Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved CNN-Based Apple Appearance Quality Classification Method With Small Samples

Apple quality classification is an important means to refine apple sales market and promote apple sales. At present, most of classification methods based on a convolutional neural network (CNN) depend on the quantity of training samples to get good performance. But due to the lack of large-scale public apple appearance dataset, it is a big challenge to obtain high accuracy of apple appearance quality classification with small samples. Therefore, we propose an improved method based on CNN for apple appearance, quality classification with small samples. Firstly, support vector machine (SVM) is used for image segmentation to avoid the decrease of recognition accuracy caused by environmental noise. Secondly, the segmented image data are input into deep convolutional generative adversarial networks (DCGAN) model, which is used for data expansion. Thirdly, the improved ResNet50 (Imp-ResNet50) is proposed as follows: Replace the fully-connected layer with global average pooling layer; Add the dropout algorithm and batch normalization algorithm at the fully-connected layer; Replace the activation function ReLU with Swish. Through comparative experiments with 360 apple images, we verify the performance of the proposed method including the training image quality, the running time, and classification accuracy. The result shows that the proposed method can obtain high quality training samples and reduce the running time of the method effectively. At the same time, it can realize higher classification accuracy that is up to 96.5%, which is higher than the previous classification method.
The Use of Health Code to Control the COVID-19 Pandemic: Achievements, Evaluations and Future Prospects

The ongoing COVID-19 outbreak is the most serious public health crisis in the world in centuries. In its efforts for the epidemic, China has innovatively used the "health code" as a governing tool. The method has won applauses from many yet raised concerns and discussions from others. This paper reviews the basic concept, origins, contents, functions, use, technical framework, data sources, data management and data transmission of the health code for the pandemic control. It also reports popular evaluations of the effectiveness of the methods, its remaining issues and problems, rooms for improvements. Issues and problems discussed include precise identification and positioning risk groups, realization of efficient supervision of administrative rating and classification, reduction of discretional space, provision of discretional benchmarks, reduction of administrative execution burden, reduction of administrative costs, regional integration and collaboration, digital security, and privacy concerns. The authors then discuss the future implications and prospects of health code for intelligent health management and its potential to lead to other areas of smart city building.
An Analysis of Testing Scenarios for Automated Driving Systems

Automated Driving System refers to a vehicle system where hardware and software are collectively capable of on-road operational and tactical functions and such functions involve the detection, recognition, classification of objects and response to events. Many automotive companies are incorporating automated driving into their current R&D and are transforming their business models. To both conventional and disruptive manufacturers, safety is always one of the top priorities. Appropriate verification and validation procedures are needed and should be followed to mitigate unreliability and hazardousness. Sufficient testing scenario should be considered and planned to simulate and cover functional and non-functional requirements. Disengagement ratio serves as an indicator during performance evaluations because analysing root causes of both technical and non-technical disengagements is pivotal especially during testing strategy planning. Autonomous Vehicle Disengagement Reports and Autonomous Vehicle Collision Reports from the Department of Motor Vehicles (DMV), California, USA are collectively used for the purpose of this research. And the analytical result shows there is no clear relationship between mileage and disengagements. Influencing factors are generated and consolidated from the mentioned reports and are proposed in addition to a Society of Automotive Engineers International (SAE International) standard. Stakeholders will benefit from the presented rationales and consider the suggestive parameters throughout their developing and testing activities. This paper further recommends testing management for automated driving systems, especially test driver management and test routes planning. And the recommendations are in accordance with the analytical results and feedback from KPMG s Global Automotive Executive Surveys.
A Framework for Detecting and Summarizing Students' Typical Errors in English Teaching

With the development of online education, the mining and analysis of educational data has become especially important. In teaching, detecting students' typical errors is an extremely important factor for higher teaching efficacy. Most of the current researches use clustering or decision tree algorithms for partitioning. However, these algorithms more or less fail to recognize the connection between students and the errors they make, and cannot effectively and intuitively derive their typical errors. This paper proposes a framework that combines community detection and association rules to detect students' typical errors in English teaching. First, the framework adds the error auxiliary nodes and obtains the student's error communities and typical errors. Second, it calculates the errors' frequent itemsets, and mines the association rules between errors. And last, it combines the association rules with the error communities to supplement the potential errors, which effectively summarizes students' typical errors in their learning process.
Flexible piezoresistive sensors with 3D CB/graphene conductive networks

Herein, flexible piezoresistive sensors with 3D surface-filled (SF) carbon black (CB)/graphene (GR) conductive composite layers were fabricated through 3D printing and modified dip-coating process, which show high stretchability of 15.5 times, good sensitivity of200. 73 kPa−1, high gauge factor of 17.20 and excellent stability (>2000 cycles). In addition, the novel sensor demonstrated the applications in detecting tiny and big human activities, exhibiting its great potential for application in wearable electronics.
EduEasy - Smart Learning Assistant System

Usage of smart learning concepts have increased rapidly recently as better teaching and learning methods. Smart learning concepts are especially useful for students to learn better in large classes. In large classes, the lecture method is the most popular method of teaching. In the lecture method, the lecturer presents the content mostly using lecture slides and the students make their own notes based on the content presented. However, some students may find difficulties with the above method due to various issues such as speed in delivery. The purpose of this research is to assist students in large classes in the following content. The research proposes a solution with four components namely note taker, slide matcher, reference finder, and question presenter which are helpful for the students to obtain a summarized version of the lecture note, easily navigate to the content and find resources, and revise content using questions.
Modelling and Real Time Simulation of Microgrid using Typhoon HIL

This paper presents the modeling and design of a simple microgrid consisting of two sources and two loads using a Typhoon Hardware-in-loop (HIL) simulation package with HIL402 configuration in the virtual environment. The performance of two sources on different loads is considered. The contactors are used to find the behavior of the sources on the loads. When the Source contactor is operational, two sources are supplying to two loads and when it is disconnected, two sources are supplying to a constant power load with frequency droop characteristics. The load contactor is activated by connecting it to another resistive load in parallel to the existing one. As the constant power blocks are modeled as current sources, a core coupling component is added to avoid degeneration in the circuit. The simulation signals are monitored with SCADA widgets using a wide selection of measuring and monitoring instruments.
Design Study on Additive Manufacturing Technologies in Permanent Magnet Synchronous Machines for Electric Aircraft

This paper describes the use of Additive Manufacturing (AM) technologies in permanent magnet synchronous machines (PMSM) for electric aircraft. Due to economic aspects and government restrictions, conventional manufacturing technologies like punching reach their limits when aiming for fulfilling future requirements. In the past years, AM has become a more and more relevant technology. In this paper, we present an investigation on how AM can be used in order to improve electric machines for electric flight propulsion. More precisely, the aim is to obtain an increasing torque and power density. Nevertheless, giving a brief overview of the feasibility of AM in electric aircraft is focused more than discussing technical issues in detail in this paper. The research starts with an extensive analysis on the current state of research of AM on electric machines and an evaluation of the degree of maturity. Moreover, geometric approaches for the rotor and the winding, which can only be built by AM, are pointed out and will be evaluated. Therefore, the winding and the rotor are modified. This results in lower ohmic loss density and lower eddy current loss for the winding as well as in higher torque density due to the decreased rotor leakage flux. So finally, the power density is increased which is crucial for flight propulsion. In summary, the huge potential of AM in all-electric aircraft is presented and performance improvements are quantified in this paper.
Acoustic Scene Classification using Deep Learning Architectures

Enabling devices to make sense of sound is known as Acoustic Scene Classification (ASC). The analysis of various scenes by applying computational algorithms is known as computational auditory scene analysis. The main aim of this paper is to classify audio recordings based on the scenes/environment in which they are recorded. Deep learning is amongst the recent trends in most of the applications. In this paper, two deep learning algorithms are used to perform the classification of acoustic scenes, namely Convolution Neural Network (CNN) and Convolution-Recurrent Neural Network (CRNN). The model is evaluated on three activation functions, namely, ReLU, LeakyReLU and ELU. The highest recognition accuracy achieved for ASC task is 90.96% from CRNN model. The model performed well on basic convolution architecture with 10.9% improvement from the baseline system of this task.
An Adaptive Loop Gain Tracking Digital PLL Using Spectrum-Balancing Technique

A digital phase-locked loop (DPLL) using the proposed adaptive loop gain controller (ALGC) is presented. The ALGC uses a spectrum-balancing technique to detect the difference of the high-frequency and the low-frequency powers of the bang-bang phase-frequency detector output. Then, the loop gain of the DPLL is adjusted to minimize the output root-mean-square (RMS) jitter. This DPLL is fabricated in 40-nm CMOS process and its active area is 0.016mm2. Operating at a frequency of 3.2 GHz, the power consumption of the DPLL is 1.5mW from a 1V supply voltage.
A review of recent MOSFET source and drain resistances extraction methods using a single test device

The performance of MOSFETs is limited by the presence of parasitic series resistances. This article reviews three recent extraction methods to determine the values of drain and source series resistances from the measured drain current of a single three-terminal transistor. These methods can also be used in four-terminal MOSFETs.
Associative learning circuit based on synaptic transistors with temporal dynamics

Neuromorphic computing holds great potential for future artificial general intelligence, which requires device elements that can mimic the functionalities of biological units. Here, an ion gated synaptic transistor based on Mos2is demonstrated, which produces a diversity of short-term plasticity that is an enabling factor for efficient temporal information processing. A circuit based on such synaptic transistors is constructed to implement Pavlov's associative learning behavior, demonstrating the potential of the devices in brain inspired computing.
MSR4ML: Reconstructing Artifact Traceability in Machine Learning Repositories

The increasing popularity of Machine Learning (ML) is generating challenges also for developers. The multitude of programming languages, libraries and available resources allow them to easily build their own models or algorithms. However, ML models are tightly connected to their data implying a different development process from other types of software. Software projects often rely on version control platforms, such as GitHub, but these platforms have not yet been extended to support ML projects. There is poor support for data versioning and no link between ML and software artifacts. Thus, traceability and model evolution can become challenging for developers. While some specific ML platforms exist, they still require considerable manual specification of ML artifacts and links between them. In this work, we propose a framework for automatic identification and traceability of links between data, code and ML model through Mining Software Repositories (MSR) techniques. Our tool combines static code analysis and mining commit data to identify ML, code and data artifacts, reconstruct links between them and retrieve commits that affect each end of the link. The objective is to increase productivity and the developers’ awareness of their project through the recovered traceability.
Computationally Efficient System-Level Evaluation of Battery Electric Vehicles

This work is about efforts made for achieving a system-level evaluation of battery electric vehicles. With the electric machine being the centre stage, all major components are considered, including the battery, the power electronics, the gearbox, and characteristic parameters of the car like its mass, air drag, and the wheel diameter. Several standardized driving cycles are used for the system-level evaluation. The modeling is achieved through performing finite element analyses, while the computational cost is minimized by applying surrogate modeling techniques. A test scenario is defined, where typical parameters subject to change are characterized through distribution functions. Their impact is analyzed by focusing on the associated change of the cruising range. Results reveal the significance of any parameter change and thus facilitate to gain deep knowledge about what affects the performance of battery electric vehicles most.
Pneumonia Detection from Chest X-ray using Transfer Learning

The use of computing technology to aid modern medicine advancement has revolutionized healthcare. Quick and accurate diagnostic tools help professionals start the necessary treatment as soon as possible, saving millions of lives. Pneumonia, a symptom of Covid-19, is a life-threatening condition that affects the lungs and can be detected by analyzing X-ray scans of the chest. The study highlights Convolutional Neural Networks, develops and trains models such as VGG16, ResNet-50, and InceptionV3, to detect pneumonia with improved testing accuracies of 94%, 93.9 %, and 93.5 %, respectively. The work discusses and compares the implementation and performance of these models.
Extremely Sparse Co-Prime Acquisition: Low Latency Estimation using MUSIC Algorithm

Extremely sparse co-prime sensing is a recently proposed sub-Nyquist acquisition strategy. Spectrum estimation using the standard correlogram and periodogram method was presented. It demonstrates low latency estimation. This paper presents the results for spectral estimation using the MUSIC algorithm available in MATLAB. This is the first such attempt to demonstrate low latency estimation for the extremely sparse co-prime scheme with a subspace based method like MUSIC algorithm.
Revisiting the definition of ferroelectric negative capacitance based on Gibbs free energy

The applicability of the Gibbs free energy landscape to the definition and understanding of negative capacitance in a ferroelectric capacitor is investigated. We find that the Gibbs free energy cannot define the transient negative capacitance. In order to define the negative capacitance, we suggest returning to the original definition of capacitance, which is the second-order derivative of electrostatic force work with respect to polarization.
Vertical Oil-in-Water Flow Pattern Identification with Deep CNN-LSTM Network

Oil-in-Water two-phase flows widely exist in various industrial applications. Identifying the flow patterns is of great importance for the optimization of the oil-water two-phase flow control system. In this paper, a Deep CNN-LSTM network is proposed to extract the spatial-temporal features of the vertical oil-in-water two phase flows. Then the flow patterns of the vertical oil-in-water two phase flows are identified with the extracted spatial-temporal features. The testing results on our data set show that the proposed network can effectively identify the typical oil-in-water two-phase flow patterns in vertical pipes with a relatively high accuracy.
A Reliable Approach to Secure IoT Systems Using Cryptosystems Based on SoC FPGA Platforms

The use of cryptography algorithms based on hardware to protect the security and privacy of the loT systems is in huge demand. However, hardware-based security systems are similar to software-based in that they can be attacked by a type of malicious code called malware. In this article, we used the self-applied cryptography algorithm on the hardware-based to protect the data security of loT systems, and that gives the trustee than using software-based of hardware-based inform of ASIC chip. Detailly, we have developed an SoC FPGA system including main factors are a built-in ARM processor core, a self-implemented IP core acting as 128-bit AES co-processors, and SDRAM memory. Furthermore, this system has been placed on the DE10-Standard board and ran with an application in Linux operations. Besides, we have applied the DMA technique to obtain high-speed data transfer. As a result, our cryptosystem is compact and can achieve high performance with low frequencies. Details, the DMA Controller and 128-bit AES IP core operate at a frequency of 150 MHz, and they can reach the speed of 1200 Mbps and above 700 Mbps, respectively.
Analysis on Deep Reinforcement Learning in Industrial Robotic Arm

Deep reinforcement learning is a combination of reinforcement learning and deep learning. It allows the robot to learn new tasks on its own. In recent years, many studies have applied deep reinforcement learning algorithms to the manipulation of robotic arms, and have achieved excellent results. This article described the basic knowledge of deep reinforcement learning and analyzed the current problems faced by industrial robotic arms. By reviewing the main research that researchers have applied deep reinforcement learning algorithms to the field of manipulator operation in recent years and the development of related deep reinforcement learning algorithms. It concluded that how deep reinforcement learning can solve the problems faced by industrial robotic arms. Finally, this article referred to the challenges faced by the application of deep reinforcement learning and its application in the field of industrial robotic arms and then made a detailed analysis and explanation.
Energized IoT devices through RF Wireless Power Transfer

Wireless Power Harvesting (WPH) of scale is the next stage in low power electronics. In this research, we study to energize wirelessly smart Internet of things (IoT) devices. The IoT and WPH offer a solution to encourage the engineers and growth of modern societies building cell blocks. The idea is a solution to electricity all sensors and devices wirelessly from radio frequency (RF) energy. With RF harvesting technology, we upgrade more IoT devices and keep our lives smarter and more relaxed. The vision of future technology is to self-energize and charge any IoT devices without wires. In this paper, a design patch antenna and four-stage voltage multiplier for RF energy harvesting device with microwave energy 2.45 GHz as the input source. The objective of the work scope is to combine wireless power harvesting technology with a smart house. The proposed prototype takes all parameters above to deliver sufficient energy to remote powering the LED bulbs 5 W wirelessly on a distance of more than five meters at the smart Lab. Finally, we conclude that the harvester circuit coupled with patch antenna can power up the bulb, ultimately IoT devices battery-less.
Smart Community Building in Hangzhou and Shenzhen, China: Lessons and Experiences

The paper discusses smart community building experiences and lessons in two of China’s prominent cities: Shenzhen and Hangzhou, both have over 10 million population. The authors reviewed their understanding of the concept of smart cities, their policies and implementation strategies, achievements, issues and problems, and lessons and experiences learned, and future prospects. Efforts are also made to examine smart community practices in other areas of China and in foreign countries. In the end, the authors proposed suggestions for further improvements for future practice.
Reduction of Garbage Outputs and Constant Inputs in Design of Combinational Circuits Using Reversible Logic

Reversible logic is one of the latest technologies for low power design. This paper focuses on reducing the garbage output (GO) and constant input(CI) in combinational circuits. GO and CI are some of the parameters in Reversible logic. Two new reversible gates DINV ($3^{\ast}3$) and TwinSJ ($5^{\ast}5$) are designed. 3:8, 4:16 and 5:32 Decoders are the circuits built. Garbage output reduction of 25 to 66 percent is achieved. Also 12.5 to 75 percent reduction of constant input is achieved.
Improved Propulsion Motor Design for a Twelve Passenger All-Electric Aircraft

This paper investigates the design of a 12-seater aircraft electric propulsion motor (ProMo). The design procedures aim to improve the performance of a previously designed motor, while adding further restrictions to the electrical and thermal boundaries. The main targets are to achieve the required output power of 550 kW, low torque ripple and an efficiency higher than 97%. The proposed procedures include an optimum slot/pole combination study, investigation on the winding configuration and connection, scaling of the airgap thickness and permanent magnet (PM) thicknesses, and finally minimising of the motor volume. The electromagnetic design is performed through a finite element electromagnetic software package, whereas the thermal analysis uses a lumped parameter thermal network-based. The mechanical stress above the rated speed of 2000 rpm is also examined. The final design fulfils the design requirements and meets the electrical and thermal constraints.