Miracle-dz/UniFAR-FTUs huggingface dataset:
IT Industry Service Management Tools for Managing Large Classes

Innovation Paper: With ever-increasing numbers of students entering university, large classes are becoming the norm rather than the exception for universities in many parts of the world such as Australia and some areas of the USA. This is particularly true in technical degrees such as engineering and IT. Cohorts of around 700 or even 1000 students are no longer uncommon. With so many students it can be difficult for academics to manage the administration of these classes, taking time away from curriculum development and teaching duties. Hundreds of email enquiries grace the modern academics’ inbox, and in addition, increasingly there are multiple communication channels to monitor such as Microsoft Teams, Slack, LMS discussion forums, LMS instant messaging, and many more.The IT industry has had a long history of dealing with large volumes of enquiries or in the case of software development, ‘bug’ reports. Further, tools developed by the IT industry have increasingly been seen outside the IT sector to help with nonIT service delivery. This project sought to use IT industry knowledge and tools to manage the administration of our large first-year engineering class Introduction to Engineering Projects, a project subject that sees approximately 1000 students per year asymmetrically split between approximately 700 students in the first semester and approximately 300 students in the second semester. To that end, in the second semester of 2021 (July – November) we implemented a service management tool, Atlassian’s Jira Service Management Cloud, to manage our large volume of student enquiries, replacing traditional emails and most other communication channels.While we discovered these tools can be complex to set up, once set up they significantly improved academic-student communication and communication management. The tool allowed for emails to also feed into the enquiry portal, simplifying where students submit enquiries to coordination staff. Staff found it easier to respond to queries, clarity on which enquiries had been dealt with or who was responsible for responding to open enquiries, and students found we communicated with them in a timely manner. A further trial will be run in our larger first semester in 2022 (March – June). We would recommend academics consider IT service management tools for communication/query management of their large classes, especially where responsibility for responding to enquiries is delegated among a team and not just a single individual.
CURUMIM: A Proposal of an Intelligent Tutor System to Teach Trigonometry

This article presents a proposal for an Intelligent Tutoring System (ITS) for teaching Trigonometry in K-12 programs, using the teacher as an active agent in the learning process and employing the assumptions of the Theory of Mediated Learning Experiences (MLE). In the current scenario where Distance Learning (DL) has become a necessity, ITS offer an alternative to individualized study, however, for the construction of knowledge, mediation in learning is often necessary. MLEs offer pedagogical support focused on mediating and addressing learning problems, desirable characteristics for disciplines based on problem solving. Therefore, this research aims to present a proposal for an ITS to support the development of mathematical knowledge in trigonometry problems, inserting the teacher as an active agent in this process, exploring the potential of the MLE for the construction of the ITS. The proposed system, called CURUMIM, also has a Pedagogical Virtual Assistant (PVA) with the function of clearing up doubts arising from the subject, generating a dialogue according to the student’s reciprocity index.
Image Augmentations in Planetary Science: Implications in Self-Supervised Learning and Weakly-Supervised Segmentation on Mars

Research on the use of augmentations in physically constrained remote sensing scenarios, like the analysis of Martian surface data, is largely unexplored. In this work we present an analysis on how reasonable augmentation strategies can be selected which are class agnostic and respect physical plausibility in supervised and weakly-supervised tasks. Additionally, we present the first results of self-supervised learning on Martian surface data, discuss the importance of physically plausible augmentations in the context of self-supervised learning, specifically contrastive learning, and provide a comprehensive overview of the generalization properties induced by different augmentation strategies with the help of geomorphic maps.
Nonintrusive Load Disaggregation Combining with External Attention Mechanism and Seq2Piont

Nonintrusive load disaggregation can deeply explore the value of customer power consumption data, providing a reference for decision analysis such as power equipment fault monitoring and demand response. Aiming at the conflict between training time and accuracy of nonintrusive load disaggregation, a novel nonintrusive load disaggregation algorithm combining an external attention mechanism with Seq2Point (Seq2Point-EA) model is proposed. Firstly, preprocess the original data by data purification, normalization, and some other operations, and build the training data with a same length window. Secondly, extract the appliance feature through the encoder layer. Thirdly, enhance the feature weights of important parts by introducing an external attention mechanism. Lastly, yield the results through the decoder layer. The proposed model was tested on the REDD dataset, and compared with the state-of-the-art model. The proposed model improves the disadvantage of distraction of the convolutional layer and enhances the extraction and utilization of effective information without increasing the training time cost.
Design of a Circulator-Based Sequential Doherty Power Amplifier with Octave Bandwidth and Extended Dynamic Range

This paper presents a novel load-modulated power amplifier (PA), named as circulator-based sequential Doherty power amplifier (CSDPA). A 3-port non-reciprocal circulator is utilized as a combiner in the proposed CSDPA, leading to a high efficiency over a wide bandwidth and large dynamic range. It is illustrated that the main PA in the CSDPA sees a constant load impedance over the whole power range, while the auxiliary PA sees a load-modulated impedance. The constant load impedance of the main PA enables the CSDPA achieve high back-off efficiency over a wide bandwidth. Moreover, the main PA reaches saturation at output back-off (OBO) power level. This means the output power will be transferred from the main PA to the auxiliary PA after it is turned-on. As a verification, a CSDPA working over 0.6-1.2 GHz (an Octave bandwidth) is designed using 6W GaN transistors and a commercial circulator. This CSDPA delivers a maximum output power of 39.2-40.2 dBm with a drain efficiency of 59.7%-78.6% and a gain of 11.2-12.2dB. Meanwhile, the designed CSDPA achieves a drain efficiency of 47.7%-61.4% at 8 dB OBO power level.
Losses Analysis of Induction Motors under Ambient and Cryogenic Conditions

Cryogenic induction machines have been re-emerging as a potential solution to increase the current power density for challenging specific power applications. Compared with an ambient temperature operation, cryogenic conditions allow higher power density and increased efficiency due to the decrease of electric resistivity of conducting materials and increased cooling capability. This work focuses on the losses analysis of induction machines immersed in liquid nitrogen. Under ambient and cryogenic conditions, experimental tests are performed and presented for two induction machines, a 90 W 40 V induction machine, and a 550 W 400 V induction machine, to evaluate the mechanical, iron and residual losses, and the change of their equivalent circuit parameters.
Dog Monitoring System Powered by Deep Learning

Dogs are man's best friend and they bring endless fun to our lives. But we can't be with our dogs all the time, for example, when we go out to work, we lose the information about our dogs' behavior at home. So, in this paper, we propose an approach to the lightweight pet monitoring system that is reinforced with a deep learning algorithm. Real-time footage of the dog going about its day-to-day routine is captured in frames per cycle. The image form of the frame is used as testing data and ran up against a YOLOv5 model that is trained on images grouped into several classes that represent essential dog activities such as eating and walking. This paper twists the generic use of image classification to pose detection or pose estimation. The predictions made by the YOLOv5 model are then returned and saved under the activity tracker which will describe the dog's current activity, also these activities can be viewed in the GUI created for the paper. The modified YOLOv5 model is the perfect object detection algorithm to keep up with these demands.
Research on Trajectory Tracking Control of Traction upper Limb Rehabilitation Robot Based on Feedforward Impedance Theory

The goal of this paper is to ensure the safety of human-computer interaction during passive upper-limb rehabilitation robot training. For the robot mobile platform, external environment contact force feedback control is required, as well as the capacity to regulate high-precision motion trajectory tracking. As a result, the feedforward impedance theory is used to create a trajectory tracking control method. The method may successfully increase patient safety when using upper limb rehabilitation robots, as well as improve patient rehabilitation efficiency. This study creates a virtual prototype and simulation experiment platform using a hybrid simulation of 3D modeling, finds the optimal control effect solution through many experiments on the simulation platform, then analyzes the algorithm. The algorithm will eventually be transferred to the prototype of the experimental platform for further verification and analysis in order to evaluate its dependability. The results show that the method can monitor a trajectory with great precision, and that the desired trajectory can be corrected. Keyword— Upper limb rehabilitation robot, Impedance control, Feedforward control, Human-computer of interaction, Track tracking.
Integrated Single-Stage EMI Filters for Grid-Tied Voltage Source Converters: A Design Oriented Approach

This work introduces and elucidates a general framework for designing and evaluating the performance of a standard single-stage, multi-order integrated electromagnetic interference (EMI) filter for grid-tied, 3ϕ voltage source converters (VSCs). The proposed methodology is based on designing the EMI filter to meet power quality (PQ) requirements while providing adequate attenuation to both differential mode (DM) and common mode (CM) noise generated by the 3ϕ VSC to comply with conducted emissions (CE) recommendations. The impact of switching frequency of the power converter on the filter size is investigated to understand the performance limits of single-stage EMI filters and to offer guidelines as to when such a single-stage filter is feasible. A size comparison for the magnetic components used in the EMI filter has been performed using an area product approach. Additionally, various single-stage CM and DM filter performances have been analyzed and compared. A hardware prototype with a single-stage EMI filter has been implemented based on the proposed design guidelines. Time-domain and CE experimental results for a 3ϕ VSC prototype interfaced to the utility grid are provided to showcase the validity of the single-stage integrated EMI filter design to meet multiple performance constraints.
Looking beyond the West for emerging leaders in engineering education reform: A case study of two Chinese engineering schools informed by neo-institutional theory

This work-in-progress paper considers the current state of curriculum reform in engineering education in China (presently the world’s largest producer of engineering graduates), with an empirical focus on innovative curricula at two prominent schools. Tsinghua University is a well-established leading institution of engineering education in China, and likely the best known internationally due in part to its inclusion in the MIT report The Global State of the Art in Engineering Education. The Southern University of Science and Technology (SUSTech) is a new university established just over ten years ago, which is nonetheless already prominent on the national stage for its innovative engineering programs. Our study compares the Tsien Class at Tsinghua University with the SDIM curriculum at SUSTech and finds that while they are both drawing on global ideas to design these innovative curricula designed from scratch, that the local contexts also influence the forms of curriculum adopted. The Tsien Class is very much focused on academic research, and has individual students designing projects and building global links. SDIM is focused on industry collaboration and uses a project-based learning design which has students working in groups on relevant interdisciplinary projects.
An Algorithm for Generating All Subsets in Lexicographic Order

The algorithms for generating all subsets of a given set, like many other generating algorithms, are of two main types: for generating in lexicographic order or in Gray code order. Many of them use binary representation of integers (i.e., binary vectors) as characteristic vectors of the subsets.Here we consider the set Un of n elements that are ordered according to a given total order relation. We propose the ordering of characteristic vectors and their serial numbers (i.e., their corresponding integers) such that the characteristic vectors define a lexicographic ordering of subsets of Un. To get this, we define and study the properties of three sequences: (1) pn—of all subsets in lexicographic order, (2) cn—of characteristic vectors corresponding to pn, and (3) sn—of integers representing the vectors of cn. We then propose a simple, straightforward, and fast algorithm that, for a given n, 1 ≤ n ≤ 64, generates the sequence sn. This algorithm only performs integer additions. Its time and space complexity is of the type Θ(2n)—exponential with respect to the size of the input n but linear with respect to the size of the output 2n. The algorithm was used in the creation of sequence A356120 in the OEIS [1]. Finally, the general case where n is a positive natural number and the generation of the subsets themselves, i.e., the sequence pn, are discussed.
Analytical Modeling of Air-Cooling System for Multiple Discrete Device Parallel Converter

Following the continuous increasing of power density of nowadays power converter, more and more attention is paid to thermal system design, since its performance is the key index to affect the power density and also determine the reliability of power converters. The rectangular fin-type air cooling heatsink is a common heatsink design in power converters. The modeling of such heatsinks become an important step for power converter design optimization. This paper proposes an analytical model for air-cooling system with multiple discrete device used in parallel. In this model, an air temperature rise model is constructed to reveal the air temperature distribution considering the position of the power devices and the flow direction of air in the heatsink channel. And a correction term with thermal diffusion angle is introduced to solve the mismatch problem between the size of the power device and the surface area of heatsink. Finally, the simulation results verify the validity of the proposed model and its accuracy.
PSND: A Robust Parking Space Number Detector

It is necessary to detect the license plate and the parking space number in autonomous driving. Due to the complex environment of the parking space, it is challenging and interesting in parking space number detection. This paper proposes a robust Parking Space Number Detector (PSND), which enhances the Differentiable Binarization (DB) model by introducing a cascaded feature enhancement module and context attention block. Our method has better feature extraction ability and a better detection effect for long text than the DB model. Meanwhile, we collected and annotated a dataset containing 9000 parking space number images to train and test our model. Extensive experiments demonstrate that our method achieves better or competitive performance in terms of accuracy on various standard benchmarks, including MSRA-TD500, ICDAR2015, CTW1500, and Total-Text datasets while maintaining the real-time detection speed. The high recognition accuracy makes it possible for the model to be practically applied.
End-to-End Task-Guided Refinement of Synthetic Images for Data Efficient Cerebral Microbleed Detection

Data scarcity is a fundamental obstacle to the adoption of deep learning models for a variety of detection tasks in medical images due to inherent difficulties in acquiring and labeling such images. This is particularly so for cerebral microbleed (CMB) detection in community cohorts where datasets are small and targets are hard to find. While numerous methods have been proposed for leveraging unlabeled data and semi-supervised learning to improve detection performance, unlabeled data may not be available in some domains. Data synthesis provides an alternative approach for increasing the size of the dataset but learning a realistic synthesis model using state-of-the-art methods such as generative adversarial networks suffers from the same data scarcity challenges as the detection model. In this work, we propose an end to end approach for refining synthesized examples developed using a priori domain knowledge, in order to improve detection performance using limited amounts of actual data. We combine detection and synthesis in a single network and incorporate detection loss into the synthesis refinement block in order to encourage the generation of realistic synthetic data, while concurrently updating the detection model trained with a small set of real data to enforce realistic synthesis and prevent overfitting. We demonstrate the effectiveness of our approach to improve CMB detection on postmortem MRI data and show significant improvements in average precision over baseline data synthesis and adversarial approaches.
Spatial-channel attention-based stochastic neighboring embedding pooling and long-short-term memory for lung nodules classification

Handling lesion size and location variance in lung nodules are one of the main shortcomings of traditional convolutional neural networks (CNNs). The pooling layer within CNNs reduces the resolution of the feature maps causing small local details loss that needs processing by the following layers. In this article, we proposed a new pooling-based stochastic neighboring embedding method (SNE-pooling) that is able to handle the long-range dependencies property of the lung nodules. Further, an attention-based SNE-pooling model is proposed that could perform spatial and channel attention. The experimental results conducted on LIDC and LUNGx datasets show that the attention-based SNE-pooling model significantly improves the performance for the state of the art.
Semantic Texture Complexity Model for Feature Generation and Selection in Visual SLAM

Simultaneous Localization And Mapping (SLAM) plays an irreplaceable role in autonomous driving, robotics and augmented reality for whose ability of locating and providing the map of the unknown environment. The traditional SLAM algorithms describe the environment by extracting, tracking and matching the local feature points, which often leads to the feature points gathering in certain areas and affects the performance of pose detection. In order to obtain a SLAM environment map with accurate representative feature points, this paper proposes a semantic texture complexity model for feature generation and selection. The semantic texture complexity model respectively evaluates the texture complexity of each semantic area so that even the area with few corners or edges can be represented by feature points. For different semantic regions, different feature point generation and selection schemes in the Visual-Inertial Odometry (VIO) module are adopted according to the texture complexity, which avoids the concentration of feature points and effectively improves the positioning accuracy. In this paper, we use the uHumans2 dataset to test the positioning accuracy. The test results show that the proposed algorithm reduces the error by 10%-30% compared with the related work.
High overtone acoustic resonator HBAR based on IDT's/c-tilted ZnO/Si for timing applications

In this paper, the frequency characteristics of high overtone bulk acoustic modes, generated by interdigital transducers IDT's on c-tilted ZnO/Si, are theoretically and experimentally investigated. The origin and characteristics of high overtone acoustic modes in ZnO piezoelectric layer on silicon substrate are discussed and one port HBAR resonator, based on c-axis tilted ZnO/Si, is fabricated and tested by network analyzer. The results achieved in this work are of interest in design and fabrication of radiofrequency sources and electronic timing devices based on thin film technology.
Low Complexity Fast Grid-Based Refining Segmentation in the V-PCC encoder

The video-based point cloud compression (V-PCC, ISO/IEC 23090–5) is the state-of-the-art international standard for compressing dynamic dense point cloud. Despite its good rate-distortion (RD) performance, excessive encoding complexity makes real-time V-PCC applications difficult to implement. One of the most computationally intensive modules of the V-PCC encoder is the fast grid-based refine segmentation (FGRS). This paper proposes fast and efficient radius search method for the FGRS. The proposed radius search method works twice as fast as the conventional k-d tree radius search in the FGRS. In addition, overall coding efficiency is slightly improved by applying a coarse-to-fine approach to sorting search results according to geometric priority. Simulation results show that the proposed method reduces the FGRS time by an average of 47% with a slight coding gain.
Nonlinear Position Control of a Magnetic-Geared Servo Drive With Unknown Load

Magnetic gears (MGs) emerged as an attractive alternative to conventional mechanical gears and their application in different fields, including servo drives, is already being considered. However, high-performance control of magnetic-geared drives is not an easy task due to the low stiffness and highly nonlinear characteristics of MGs. This paper presents a nonlinear approach to position control of magnetic-geared servo drives based on exact feedback linearization and load torque estimation. Simulation results show that the proposed nonlinear controller exhibits excellent tracking and disturbance rejection properties, and that it ensures the same high-performance dynamic behaviour in the entire operating region of the drive.
Dynamic Target Following Control for Autonomous Vehicles with Deep Reinforcement Learning

Target following control is an operating condition for autonomous driving in vehicle platooning and service robots. But the model-based target following control is challenging due to the nonlinearity and uncertainty of the vehicle dynamics model. To address this problem, this paper proposes a dynamic target following control method based on deep reinforcement learning for autonomous vehicles. Its primary purpose is to make the car follow dynamic targets smoothly and reasonably. Only collected state data sets are used to learn control policy. The policy learning consists of two steps. Firstly, we describe a specific following situation in which we design a dense reward function for realizing following control. We use empirical data to pre-train with deep neural networks. Then the deep deterministic policy gradient (DDPG) algorithm is used to train the control policy to achieve continuous optimization. We conducted experiments with Carla simulation software, including the comparison with the supervised learning-based approach. The results show the effectiveness of our approach.