Miracle-dz/UniFAR-FTUs huggingface dataset:
Chest X-Ray Outlier Detection Model Using Dimension Reduction and Edge Detection

With the advancement of Artificial Intelligence technology, the development of various applied software and studies are actively conducted on detection, classification, and prediction through interdisciplinary convergence and integration. Among them, medical AI has been drawing huge interest and popularity in Computer-Aided Diagnosis, which collects human body signals to predict abnormal symptoms of health, and diagnoses diseases through medical images such as X-ray and CT. Since X-ray and CT in medicine use high-resolution images, they require high specification equipment and huge energy consumption due to high computation in learning and recognition, incurring huge costs to create an environment for operation. Thus, this paper proposes a chest X-ray outlier detection model using dimension reduction and edge detection to solve these issues. The proposed method scans an X-ray image using a window of a certain size, conducts difference imaging of adjacent segment-images, and extracts the edge information in a binary format through the AND operation. To convert the extracted edge, which is visual information, into a series of lines, it is computed in convolution with the detection filter that has a coefficient of 2n and the lines are divided into 16 types. By counting the converted data, a one-dimensional 16-size array per one segment-image is produced, and this reduced data is used as an input to the RNN-based learning model. In addition, the study conducted various experiments based on the COVID-chest X-ray dataset to evaluate the performance of the proposed model. According to the experiment results, the LFA-RNN showed the highest accuracy at 97.5% in the learning calculated through learning, followed by CRNN 96.1%, VGG 96.6%, AlexNet 94.1%, Conv1D 79.4%, and DNN 78.9%. In addition, LFA-RNN showed the lowest loss at about 0.0357.
Real-Time 3D Object Detection From Point Cloud Through Foreground Segmentation

This paper aims to apply real-time light-weight high-precision 3D detection for autonomous driving. We propose LIDAR-based 3D object detection based on foreground segmentation using a fully sparse convolutional network (FS23D). We design a sparse convolutional backbone network and a sparse convolutional detection head to efficiently use the computing and memory resources and accelerate the inference. Instead of using the anchor-based method, we convert the detection problem into a foreground segmentation problem on a bird’s-eye view. The sparse convolutional detection head predicts the objectness and bounding box on each active point on the sparse feature map. We design a new oriented bounding box coding method and corresponding loss functions. We predict the endpoints of two mutually perpendicular lines that pass through the foreground active points and indirectly predict the objects’ oriented bounding box from these four endpoints. We use the indirectly calculated object center, size, and orientation as inputs of loss functions in the training step. Experiments on the KITTI dataset show that the sparse backbone network we designed is 2.2 times faster and 18.4 times fewer FLOPs than the dense backbone network. The average improvement of the loss functions based on the bounding box code is 1.1% and 0.8% on the BEV and 3D detection, respectively, compared to no addition of these losses. Moreover, FS23D outperforms the state-of-the-art LIDAR-based method in speed and precision for both cars and cyclists.
On the potential of parallel multilevel Current Source Inverter using SiC devices for renewable applications

Nowadays, renewable energy conversion systems are mainly realized through a double-stage topology, which often combines a Boost chopper (DC-DC) and a three-phase Voltage Source Inverter (DC-AC). Although widely employed, this configuration presents some major drawbacks. New wide-bandgap (WBG) semi-conductors as SiC devices have led to consider alternative topologies, as the Current Source Inverter (CSI). When implementing WBG devices, the high level of conduction losses in the CSI are vastly improved. Moreover, an interleaved CSI is an extra solution to reduce the constraints on passives and semiconductor components, in addition to providing modularity. The aim of this paper is to analyze a parallel multilevel CSI using SiC devices for renewable energy applications. A dedicated tool to evaluate semiconductor losses is developed with this purpose.
Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-cloud Data

Perception systems for autonomous vehicles are reliant on a comprehensive sensor suite to identify objects in the environment. While object recognition systems in the LiDAR and camera modalities are reaching maturity, recognition models on sparse radar point measurements have remained an open research challenge. An object recognition model is here presented which imposes a graph structure on the radar point-cloud by connecting spatially proximal points and extracts local patterns by performing convolutional operations across the graph’s edges. The model’s performance is evaluated by the nuScenes benchmark and is the first radar object recognition model evaluated on the dataset. The results show that end-to-end deep learning solutions for object recognition in the radar domain are viable but currently not competitive with solutions based on LiDAR data.
A Robust Passive Target Localization for Substation Inspection of UAV in a GPS-denied Environment

This paper addresses a conceptual study on a precise passive target localization approach for substation facilities inspection using unmanned aerial vehicle (UAV) under GPS-denied environments. First, a cruciform receiver array that provides the range difference (RD) measurements between a facility (receiver array) and a UAV (transmitter) is devised. Then, to effectively deal with the real-time and accurate localization performance issue, our RD-based target localization problem is reformulated from the viewpoint of the linear least-squares estimation theory, and constrained robust weighted least-squares (C-RWLS) estimation scheme is applied. The proposed method shows excellent location estimates with the help of hard constraints on the geometric relation to the transceivers. Also, it is suitable for real-time implementation due to its linear structure. The reliability of the proposed estimation method is demonstrated through simulations from various points of the UAV.
Exploring Educational Practices in Emergency Remote Teaching. The Role of Educational Neuroscience

The COVID-19 pandemic led to an abrupt shift from conventional in-classroom teaching to an online form of synchronous distance education. In this context, educators faced the challenge to modify their teaching (planning, design and implementation) in order to answer the need for Emergency Remote Teaching (ERT), which arose during the past months. However, were they ready – or appropriately trained – to teach online at such short notice? What is more, did they manage to integrate the principles of online learning into their teaching practices according to the results of research from the field of educational neuroscience? In order to answer these questions, a qualitative small-scale research was conducted, using interviews with eight educators from private secondary schools in Greece. The research findings lead to the conclusion that the educators lack the theoretical knowledge of relevant learning theories for distance learning and, at the same time, seem to ignore the results of research in educational neuroscience, which clearly suggest the need to integrate multiple representational models into the learning process to activate different areas of the trainee’s brain to facilitate the learning of the latter. Moreover, important factors for effective learning, such as collaboration, interaction and time, are neglected during their teaching planning and design. Hence, it is proposed that educators should be trained according to the Technological Pedagogical and Content Knowledge (TPACK) framework in the principles of distance learning and educational neuroscience, in order to be more effective in the planning, design, and implementation of teaching.
Teaching the Digital Transformation of Business Processes: Design of a Simulation Game of Business Process Change

The impact of digital transformation (DT) on the business processes within the context of education is an issue that should be addressed when preparing the future workforce. It is necessary to emphasize the effects of digital technologies on business processes so that students can come prepared for their future working places. Simulation games are proven to be a valid method of teaching these concepts. On the other hand, topics like sustainability or environmental friendliness are also gaining more attention during last years. These factors are even considered among the DT drivers. However, the existing games, aimed at teaching DT, often bypass these concepts with their attention. Our paper aims at closing this gap by extending a game that simulates the manufacturing process of a bike producer, undergoing servitization, by integrating, sustainability, regionality, and ecological factor into its simulation model. By taking the role of the process manager, players guide their company through various process steps in several rounds and compete against other teams. At the end of each round teams are compared based on their financial success, with balancing out the ecological factor being an essential part of the strategy. Evaluation of our game shows that this approach contributes to a better understanding of both effective process management under DT as well as sustainability, regionality, and environmental friendliness concepts by the target audience.
Eliciting Educators’ Needs on the Design and Application of Augmented Reality Educational Board Games on Cultural Heritage: The case of CHARMap

The education of cultural heritage has been in the spotlight of the academia and the industry. In the scope of teaching about cultural heritage in Greece and China, an Augmented Reality board game, called CHARMap, was created. The game aims at providing at creating intrinsically motivating learning experiences for students and explore possibilities towards a positive impact on educators’ adoption of Augmented Reality technologies in learning contexts. This paper presents a pilot study during which primary and high school educators in Greece and China were presented with and explored the functionalities of this Augmented Reality board game. The aim of the study was to explore educators’ potential interest and needs regarding the use of an Augmented Reality educational game around Cultural Heritage in their classrooms. The study contributes to the broader academic research body by providing empirical evidence concerning the design, adoption and application of Augmented Reality educational games on cultural heritage in classrooms.
Error Correction Output Code-Based Radar Platform Motion Type Classification

In this paper, radar parameters measured by EW systems are aimed to determine the platform types with which they are related. First, an efficient preprocessing method is applied, which involves quantifying range values and grouping class values to improve classification performance. Then, Multi-task Learning (MTL) neural network, Support Vector Machine (SVM) and Error-Correcting Output Codes(ECOC) techniques are applied to the problem. It is observed that ECOC technique outperforms SVM and MTL technique. It is considered that the result of the classification process can be used in the selection of the type and parameters of the filter used for tracking.
Smart charging impact on electric vehicles in presence of photovoltaics

The emergence of electric vehicles (EVs) promises to be the turning point of sustainable energy and, in particular, renewable energy production in the world. EV Charging will generate substantial extra demand for electricity. Renewable energy, including solar and wind power, can be met in the grid technically and economically. Recent studies have shown that smart charging of EV can increase the synergy between photovoltaics (PV), electrical transmission and electricity usage, resulting in technological and economic advantages. Given the increasing emphasis on this area, this analysis summarizes an overview on smart charging studies taking into account PV power output and consumption of electricity.
Potential Hazards of Transient Overvoltages in an Industrial DC Grid and Basic Protective Measures

This article highlights the special electrical properties of an industrial DC grid and compares it with other typical DC grids. In this article, the extent of transient overvoltages is presented, which must be assumed to be caused by grid faults and switching operations. Basic measures for the protection of devices are listed and evaluated.The article clarifies that basic protection is necessary for the reliable operation of devices in industrial DC grids, but is easy to implement.
Low-Loss Low-Cost Substrate-Integrated Waveguide and Filter in GaAs IPD Technology for Terahertz Applications

A low-loss and low-cost terahertz (THz) substrate-integrated waveguide (SIW) and a SIW filter implemented in a commercially-available GaAs integrated-passive-devices (IPD) technology are proposed for THz applications. Ellipse vias penetrating through a 100- $\mu \text{m}$ thick GaAs substrate are employed to realize a low-loss SIW. The via’s orientation is designed as being transverse, instead of being longitudinal, to the propagation direction of the input wave, which can improve the insertion loss by 2.7 dB at 415 GHz due to lower signal leakage from the waveguide. The proposed SIW is able to provide simulated insertion loss of only 0.39 dB/mm, i.e., 0.14 dB/ $\lambda _{\mathrm {g}}$ , at 340 GHz. A new SIW filter structure using the ellipse vias is proposed which not only successfully realizes a low-loss fourth-order Chebyshev filter under hard design-rule-check (DRC) rules imposed by the IPD technology, but also can enhance out-of-band rejection by 10.5 dB at 390 GHz as compared with conventional waveguide filters. A slot-coupled coplanar waveguide (CPW) to SIW transition structure without any impedance tuning stub required is also proposed to measure the proposed SIW and SIW filter. The proposed transition structure can give simulated insertion loss of 0.7 dB at 340 GHz while keeping return loss better than 10 dB from 307 to 374 GHz. Eight samples are measured to demonstrate the robustness of the proposed designs against process variations. Experimental results show that the proposed transition structure with a 220- $\mu \text{m}$ long SIW and the SIW filter can provide measured insertion loss of 0.7 and 3.6 dB at 327.5 GHz, respectively. The reasons for the discrepancy between the simulation and measurement results are identified and discussed in detail. As compared with prior works, the proposed SIW and SIW filter exhibit lower loss, lower cost, higher repeatability, higher reliability, and mass-producible capability. To the best of the authors’ knowledge, this is the first demonstration of the THz SIW and THz SIW filter designs using a commercially-available and mass-producible IPD technology reported thus far.
Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.
A Big Data Analysis on Efficiency of Bangkok Taxi System

Taxi is one of the most popular urban public transportation systems. Many people rely on taxi service on a daily basis. However, the Bangkok taxi system may not be operating at its full potentials. Obviously, there are plenty of vacant taxis on the road searching for passengers while the passengers have to spend a lot of time waiting for taxis. This inefficiency wastes valuable resources such as time and fuel energy. Nonetheless, to our knowledge, the efficiency of the Bangkok taxi system has not been measured quantitatively. In this paper, we quantitatively analyze the performance of the Bangkok taxi system based on the big data collected from real taxi traces. Both the system-level efficiency and the agent-level efficiency are investigated.
Benefits of Digitalization for Business Processes in Semiconductor Manufacturing

The global semiconductor industry is increasingly transforming manufacturing to highly automated and digitized processes. Various novel technologies based on the Internet of Things (IoT) that allow competitive advantages in the European industry emerged thanks to the advancements of digitization and its possibilities supporting the development processes within the semiconductor industry. Digitization, virtualization, digital twins and simulation applications offer the opportunity to create a smart fabrication facility. In this paper, three use cases applying new digitization technologies at the semiconductor front-end facility of Infineon Technologies Dresden supporting the digital transformation in important business processes of the factory are presented. The wafer facilities are already highly automated with respect to the material flows using hundreds of robotics, so that digitization is the next consequent step to improve the performance of the business and speed up processes within the company, especially with the help of digital twins. Besides fabrication automation using only simple robotics for tool loading and unloading, digitization of repetitive, administrative processes implies very high potentials to enhance the competitiveness of a fabrication facility or even a whole fabrication network, not only technically but also in terms of human factors.
Detection and Classification of Incoming Ambulance Vehicle using Artificial Intelligence Technology

Traffic congestion increases due to a number of vehicles on the road. Drivers cannot see the arrival of emergency ambulance. Then, personal vehicles block emergency ambulance. As a result, the chance of patient’s dead increases due to such problems. Currently, personal vehicles equipped with Car DVR. This article introduces the implementation of AI for ambulance classification from features of ambulance (Text Ambulance, ambulance lights, Red Cross symbol, Star of life symbol) using Faster-RCNN by TensorFlow. Initially, various image pre-processing techniques are performed. Intersection over Union (IoU) are used for accuracy representation. The results of this research found that the accuracy of classification, star of life symbol give accuracy 98%, red cross symbol 99%, text Ambulance 95.6%, and Ambulance light 97.9%. Our results confirm the proposed system can classify and detection an incoming of Ambulance vehicles.
Impact of Gateway Placement and Energy Consumption for Data Processing on Lifetime of IoT Networks

The network lifetime has been considered as one of the key parameters in IoT networks. Energy consumption of sensor nodes is a major concern in maximizing the network lifetime where not only data transmission but also data processing consume energy. In this article, maximum lifetime routing (MLR) in an IoT network is formulated as a linear optimization problem that, in addition to energy dissipation of data transmission, includes energy consumption for data processing. Optimization results reveal that the placement of the gateway has a significant impact on the network lifetime. Compared to placing the gateway at the geographical center, the network lifetime increases by almost 36% if the gateway is in the optimal location. Numerical results of the network lifetime also quantify the impact of energy consumption for data processing. Compared with the MLR problem without considering data processing, the network lifetime can decrease as much as 40% when energy consumption for data transmission and data processing units are equal (Eelec = Epro). This work could be applied to facilitate the field of wireless network planning.
Using Fast Intersection to improve SCAN Algorithm

The structural graph clustering is considered in this paper. Given a graph G = (V,E) and parameters 0 < ϵ < 0 and µ ≥ 2, we want to efficiently assign vertices in V to clusters such that vertices from the same cluster are densely connected and vertices from different clusters are loosely connected. SCAN algorithm is a standard approach to solve this problem. However, SCAN has an expensive computation cost. In this paper, we propose an improved version of SCAN and reduce computation time in the similarity calculation step. We use simple calculation for edges that connect to a leaf node. For the remaining edges, we adopt the fast intersection algorithm of Baeza-Yates and Salinger. We validate our algorithm with real network datasets. Our algorithm outperforms SCAN for any parameters and pSCAN for small µ.
On the Practicality of Local Ranking-Based Cancelable Iris Recognition

Practical cancelable biometrics (CB) schemes should satisfy the requirements of revocability, non-invertibility, and non-linkability without deteriorating the matching accuracy of the underlying biometric recognition system. In order to bridge the gap between theory and practice, it is important to prove that new CB schemes can achieve a balance between the conflicting goals of security and matching accuracy. This paper investigates the security and accuracy trade-off of a recently proposed local ranking-based cancelable biometrics (LRCB) scheme for protecting iris-codes. First, the irreversibility of the LRCB is revisited and a new attack for reversing the LRCB transform is proposed. The proposed attack utilizes the distribution of order statistics for discrete random variables to reverse the protected rank values and obtain a close approximation of the original iris template. This ranking-inversion attack is then utilized to realize authentication, record multiplicity, and correlation attacks against the LRCB transform. Our theoretical analysis shows that the proposed reversibility attack can recover more than 95% of the original iris-code bits and the proposed correlation attack can correctly correlate two templates 100% of the time for the parameter setting that retain the recognition accuracy of the underlying iris recognition system. The validity of the proposed attacks is verified using the same iris dataset adopted by the authors of the LRCB scheme. Experimental results support our theoretical findings and demonstrate that the security properties of irreversibility and non-linkability can only be fulfilled at the expense of significant and impractical degradation of the matching accuracy.