNN-like models by utilizing MediaPipe, which is based on pybind11. It has two modules: one that operates directly on hands using MediaPipe Hand detection, and the other that makes use of gloves of any consistent colour.
A Wideband Bias Circuit with Low Parasitic Inductance for High-Power GaN Pulsed Power Amplifiers

In this paper, a wideband bias circuit with low parasitic inductance for high-power GaN pulsed amplifiers is proposed. The proposed bias circuit can ensure that the microwave can be normally transmitted from the power amplifier to the load, while preventing the transmission of microwave from the power amplifier to the power supply. At the same time, the parasitic inductance is reduced and the bandwidth of the bias circuit is increased by shortening the bias line and widening the microwave transmission line. It is valuable to reduce the drain voltage overshoot of high-power pulsed power amplifier and suitable for wideband power amplifiers.
Human Segmentation Using Commercial Wi-Fi Device

Image segmentation technology is widely used in automatic driving, smart home and other fields. However, due to the problem of privacy of image methods, they face restrictions when used in private occasions. In order to avoiding private problem image-based method, we proposed a human segmentation method based on commercial Wi-Fi devices named Wi-Seg, which is used to extract human silhouette information and generate pixel-level annotations. So that it can be integrated with existing human- computer interaction schemes using image segmentation, and avoiding the privacy problem of image segmentation methods. In this paper, the channel state index (CSI) in the Wi-Fi signal is collected as the original data, and a encoder-decoder deep neural network is designed to extract the human silhouette information from the CSI signal. Compared with the mainstream image segmentation methods, our Wi-Seg is superior to recent work that using Wi-Fi signal to extract human silhouette information, and performs as well as image segmentation methods.
A Transformer-Based Up-Conversion Mixer With Wide IF Bandwidth for Multi-Beam 5G New Radio

This letter presents a wideband millimeter-wave (mm-Wave) up-conversion mixer with high linearity for multibeam 5G new radio. It consists of a double-balanced switch stage designed by a ring topology and a separated gain stage realized by the common-source (CS) structure with cross-coupled capacitors. To improve the linearity of the circuit, the passive switch stage is placed before the gain stage. Transformer-based high-order matching networks for the mixer are also developed to broaden the RF, LO, and IF terminals’ impedance matching. The measured results show the mixer achieves an output 1-dB compression point (OP1dB) of 2.48 dBm with a conversion gain of 2.5 dB. Thanks to the transformer-based matching networks, the fractional matching bandwidths of IF, LO, and RF are up to 88%, 47.6%, and 26.4%, respectively.
Design and demonstration of Cu/Al2O3 /Cu RRAM with complementary resistance switching characteristic

To overcome the crosstalk problem originating from the sneak path current, a simple structure of $\text{Cu}/\text{Al}_{2}\mathrm{O}_{3}$ /Cu memristor was fabricated in this paper. Results showed that the device presented good bipolar resistive switching characteristic (BRS) as well as the endurance and retention performance. Most importantly, the complimentary resistance switching characteristic (CRS) appeared in this device by adjusting the testing parameters, which is important in crossbar array applications.
Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation

Machine learning is a versatile but little-understood approach in tunable RF electronics. This paper serves as the first to investigate the feasibility of taking inspiration from machine learning and the recent advances in digital twinning for designing liquid crystals-based reconfigurable devices in the regime of gigahertz (GHz) and terahertz (THz), targeting demanding applications in 5G/6G wireless backhaul links, satellite internet mobile terminals, and smart sensing. States of the arts are reviewed, with a prime design example of liquid crystals-based enclosed coplanar waveguide phase shifter illustrated, highlighting the opportunities offered and challenges encountered.
On shallow-water wakes: an analytical study

Analytical solutions for the characteristic scales of a turbulent wake in shallow flows are presented for two asymptotic cases: in one case, boundary-layer effects dominate whereas in the other, wake effects prevail. The latter case degenerates into the solution valid for an unbounded two-dimensional wake. These solutions show that the momentum deficit decreases exponentially in the longitudinal direction while the transverse velocity profile reveals a wake region characterized by a reduced velocity deficit compared to that of an unbounded wake. When wake-turbulence dominates there is a non-uniform turbulent viscosity in the longitudinal direction. These analytical solutions are compared with experimental data showing good agreement.
1T FDSOI Based LIF Neuron Without Reset Circuitry: A proposal and Investigation

In this article, an FDSOI-based neuron is demonstrated to mimic the functionalities of LIF neuron. As the proposed device-based neuron utilizes the single transistor latch (STL) mechanism and it takes the current input and voltage output in the shape of a spike, it does not require any extra circuit for reset. Furthermore, the proposed neuron shows an impressive energy consumption per spike i.e., less than 0.11 pJ/spike for all input values under 500 nA. Interestingly, it shows a spiking frequency in MHz range, which is ~5 orders greater than the biological neuron. Hence, due to its high frequency, energy and area efficiency, equivalent to that of a biological neuron and compatibility with CMOS process, the proposed FDSOI-based LIF neuron is more appropriate for large-scale hardware implementation of a spiking neural network (SNN).
High Gain Hybrid Integrated On-Chip Antenna Based on 40-nm Bulk CMOS Process

In this paper, a high gain hybrid integrated on-chip antenna (OCA) is designed based on 40-nm bulk CMOS technology. The OCA is in the form of patch and operates at the D-band. The OCA is a hybrid integrated configuration of patch antenna with off-chip ground and off-chip dielectric resonator (DR) to enhance the radiation performance. The designed OCA has a radiation efficiency and gain of 53% and 4.1 dBi at 150 GHz, respectively. Its impedance bandwidth relative to the center frequency is 13%. The radiation performance and bandwidth of the OCA are significantly improved compared with the traditional OCA. The antenna occupies 0.8×0.8 mm2. The OCA designed in this work solved the very ideal but challenging overall design of the future system, which achieved the goal of compactness and compatibility.
Passive Wi-Fi-based Radars with 802.11ax MU-MIMO Signals: AoD Estimation with a Single Antenna

Passive Wi-Fi based Radar (PWR) is a device that makes use of existing Wi-Fi signals to detect/track targets in the environment. Therefore, the radar processing at the PWR largely depends on the Wi-Fi signal and its characteristics. In the latest amendment of the Wi-Fi standard, namely 802.11ax, Multi-User Multiple-Input Multiple-Output (MU-MIMO) technology is used to improve the spatial efficiency of the wireless channel by precoding the transmitted signal for each user. However, from the PWR perspective, precoding affects the signal power that illuminates a given target depending on its Angle-of-Departure (AoD). In this paper, we first quantify the impact of precoded signals on PWR processing. Then, we show that it is possible to recover the AoD of well-illuminated targets by using a PWR equipped with a single-antenna. To do so, a three-step-scheme, which works by exploiting the MU-MIMO synchronization and precoded signal transmission, is proposed. The accuracy of the proposed method, as well as its shortcomings are shown through numerical analyses.
Design of an Extended Continuous Class-F Mode Power Amplifier Using Simplified Matching Network

The conventional continuous mode bandwidth cannot exceed one octave theoretically. This paper presents an ultra-wideband power amplifier based on extended continuous class F mode, implemented by a simplified output matching network. The output matching network consists of a harmonic control network and a fundamental matching network. The simulation results show that the PA can achieve the saturated output power of 39.6-41.1dBm and the drain efficiency of 55%-68% within the frequency range of 0.7-3.2GHz.
Disease Recognition of Crops using ResNet and MDFC-ResNet

In the field of agriculture, recognizing a crop disease is often a crucial task. Identification of crop disease directly impacts the treatment strategy for the disease. Without proper identification, the disease control measures could be waste of time and cost if an inappropriate approach is chosen. Ineffective measures have a negative impact on crop yield and food safety in addition to ineffectively treating diseases. Deep Convolutional Neural Networks (DCNN) with leaf image classification is used to identify crop diseases. The model identifies crop diseases and sends the results to the users through a web application. Residual neural network and multidimensional feature compensation residual network (MDFC-ResNet) model is used for recognizing the disease and their performance is compared. The models identify the species of crop along with the disease of crop. The models are capable of distinguishing diseased leaves and healthy leaves from the environment.
Easy and efficient algorithm to determine number of clusters

Many clustering algorithm request users to identify the number of clusters before cluster data.This is very difficult for users.In this paper,clusters which are bigger than intra similarity threshold value are split repeatedly.At last,the clusters which are smaller than inter similarity threshold value are merged to have the final number of clusters.Experiments show that the number of clusters identified by the algorithm is equal to the natural number of clusters,and the intra similarity is high,the inter similarity is low,so the algorithm is easy and efficient.