Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Large Area Temperature Control System for a Low-cost PCR Chip

The complicated structure, the high cost of fabrication process and chip materials make polymerase chain reaction (PCR) chips difficult to realize mass production and promotion. In addition, the detection of multiple samples using PCR chips is time-consuming since most of the chips can only proceed one reaction at a time. Here, a high throughput low-cost PCR chip with simple fabrication process and a large area temperature control system were presented. The PCR chip and the heater chip were fabricated by micro-electro-mechanical system (MEMS) technology. The temperature distribution of different shapes of the heaters were simulated by finite element method. A temperature control system based on digital signal processor (DSP) was designed to provide accurate temperature for the amplification reaction. The temperature of the liquid in PCR chip was tested by a precision temperature scanner with four wire thin film sensors. The temperature distribution of the PCR chip was verified by an infrared thermal imaging camera. The experiment showed the system has a high heating rate and good temperature uniformity. It is anticipated that the PCR chip and the system are suitable for mass production and multiple samples detecting.
Learning Deep Features For Efficient Face Detection

Over the last decade, large companies have heavily invested in the collection, storage and the extraction of structured and unstructured data related to their clients. This trend has lead to a rise of a large data volumes considered as mine of knowledge. With the appearance of more powerful computers and the application of the graphics processing unit (GPU) computing, Deep learning has appeared as a practical solution for large data sets processing and offers an alternative strategy to learning characteristics.In this paper, we consider learning a collection of high-level facial representation characteristics, referred to as Deep hidden face characteristics, through deep learning. We present a deep learning model (DeepDetect) trained on the Annotated Facial Landmarks in the Wild(AFLW) dataset witch offers large visual variations that allows learning very discriminative face representation. We compare the performance of our DeepDetectmodel with different variants of Boosting LBPfeatures, and we show that DeepDetectoutperforms the hand-crafted features based approach. A detection accuracy of 98.17% is achieved on the fusion of Feretdata-set and negative images extracted from the wild. The depiction of an picture is no longer a heavy hand-engineered operation with our suggested strategy, but a learning operation that leads to an effective solution to accurately differentiate faces from backgrounds.
Global Interconnect Optimization

We propose a new comprehensive solution to global interconnect optimization. Traditional buffering algorithms mostly insert repeaters on a net-by-net basis based on slacks and possibly guided by global wires. We show how to integrate routing congestion, placement congestion, global timing constraints, power consumption, and additional constraints into a single resource sharing formulation. The core of our algorithm is a new buffered routing subroutine. Given a net and Lagrangean resource prices for routing, timing, placement, and power, it computes a buffered global route. The resource sharing framework provides a special multiplicative price update for fast convergence. Our algorithm is fast enough for practical instances. We demonstrate experimentally on 7nm microprocessor units that it significantly improves timing while reducing netlength and power consumption in an industrial design flow.
Network Disruption Prediction Using Naïve Bayes Classifier

The most crucial challenge of internet service providers is to assure the availability and reliability of their services to their customers. The companies should prevent the customer's complaint by recognizing a potential disruption for the customers, especially in the category ‘under spec' condition (potentially impaired service). This study proposed and implemented a model using the Naïve Bayes classifier to classify and detect the potential disruption of network services to prevent customer's complaints about their service. The criteria for this model prediction are revenue number of each customer (REVENUE), recurrent disruption value of ODP (N_Q), attenuation value in ODP (OLT), and attenuation value in customer (ONU). The data classified into three classes or conditions, namely GREEN representing no network disruption, YELLOW is representing low-level disruption, and RED representing high-level disruption, which needs more attention to follow up. The result obtained 91.89% accuracy of the model performance using WEKA Tool.
A Book Without Pages: Developing an Augmented Reality Storyteller Based on Open Web Data

In this study, we describe research carried out to develop a context aware augmented reality storyteller. The system allows users to listen to short stories from books and novels as they move around in the physical world based on the spatial characteristics of their physical location. Large-Scale Open web data from sources such as Open Street Map, Google Street View and Project Gutenberg were collected and used to implement our system. Word embedding based modeling and Natural Language Processing techniques were used to match the stories with the physical locations. As an example, we show how our proposed approach could be implemented to map stories from Aesop's Fables to different locations in two cities, Kyoto and San Francisco.
Ultra Low Power Self- Resonating Boost Converter

In this paper, the effort has been made to design an ultra-low power self-resonating boost converter. Proper choice of capacitors, inductors and resistors has made it possible to design a boost converter with an efficiency percentage of 37%. In the proposed circuit, the input inductance of 10kH is fixed while, to get an output voltage between 1V – 3V the input voltage is varied. The design draft of the self resonating boost circuit and simulation is done on the liner technology software named LTspice. A basic introduction of the software and process to add the JFET into the library is discussed. The reliability of the circuit is tested by plotting the waveforms at different input voltages from 1mV to 750mV. Moreover, an exceptional case to obtain an output voltage as high as 14V is also discussed.
An Image-Based Identification System of Bubalus Bubalis Using Image Processing Feature Extraction by Linear Discriminant Analysis

Animal identification allows producers to keep records on the most important information such as breed, date of birth, and gender. Plastic tags on water buffaloes (Bubalus Bubalis) are prone to damage and in order to be read, they should be restrained. The main objective of the study is to create a portable device that can identify the buffalo by taking its facial image. This study shows that with the use of Linear Discrimant Analysis (LDA), the water buffaloes can be identified by just taking a photo of its face for training images and testing images. In comparison with various algorithms used for the identification of water buffaloes, Local Binary Patterns Histograms (LBPH) shows more accurate results compared to other algorithms such as Principal Component Analysis (PCA), Linear Discriminant Analysis with Support Vector Machine (LDA+SVM) and LDA alone. The output of the simulated training images and testing images show favourable results indicating effectiveness of the study.
Railway Track Fasteners Fault Detection using Deep Learning

The railway track fastener is the most important component of the rail fastening system. It can fasten the railway track onto the crosstie, and has great influence on train's stability and safety. In the early days, track patrol and maintenance were carried out on foot by track workers, and gradually adjusted to the use of track patrol vehicles, but still completely rely on manual visual inspection. However, the results of visual inspection may be limited by the vehicle ’s speed and inspection angle, or even long -term visual inspection may cause fatigue, and the problem of omission. In this study, we not only collect the related research results of track inspection, but also set up the image collecting device for track fastener including image recording equipment and lighting equipment. Yolo v3 is deployed and trained as the deep learning model, then the accuracy and recall rates of damaged fasteners are verified from the test dataset. In the experiments, we used the GoPro motion camera to capture images and record a total of 20 km track fastener images. The precision rate and recall rate of fasteners detection including defects were 89% and 95%, respectively, which can meet the demand of efficient inspection for railway track workers.
Materia Medica in Chinese Religious Sources: Towards a Critical Digital Philology for Modelling Knowledge Distribution in Early Chinese Texts

The circulation of medical knowledge extends far beyond recognized "medical" fields in all cultures, but can be difficult to trace outside of canonical sources. This paper models the distribution of medical knowledge in Buddhist, Daoist and medical source from the early medieval period (Six Dynasties--up to the year 589) by charting the distribution of materia medica terminology. Surviving Shangqing Daoist records appear to contain the most materia medica vocabulary, and indicate the relatively higher use of drugs within the sect. The paper argues that such search results and visualisations constitute "qualified historical hypotheses" about the distribution of medical knowledge across religious sects in the period, and the relative saturation of medical knowledge in each genre of writing and sect. It details how search results for the terms are grounded in philologically rich meta-data about the textual corpora. The paper describes how, using post-search classification, a key function of DocuSky's TermStats Tools, how search results were produced using the enriched metadata, and then further refined to generate a readable graph and reliable statistics. These refinements are the "qualifications" of the hypothesis, and the analysis of how they impact the search results constitute what the paper refers to as a "critical digital philology. " The digital visualisations of DocuSky outputs allows researchers to model the distribution of knowledge and generate new research questions. Greater accuracy can be produced through semi-automatic markup and synonymy tables, i.e. name authority databases. The authors describe their current processes for producing a synonymy for Chinese materia medica, to account for the use of alternate names for specific materia medica. The relevant open access databases, metadata, term lists and name authority tables for the study of medicine across Daoist Buddhist and medical sources that the authors have published are all hyper-linked from the paper. The hypotheses of the paper can thus be reproduced, and the paper constitutes a full working model and datasets for the development of similar projects.
A Similarity-based and Model-based Fusion Prognostics Framework for Remaining Useful Life Prediction

In this work, a hybrid prognostic framework which interfaces a model-based prognostic method, namely particle filter, with a similarity-based prognostic method is proposed. The proposed framework consists of automatic determination of predication start point, sensor fusion, and prognostics steps that lead to accurate remaining useful life (RUL) estimations. This approach first applies the canonical variate analysis (CVA) approach for determining the prediction start time and constructing the prognostic health indicators (HIs). The similarity-based method is then employed together with the model-based particle filter (PF) algorithm to improve the predictive performance in terms of reducing the uncertainty of RUL and improving the prediction accuracy. The proposed framework can automatically construct HIs that are suitable for RUL prediction and offer higher prediction accuracy and lower uncertainty boundaries than traditional model-based PF methods. Our proposed approach is successfully applied on aircraft turbofan engines RUL prediction.
XR for Augmented Utilitarianism

Steady progresses in the AI field create enriching possibilities for society while simultaneously posing new complex challenges of ethical, legal and safety-relevant nature. In order to achieve an efficient human-centered governance of artificial intelligent systems, it has been proposed to harness augmented utilitarianism (AU), a novel non-normative ethical framework grounded in science which can be assisted e.g. by Extended Reality (XR) technologies. While AU provides a scaffold to encode human ethical and legal conceptions in a machine-readable form, the filling in of these conceptions requires a transdisciplinary amalgamation of scientific insights and preconditions from manifold research areas. In this short paper, we present a compact review on how XR technologies could leverage the underlying transdisciplinary AI governance approach utilizing the AU framework. Towards that end, we outline pertinent needs for XR in two hereto related contexts: as experiential testbed for AU-relevant moral psychology studies and as proactive AI Safety measure and enhancing policy-by-simulation method preceding the deployment of AU-based ethical goal functions.
LoRa ToA-Based Localization Using Fingerprint Method

Localization is one of the essential elements in Internet of Things (IoT) applications for location-based service. In particular, LoRa has suitable features for localization such as low power consumption, long transmission range, and low deployment cost. However, the existing LoRa localization methods have limitations in terms of feasible localization area and accuracy of the localization in noisy environments. In the area where there are less than three gateways, it is difficult to estimate a specific location using the existing algorithms. Also, the existing LoRa localization algorithms result in large estimation error in the noisy environment such as urban. In this paper, we propose a LoRa ToA-based localization using fingerprint map to handle the issues. Through the proposed algorithm, we can estimate location even in the area where there are less than three gateways. In addition, to efficiently calculate distances using ToA, we suggest a LoRa time synchronization protocol (LTSP). By utilizing fingerprint map, the localization error caused by noise and multipath can be significantly reduced. The simulation results show that, compared with other schemes, the proposed algorithm significantly expands feasible localization area while improving the localization accuracy.
DNS Network Security Analysis Based on Campus Environment

In this paper, we analyze the log data of the campus network DNS64, and build a new big data analysis platform to process the DNS64 streaming data in real time. Focus on mining information related to network security threats in DNS64 data. Through our analysis, we can organically correlate domain name traffic, IP address access, server load and other information. Real-time detection of DDOS network security attacks. Also the experimental verification, we found the abnormal behavior of individual IP, and the experimental results were good.
A Bidirectional Wireless Power Transfer for Electric Vehicle Charging in V2G System

Wireless Power Transfer is a innovative technology in which power is transferred without physical contact. As technical knowledge is proceeding, the most of wired technology is also converting into wireless technology through different techniques. Electric Vehicles and plug-in hybrids may be fresh and feasible but it does not enough if it is forgetting to plug in power source the night before. Electric Vehicle will automatically charge when it will park in the special parking space where transmitter circuit has already been developed, when an electric vehicle park on that place, charging will start automatically. A preceding review of few methods for wireless charging discover that Inductively Coupled Power Transfer System (ICPT) is a advantageous method for wireless charging of EVs(Electric Vehicles). This paper presents a Bidirectional IPT(Inductively Coupled Power Transfer) system which is appropriate for Vehicle to Grid (V2G) systems. For EV charging A bidirectional WPT is a stable dynamic and effective system. Wireless power techniques falls into two categories, non-radiative and radiative. This research follows the non-radiative field using magnetic inductive coupling between coils of wire. In this project mutual inductance technique is used for wireless charging for transferring voltage (12 V) for charging of that battery at a considerable distance of 30 cm between two coils. This paper will also enhance the feasibility, reliability and efficiency of the system.
Improvement of ordered statistics decoding for low-rate BCH codes

We propose an appropriate decoding algorithm based on ordered statistics for ultra-reliable low latency communication (URLCC) use cases, which typically require low code rates and short block length. This paper investigates tradeoff between performance and complexity of ordered statistics decoding (OSD) algorithms for BCH codes with low code rates over AWGN channel. The block error rate (BLER) performance of the proposed algorithm approaches that of the original OSD resulting in lower complexity. In addition, it outperforms the segmentation-based OSD at a cost of slightly higher complexity.
Design of Air Quality Monitoring System Based on NB-IoT

A air quality monitoring system based on the NB-IoT was designed for wireless monitoring of special field environment. The STM32FI03RCT6 microprocessor unit was the core processor and the data of gas pollutants was collected by electrochemical gas sensor through serial port. The switching mechanism of wireless network and the data storage mechanism of disconnected network was designed. The network communication based on the TCP/IP protocol was realized through NB-IoT module or GPRS module. The experimental results show that the data transmission of NB-IoT network is stable. The system can automatically switch to GPRS network as the NB-IoT network is abnormal. In the absence of network, the data is stored in SPI Flash. The collected data can be reloaded as the disconnected network is recovered.
A Cross Domain Feature Extraction Method for Bearing Fault diagnosis based on Balanced Distribution Adaptation

Traditional intelligent fault diagnosis techniques for rotating machines have two limitations: 1) Big data with fault information is not available in some cases; 2) The training and testing data are often drawn under discrepant distribution. Thus, transfer component analysis (TCA) has been designed to reduce the distance of marginal distribution between domains. The joint distribution adaptation (JDA) was proposed to simultaneously reduced the difference between the conditional distribution and marginal distribution in source or target domains. However, these two distributions are often treated equally in these existing methods, which will lead to poor performance in practical applications. Therefore, a cross-domain feature extraction method based on balanced distribution adaptation algorithm(BDA) has been proposed, which can adaptively utilize the importance of difference between marginal distribution and conditional distribution. It should be noted that several existing cross domain feature extraction methods can be treated as special cases of BDA. As a new method in the field of transfer learning, BDA is an effective cross-domain feature extraction method. The validity of the BDA algorithm has been successfully evaluated in the actual data set in this paper.
Solar Power Generation Forecasting Service

Starting in February 2019, the small-scale electricity brokerage market was opened and electricity aggregators were allowed to sell electricity markets. The ability of electricity aggregators to accurately predict the generation of electricity is competitive in electricity market and Renewable Energy Certificates (REC) transactions. Therefore, this paper presents a design and implementation case of the forecasting web service of the electricity generation from distributed energy sources for electricity aggregators of the small-scale electricity brokerage market.
Modeling and Control of a Cascaded H-bridge Multilevel Inverter for Hybrid Generation Sources based Islanded Microgrid

Multilevel inverters are readily used in renewable energy applications to overcome issues such as high harmonics distortion, higher switching losses and high voltage stress. This paper highlights a control scheme for multilevel inverter in a hybrid generation based islanded microgrid.Photovoltaic arrays and fuel cells are the prime sources for the multilevel inverter. Supercapacitor is employed as the energy storage system to overcome the slow nature of FC and variable nature of PV arrays. The control scheme includes voltage controlling ability of the inverter along with the pulse width modulation technique to generate controlled switching pulses for multilevel inverter switches. The results are verified through a simulation model on MATLAB / SIMULINK environment.
Non-linear Dynamic Modeling, Simulation and Control of 5-phase 10/8 Switched Reluctance Motor for Electric Vehicle Application

This paper presents non-linear dynamic modeling, simulation and control of 3.2kW, 5-phase, 10/8 switched reluctance motor (SRM) using flux linkage and machine torque static data acquired from Finite Element Method (FEM) Simulations. Initially, machine geometry is completed in ANSYS RMxprt tool and then ANSYS Maxwell electromagnetic simulation tool is used to capture magnetization data and it is stored in the form of look-up tables. Using per phase equations of motor and mechanical equations, the machine is modeled using 2-D look-up table blocks in MATLAB/Simulink. Additionally, to enhance the variable speed operation of SR motors, cascaded technique of control is employed in which PI controller (outer loop) is used for speed control and a Hysteresis controller (inner loop) is used for current control. The performance of SRM is studied at various load and speed conditions in open loop and closed loop. The MATLAB model developed is easy to modify, can be used for initial performance study and provides a better solution for selecting appropriate strategy of controlling the motor.
Cross-Subject Emotion Recognition with a Decision Tree Classifier Based on Sequential Backward Selection

Many of the current emotion recognitions are intra-subject, the reason is that recognize cross-subject emotions have always been difficult. In this paper, we proposed a decision tree classifier based on sequential backward selection (DT-SBS), and "leave-one-subject-out" verification strategy was used for evaluating performance of cross-subject emotion recognition. Power spectral density (PSD) was extracted from EEG signals as feature set. The emotion recognition performance of the method we proposed was examined on EEG data of a group of subjects. The pictures were utilized to stimulate the emotion of 12 subjects and we collected their EEG signals (PEEG), the mean recognition accuracy of 65.8%. The dataset for emotion analysis using physiological signals (DEAP) was utilized to verify the method we proposed in this paper and the mean recognition accuracy of 65.2%. Compared with the performance reported in the existing literature, the method we proposed in this paper was effective in cross-subject emotion recognition. In addition, the emotion recognition performance of different rhythms were explored, we found that the mean recognition accuracy of PSD in the gamma rhythm achieved the best mean recognition accuracy compared with the other rhythms; the high frequency bands contained higher emotional information than the low frequency bands.