Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Optimal Settings for Distance Calculation with a Low Frequency Low-Power RF system

This paper focuses on the application of low power and low frequency Received Signal Strength Indication (RSSI) based localization system. The transceiver presented will wake-up by a modulated magnetic field that is emitted from the transmitter module. This enables the receiver to have low power consumption when not in the range of the transmitter. The goal is to provide an empirical relationship between the RSSI and the relative distance between two modules. To determine the optimal settings required to reliably and consistently obtain the RSSI.
Towards Knowledge Enhanced Language Model for Machine Reading Comprehension

Machine reading comprehension is a crucial and challenging task in natural language processing (NLP). Recently, knowledge graph (KG) embedding has gained massive attention as it can effectively provide side information for downstream tasks. However, most previous knowledge-based models do not take into account the structural characteristics of the triples in KGs, and only convert them into vector representations for direct accumulation, leading to deficiencies in knowledge extraction and knowledge fusion. In order to alleviate this problem, we propose a novel deep model KCF-NET, which incorporates knowledge graph representations with context as the basis for predicting answers by leveraging capsule network to encode the intrinsic spatial relationship in triples of KG. In KCF-NET, we fine-tune BERT, a highly performance contextual language representation model, to capture complex linguistic phenomena. Besides, a novel fusion structure based on multi-head attention mechanism is designed to balance the weight of knowledge and context. To evaluate the knowledge expression and reading comprehension ability of our model, we conducted extensive experiments on multiple public datasets such as WN11, FB13, SemEval-2010 Task 8 and SQuAD. Experimental results show that KCF-NET achieves state-of-the-art results in both link prediction and MRC tasks with negligible parameter increase compared to BERT-Base, and gets competitive results in triple classification task with significantly reduced model size.
The Study of Defects Auto-Classification System in Semiconductor Manufacturing

In semiconductor manufacturing, defects detecting and analyzing are one of the key contributors for process yield improvement. A system is proposed to classify the defects automatically, which is more efficient than traditional manual work. It becomes possible to control the process promptly. The proposed system has three main parts, the defect image pre-process module, the defect classification module and the network results post-process module. The convolutional neural network (CNN) is used as the base algorithm for defect classification. And the post-process part is developed to refine the confusing images results. The dataset in this work are all collected from the 12-inch process line and the results show that the system proposed is a good candidate for saving massive human work.
Reduction of Current and Voltage Distortions under Overmodulation for Matrix Converters

This paper presents a new method to reduce current and voltage distortions for matrix converters in the overmodulation range. Analytical overmodulation conditions for both the input currents and the output voltages are derived. A carrier-based overmodulation method is then proposed to reduce the distortions by appropriately adjusting the reference voltages. Simulation results confirm that the distortions of input currents and output voltages can be improved by the proposed method.
Robust Sensorless Speed Control of Synchronous Reluctance Motor using Modified Moving Horizon Estimation Method

This paper illustrates the sensorless speed control of Synchronous reluctance motor (SynRM) using the Moving Horizon Estimation (MHE) based Kalman Filter (KF) method under sensor and process noise. The optimal estimation of the rotor speed and position is attained by minimizing the error between measured and estimated stator currents as a modified cost function, within a finite time horizon. The modified cost function includes the updation of kalman gain constant to enrich the estimation of speed variable even under noisy conditions. The proposed MHE based sensorless method is validated by performing speed control using cascaded PI based speed and current controllers for SynRM, in extensive fixed-step MATLAB simulation environment
Localization of Internet of Things Network via Deep Neural Network Based Matrix Completion

In this paper, we propose a technique to acquire the sensor map of Internet of Things (IoT) network. Our approach consists of two main steps to reconstruct the Euclidean distance matrix. First, we recast Euclidean distance matrix completion problem into the alternating minimization problem. We next employ a cascade of multiple deep neural networks to recover the location map of sensors (and the original distance matrix) from the noisy observed matrix. From the numerical experiments, we demonstrate that the proposed method can achieve an accurate reconstruction performance of the distance matrix with much smaller measurement required by conventional approaches and also outperforms state-of-the-art matrix completion algorithms both in noisy and noiseless scenarios.
Transfer Learning Based Method for COVID-19 Detection From Chest X-ray Images

Radiology examination of chest radiography or chest X-ray (CXR), is currently performed manually by radiologists. With the onset of the COVID-19 pandemic, there is now a need to automate this process which is currently one of the key methods of primary detection of the SARS-Cov-2 virus. This will lead to shorter diagnosis time and less human error. In this study, we try to perform three-class image classification on a dataset of chest X-rays of confirmed COVID-19 patients(408 images), confirmed pneumonia patients(4273 images), and chest X-rays of healthy people(1590 images). In total the dataset consists of 6271 people. We aim to use a Convolutional Neural Network(CNN) and transfer learning to perform this image classification task. Our model is based on a pre-trained InceptionV3 network with weights trained on the ImageNet dataset. We fine-tune the layers of the Inception network to train it to our specific task. We try fine-tuning the network to different extents by freezing a different number of layers and then comparing accuracy for each variation of the network. To evaluate the performance of our network we use several metrics which include Classification accuracy, Precision, Sensitivity, and Specificity. Our proposed method achieves an accuracy of 96.33% on a 3-class classification task (Normal, COVID-19, Pneumonia) and an accuracy of 99.39% on a 2-class (COVID and Non-COVID) classification task.
GALB: Load Balancing Algorithm for CP-ABE Encryption Tasks in E-Health Environment

Security of personal data in the e-healthcare has always been challenging issue. The embedded and wearable devices used to collect these personal and critical data of the patients and users are sensitive in nature. Attribute-Based Encryption is believed to provide access control along with data security for distributed data among multiple parties. These resources limited devices do have the capabilities to secure the data while sending to the cloud but instead it increases the overhead and latency of running the encryption algorithm. On the top of if confidentiality is required, which will add more latency. In order to reduce latency and overhead, we propose a new load balancing algorithm that will distribute the data to nearby devices with available resources to encrypt the data and send it to the cloud. In this article, we are proposing a load balancing algorithm for EHealth system called (GALB). Our algorithm is based on Genetic Algorithm (GA). Our algorithm (GALB) distribute the tasks that received to the main gateway between the devices on E-health environment. The distribution strategy is based on the available resources in the devices, the distance between the gateway and the those devices, and the complexity of the task (size) and CP-ABE encryption policy length. In order to evaluate our algorithm performance, we compare the near optimal solution proposed by GALB with the optimal solution proposed by LP.
Design and Implementation of Asynchronous Serial Digital Data Transmission Decoder

This study developed an asynchronous serial digital data transmission decoder and used it as a lighting control system. People began using lighting equipment in the field of art, and later unified the specifications of lighting equipment by formulating the DMX512 transmission protocol. This decoder circuit and made them into chips using a HDL. The chips were subsequently used to read DMX512 data and then to store the data into memory. Users could use switches to select the data in the memory and output the data as 8-bit or serial pulse-width modulation data. Finally, the decoder circuits underwent IP modularization.
Exploring the Impact of COVID-19 on Aviation Industry: A Text Mining Approach

Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic.
Temporal Memory Network Towards Real-Time Video Understanding

Action recognition is the basic task for video understanding. Although the action recognition has achieved impressive performance in the static image-based task (e.g. Stanford40) with deep learning, real-time video-based action recognition is still a challenging task due to video’s high complexity and computation cost. Motivated by human’s recognition ability with only a short glance, we propose the fast light-weighted Temporal Memory Network (TMNet) to achieve real-time video action recognition. The TMNet has a self-supervised structure for exploring both spatial and temporal information with a single video frame. TMNet has three main parts, the base backbone, the regression branch, and the classification branch. Specifically, the base backbone network is a shallow 2D CNN network to obtain the video’s initial feature sequences. The classification branch is based on existing successful video recognition models(e.g. TSN, I3D). To make the TMNet learn the spatial-temporal information at a lower cost, we add a self-supervised regression branch. This branch is based on light-weighted 2D CNN and only uses one frame as input. In the training stage, the input of TMNet is a video sequence, the classification branch combined with the base backbone is responsible for learning the video sequence’s spatial-temporal feature. Meanwhile, the self-supervised regression branch aims to learn the same spatial-temporal feature under the supervision of the classification branch’s output. And the regression branch’s input is a single-frame feature sampled from the encoded video sequence. In this way, the regression branch is forced to learn temporal information of adjacent frames with one frame. Therefore, TMNet only needs one frame to predict each video’s spatial-temporal information in the inference stage. Finally, TMNet can achieve real-time action recognition and better accuracy by extracting temporal information from a static image. Abundant ablation experiments demonstrate TMNet has a good trade-off between accuracy and speed.
A Study of a Bus Stop that Displays the Current Location of the Bus to Increase User Convenience

The community bus is indispensable for the local residents. Due to weather and traffic congestion, community buses may not operate on time. The local residents will have to wait for the bus in fear that they will not know when it will come. Therefore, we created a bus stop that displays the current position of the bus using a tablet terminal in order to eliminate the burden [1, 2, 3]. However, tablet terminal type bus stops were difficult for elderly people who were not familiar with smartphones and the Internet environment. In addition, in the case of bus stops without a power supply, the tablet terminal type bus stop was not suitable for long-term operation in terms of power consumption and cost. In this paper, we have created a bus stop that makes it easy for the elderly to understand by using LEDs to indicate the location of the bus. We also investigated the power consumption, operating cost, and usability of the LED type bus stops. As a result, we confirmed that it was improved from viewpoints that power, cost and user convenience.
ExKaldi: A Python-based Extension Tool of Kaldi

We present ExKaldi, an automatic speech recognition (ASR) toolkit, which is implemented based on the Kaldi toolkit and Python language. While similar Kaldi wrappers are available, a key feature of ExKaldi is an integrated strategy to build ASR systems, including processing feature and alignment, training an acoustic model, training, querying N-grams language model, decoding and scoring. Primarily, ExKaldi builds a bridge between Kaldi and deep learning frameworks to help users customize a hybrid hidden Markov model–deep neural network-based ASR system. We performed benchmark experiments on the TIMIT corpus and revealed that ExKaldi could build a system from scratch with Python and achieved reasonable recognition accuracy. The toolkit is open-source and released under the Apache license.
Adaptive Queue Management in Embedded Edge Devices for Object Detection with Low Latency

With the advancement in artificial intelligence (AI) and microchips, we have been witnessing growing use of AI in object detection from video frames captured from Internet of thing (IoT) devices (e.g., IP cameras). From the video capturing sources to Cloud, generally, the packet forwarding delay could reach up to several hundreds of milliseconds, thereby making Cloud based objection detection as an impractical solution when the latency requirement for object detection from video frames is stringent. Embedded edge devices which are situated at the network access segment are considered to be a promising solution as they perform object detection locally and impose a negligible amount of packet forwarding delay. Nevertheless, due to lack of computation resources on such embedded edge devices, processing delay is likely to be increased as the frame rates of incoming images increase. To solve this issue, we design an adaptive queue management-based object detecting (QMOD) scheme for achieving low latency AI services with limited computation resources. We introduce a target delay requirement of AI services, and map such requirement when deciding optimal queue size so as to contain frames prior to object detection procedures. The optimal queue size, which represents a desired value at the embedded devices, is dynamically determined by the target delay requirement and weighted-average of processing time. Thus, maximum queue size is adaptively controlled, and some redundant frames are automatically dropped due to overflow of the queue. We demonstrate through experiments that the proposed QMOD scheme achieves 80%-90% reduction of latency for queuing and object detection in the commercial edge devices such as NVIDIA Xavier, Jetson TX2, and Nano, compared to the various benchmark schemes (i.e., static queue size).
Joint Availability- and Traffic-aware Placement of Parallelized Service Chain in NFV-enabled Data Center

Network Function Virtualization (NFV) brings flexible provisioning and great convenience for enterprises outsource their network functions to the Data Center Networks (DCNs). Network service in NFV is deployed as a service chain, also known as Service Function Chain (SFC), which includes an ordered set of Virtual Network Functions (VNFs). However, there might be some small size flows being queued behind the large flows in one SFC, resulting in network congestion and high SFC delay. In this paper, we focus on the parallelized SFC placement problem in DCN considering availability guarantee and resource optimization. Firstly, we define the parallelized SFC and propose a multi-flow backup model. Then, we design three placement strategies and a Hybrid Placement Algorithm (HPA) aiming at mapping SFCs to DCN. Compared with the existing approaches, our proposed solutions can reduce SFC delay and optimize link consumption while guaranteeing availability.
Optimizing Uploading Time and Energy Consumption in IoT 5G Networks

Nowadays, several research efforts have been done to investigate different approaches of enhancing performance of the uplink relay-based cellular communication framework, where data transfer is uploaded through client-to-client, all the way up to the cellular base-station. Those clients could be hand-sets, sensors, or devices working in the Internet of Things (IoT) communication environment. Recently in 5G communication networks, multi-hop uplinks and device-to-device (D2D) communications are of major research interest due to its nature of massive data exchange and delivery delay intolerance,. In D2D, data loads are uploaded towards the Base Station of the Cellular Network gNodeB (gNB) using different algorithms with different (and considerable amount of) computations' complexity. The complexity of these computations are a heavy burden over the limited capabilities of the backhaul and small User Equipment's (UEs), sensors, etc. Reducing the computation complexity of these algorithms will directly enhance the network performance and its energy efficiency overall. This work attempted to use Game theory techniques (mainly, graph-coalition-formation game) where it obtained enhanced results in 5G-standards D2D uplinks environment, this is due to employing relatively lower complexity approach to this problem. Reduction of computation complexity is introduced here through applying a preparatory stage of validating the active links of coalition members first via a proposed approach of finding all the possible links between them first, then, omitting the non-candidate members of the computations steps. Thus, applying graph coalition formation game calculations - as merge and split algorithm - consequently reduced the order of calculations' complexity. Results obtained outperforms the system performance in the network behavior, uploading time, and energy efficiency in comparison with previously published work.
One-Step Synthesis and Assembly of Copper Sulfide Nanoparticles to Nanowires, Nanotubes, and Nanovesicles by a Simple Organic Amine-Assisted Hydrothermal Process

A simple organic amine-assisted hydrothermal process has been proposed to simultaneously realize the synthesis and assembly of copper sulfide nanoparticles at low temperature (90 ∼ 110 °C). Nanowire-like aggregates (average diameter of 120 nm and length of 2 μm) assembled by digenite phase copper sulfide nanoparticles have been synthesized with triethylenediamine (TEDA) as a linking agent. Nanotube-like aggregates (diameter ranging from 40 to 200 nm and length from 400 nm to 4 μm) and nanovesicle-like aggregates (size ranging from 50 to 180 nm), assembled by djurleite phase copper sulfide nanoparticles, have also been obtained by substituting tramethylethylenediamine (TMEA) and di-n-butylamine (DBA) for TEDA, respectively.
Angle Estimation Network Using Polar Transformation for Object Classification

Even if a trained model for object detection and classification is available, it may not be able to classify objects correctly unless the training data takes into account a variety of factors such as rotation. We propose a model that uses a polar transform and a convolutional neural network for rotated objects so that the detected objects become suitable inputs for classification. The model is validated by the rotated MNIST dataset and shown to predict the angles suitable for classification.
Design of an IoT interface for a solar energy system with vehicle to home option for Newfoundland conditions

This paper discusses the design of solar energy system in offline mode along with the implementation of Vehicle to Home (V2H) to meet the energy demand of a smart house for Newfoundland condition. A site was selected (13 Polina road) located in St. John's, Newfoundland, Canada. For the chosen site, an optimized system was designed to meet the energy demand of the house using BE opt and Homer software. Further, based on Nissan Leaf (Electric Vehicle), the concept of V2H is implemented with the help of smart current sensors installed in the house that also helps in transmitting the sensor's data to cloud with the help of IoT. Based on the information generated, the system operates either in V2H more or in PV power mode.
Real-Time Monitoring and Early Warning System for Landslide Preventing in Myanmar

Landslide can be triggered by both Natural and Man-induced changes in the environment. There are several natural factors that can cause slope failure such as movement of mountains, weaknesses in the structure of the rock or soil, heavy raining, and erosion by rivers. Man-induced may results from changes in slope caused by terracing for agriculture, cutting the tree on the mountains, mining operation, and so on. Like other natural disasters, landslide can destroy the natural and environmental events in a very short of time. Loss of Human life and destruction of environment are caused by landslide. To minimize loses, there is a need for monitoring and early warning system before occurrence of landslide. Therefore, the system implemented the landslide monitoring system based on wireless sensor networks (WSN). Respective sensors are connected to wireless nodes. The system received sensor data from respective sensors and then compare with threshold value. The system shows the appropriate signal at relay station and send the SMS to smart phone that near the landslide areas.
Design of Low-Complexity YOLOv3-Based Deep-Learning Networks with Joint Iris and Sclera Messages for Biometric Recognition Application

In this study, the effective low-complexity YOLOv3_tiny based deep-learning inference networks are studied for biometric authentication. First, the eye images are labelled by jointly partial iris and sclera zones, and then the proposed YOLOv3_tiny based classifier infers the person’s identity efficiently. By the UBIRIS database, the applied YOLOv3_tiny based inference model achieves the mean average precision (mAP) up to 99.92% with only using one anchor box. Compared with the previous ocular biometric studies with iris or sclera information, the proposed low-complexity design provides better performance of accuracy and does not need the iris and sclera segmentation process.