Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-optimizing Two-layer Network-on-Chip Based on Dominant Network-Flow Adaption

Network-on-Chip (NoC) is an important subsystem of multi-core system design, which can achieve low-latency, low-power, and high-bandwidth communication of data. According to earlier researches, communication between cores has the pattern that a part of flows can dominate network traffics, and it can be predicted by analyzing previous flows. In this paper, a two-layer NoC that employs an online machine learning algorithm to periodically construct predicting models at runtime and builds point-to-point paths between routers for dominant flows is implemented. A cluster-based architecture with all the cores in the same cluster sharing the same circuit-switching router is designed to reduce the number of flows. This architecture declines the complicity of flow analysis and increases the utilization of circuit-switching network. Several methods, such as data compression, are used to reduce the area and power consumption in the hardware design. Experimental results reveal that the proposed design for a 64-core system optimizes packet transmission latency as much as 40% and 18% under static and dynamic benchmarks respectively. Based on register-transfer level (RTL) model, the costs of the hardware design are evaluated. The results reveal that our work obtains latency optimization at the expense of small hardware costs.
Control System of Synchronous Reluctance Motor Based on Fuzzy PI

This paper proposed a method for automatic adjustment of PI parameters using the fuzzy inference system to enhance the control performance when the inductance of synchronous reluctance motor (SynRM) changes and its torque fluctuates with the rotor angle. First, the vector control system of SynRM using MTPA was built. Then, by combining the fuzzy inference system with the PI controller, the PI parameters can be adjusted in real time with feedback errors. Finally, through the co-simulation of the control system and the finite element model of the prototype, it was confirmed that the use of fuzzy PI can more accurately adjust the PI parameters and improve the response speed of the system during speed adjustment.
Noise reduction in a high power VLC signal with an hysteresis band control loop for M-FSK modulation

In this work, we propose the application of Multiresolution Analysis (MRA) for reducing the noise in the signal generated by the FSK (Frequency Shift Keying) modulation used transmitting information through a VLC (Visible Light Communication) channel. In order to apply MRA, the Wavelets of Daubechies and Symlet are used. The effectiveness of this technique depends on the choice of the Wavelet, the decomposition levels of the signal to be analyzed and the threshold level used to eliminate the noise of the detail part. Finally, there is a significant noise reduction in the analyzed signal. To measure this reduction, we use the Mean Square Error (RMS).
Patcher: Towards Fault - Tolerant Probing Planning for In-band Network Telemetry

In-band Network Telemetry (INT) is a recent network monitoring approach. Despite the existence of a few initiatives to orchestrate the collection of in-band network statistics, little has yet been done to coordinate active INT probes to collect network information efficiently while considering the possibility of device failures (e.g., power failure, hardware failure). In this paper, we introduce Patcher - fault-tolerant probing planning for INT. It reconstructs probing cycles affected by failure nodes and optimizes them while ensuring all non-affected links/statistics are still being traversed/collected. Results show that Patcher reduces the number of probe cycles needed up to 5.5x compared to a state-of-the-art solution, while not increasing the INT collectors' load.
Multi-Gigabit Transceivers for Optical Data Communications From the Standardization Perspective

This brief is conceived to present prime line R&D advances in Multi-Gigabit Optoelectronics Communications. In order to allow all these communication systems to be compatible between them and interoperable, it is mandatory that they are developed following a common standard. Ethernet is one of the most well-known and flexible standards for complex systems. To this end, IEEE has developed many different standards for optical data communications. In case of the automotive industry, specific standards are developed in order to accomplish the stringent requirements of the working environment. This tutorial gives a review of the actual standardization processes of the optical communication systems, with special emphasis on the automotive industry. As a complement, it groups some of the most significant circuit design contributions to the State-of-the-Art in the recent years that allow implementing the optical transceivers compatible with the latest standards.
Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross Correlation for Fast and Accurate Stereo Matching on Embedded GPU

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z2- ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.
Motorcyclists Safety Assistant App

Motorcycles are an important part of daily transportation. They are specially used to avoid traffic congestions downtown and most of the individuals tend to use a motorcycle because it takes less time to get to the destination. The rapid increase of motorcycle usage has led to a significant increase in the number of motorcycle-related accidents and fatalities. The reasons for accidents which were considered are speeding, collision with objects, lack of focus or drowsiness where only the rider's head is protected but not the body. And accidents may lead to death when help is not called immediately. By considering these, author introduces a Motorcyclists Safety Assistant Application (MSAA). This research tries to address four major factors that caused most road accidents and fatalities in Sri Lanka. They are excess speed, 360-degree threat detection, motorcyclist safety balloons, and emergency alert system. Here, MSAA can detect the vehicle's real-time speed and inform the user when a certain speed limit has been exceeded. Also, it has proposed a system which automatically detects threats that occur in each collision and alerts the rider via visual and audio cues. Moreover, the next system will be focusing on safeguarding the rider's body by inflating an airbag which will be connected to the rider's jacket. An automatic alert system is also introduced where the main objective is to mitigate the consequences of accidents by sending a message to the registered mobile using wireless communication techniques and checks whether an accident has occurred using vibration frequency limits. Location will be sent through the tracking system to cover the geographical coordinates over the area. The proposed domain successfully contributes to a drastic reduction in road accidents. The ultimate objective is to create a better future for everybody through road safety. The survey conducted to test the user satisfactory level, demonstrated high user satisfaction.
An Efficient Predictive Analysis Model of Customer Purchase Behavior using Random Forest and XGBoost Algorithm

Predictive Analytics is a bough of the advanced analytics which has been used to make predictions about unknown future events. It uses many techniques from Data Mining and Statistics Modelling to analyze the current data. In Statistics Modeling, Regression Analysis algorithms are some of the most popular processes used in Machine Learning Models. Random Forest is a supervised learning algorithm which uses Ensemble Learning method to take advantage of Bootstrap Aggregating. XGBoost is a scalable & accurate implementation of Gradient Boosting Machines (GBMs). It has been proved to push the limits of computing power. It is built & developed for the sole purpose of model performance and computational speed. Customers are the basis for growth of any type of business. In the study of sales and purchase, it is vital & crucial to be able to predict the amount of purchase or sales to increase benefit by catering from specific products to specific demographics. Our prediction analysis model can effectively help to improve the performance and increase the profit margin. Moreover, it can generalize the prediction of purchase or sales figures in any market which depends on the customers' past purchase pattern or behavior.
Estimation of Viewed Images Using Individual and Shared Brain Responses

This paper presents a method to estimate viewed images by using brain responses measured when persons view images. The proposed method predicts visual features of viewed images from not only individual brain responses but also shared brain responses of multiple subjects. Specifically, the proposed method simultaneously estimates the following two projection matrices: a matrix that converts individual brain responses into visual features and a matrix that converts shared brain responses into visual features. The collaborative use of two types of brain responses is the biggest contribution of this paper. Experimental results show that the proposed method improves the estimation accuracy in comparison with methods considering only individual or shared brain responses.
Influence of Low-frequency Harmonic Currents on Radial Electromagnetic Force of PMSM

This paper studies the influence of low-frequency harmonic currents on the radial electromagnetic force of permanent magnet synchronous motors (PMSM). The finite element method (FEM) is used to deal with the additional influence on the radial electromagnetic force caused by the harmonic current. Analyze the ideal sinusoidal fundamental current as a reference. Taking a 48-slot 8-pole interior permanent magnet synchronous motor as an example, the radial electromagnetic force model of a permanent magnet synchronous motor with harmonic current phase parameters is derived. The electromagnetic force results under different current harmonic conditions are discussed, and fast Fourier transform (FFT) is performed. The results show that low-frequency current harmonics have an influence on the radial electromagnetic force, and its influence depends on the phase and amplitude of the harmonic current.
Genetic modification of cystic fibrosis with ΔF508 mutation of CFTR gene using the CRISPR system in peripheral blood mononuclear cells

Objective(s): Cystic fibrosis (CF) is an inherited autosomal recessive disease that is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The present study aimed to investigate the genetic modification of CF with ΔF508 mutation of the CFTR gene using CRISPR in peripheral blood mononuclear cells (PBMCs). Materials and Methods: Two single guide RNAs were designed to target sequences in the CFTR gene. The transfection efficiency of PBMC cells was examined through evaluation of green fluorescent protein (GFP) expression using fluorescent microscopy. Moreover, a sgRNA-Cas9 plasmid was tested to target the CFTR gene. The ΔF508 gene modification was evaluated and confirmed by PCR and Sanger sequencing methods. Results: Our results indicate the feasibility of site-specific gene targeting with the CRISPR/Cas9 system. 33% of the samples were corrected using CRISPR in mutant locus and confirmed by sequence blast at NCBI databases and primers outside the arm locus. CRISPR/Cas9 approach represents an efficient tool to repair the ΔF508 mutation of the CFTR gene in PBMC Cells. Conclusion: Therefore, the CRISPR system can be highly efficient and specific and provides a powerful approach for genetic engineering of cells and model animals. Generally, the proposed method opens new insights into the treatment of human diseases.
Deadbeat Direct Power Control of Voltage- Sourced Rectifier with Reduced Switching Frequency

An asymmetric space vector pulse width modulation (SVPWM) method is proposed in this paper to reduce the switching frequency in the deadbeat direct power control (DB-DPC) method. The DB-DPC method that belongs to the class of predictive control strategy can be used to achieve the fast and accurate control of instantaneous active and reactive power control in the voltage-sourced rectifier (VSR). The switching frequency of the power switches equals to the control frequency in the DB-DPC with the conventional center-aligned SVPWM method. In order to reduce the switching loss, an asymmetric SVPWM method is proposed to halve the switching frequency by adjusting the switching sequence and conduction time for the switching states of the expected voltage vector in the proposed DB-DPC method. A testbench is constructed, and the results obtained from the comparative experiments confirm the feasibility of the proposed modulation method.
Towards a Framework for SLAM Performance Investigation on Mobile Robots

Simultaneous Localization And Mapping (SLAM) is the process of estimating the location of a mobile robot concurrently with constructing a model (the map) of the surrounding environment. The SLAM community has made breathtaking progress over the last two decades, varying from indoor mobile robot navigation to large-scale real-world applications. Many SLAM algorithms have been proposed in both research and applied problems. However, choosing the most suitable one among the SLAM algorithms and building it on a specific robot platform is not always easy. In this study, we develop a framework that allows us to deploy various SLAM algorithms for performance investigations. We perform experiments on two well-known SLAM algorithms, including Hector and Google Cartographer SLAMs, to show how to use the proposed framework for studying SLAM performance in terms of accuracy, processing time, and hardware resource consumption (i.e., CPU and RAM utilization). The experimental results show that Hector SLAM has better accuracy performance than Cartographer. Meanwhile, Cartographer has outstanding time performance but consuming more CPUs and RAM as a trade-off. These results are also valuable references for comparison with other studies.
H2ORAM: Low Response Latency Optimized ORAM for Hybrid Memory Systems

Emerging Non-Volatile Memory (NVM) techniques suffer from many attacks on endurance and data confidentiality. Oblivious RAM (ORAM) can prevent both types of attacks by address obfuscation and encryption. However, ORAM also introduces a lot of redundant memory operations, worsening the NVM endurance and system performance. Furthermore, the slower NVM increases the response latency of target blocks compared to DRAM. As the read requests are on the critical path of program execution, the increased latency further reduces system performance. To alleviate endurance and performance problems, we propose H2ORAM, a low response latency optimized ORAM scheme for NVM. Hybrid DRAM/NVM system is introduced to eliminate 57% read/write traffic to NVM, which improves the lifetime of NVM devices. To reduce response latency of read requests, we securely manipulate data distributions in hybrid memory to speed up hot data access. The manipulation leverages a frequency-based eviction scheme and a data duplication scheme to keep more hot data accessible from DRAM earlier and faster. Moreover, we utilize the empty slots in DRAM region to further improve performance. The experimental results show that 44% response latency reduction and 18% performance improvement are achieved over the basic Ring ORAM with hybrid memory architecture integrated.
Quantify the effect of docetaxel with different concentration on cell mechanical properties using atomic force microscopy

Mechanical properties of cells have been recognized as a biomarker for indicating cellular cytoskeletal organization. On the reason that chemical treatments can lead to rearrangements of cell cytoskeletal, characterization of cellular mechanical properties can provides an insightful approach to evaluate the effects of chemical treatments on cancer cells. The appearance of atomic force microscope (AFM) provides a powerful tool for detecting the nanoscale physiological activity of single living cell. In this paper, the detailed changes of cellular morphology and mechanical properties after docetaxel (DCT) treatment were quantified using AFM. The results showed that the Young’s modulus of the cells after DCT treatment was larger than the untreated cells and the adhesion of the treated cell was lower than before. In addition, these changes was related to the chemical concentration. These results thus provide a unique idea into the effects of chemical on individual cells.
Multi-objective Optimization of Parabolic Trough Concentrated Solar Power with Thermal Energy Storage Plant Parameters using Elitist Nondominated Sorting Genetic Algorithm

Large-scale concentrated solar power (CSP) with thermal energy storage (TES) is the most viable option in a centralized generation for a sustainable energy system. To have a successful CSP-TES plant operation, selecting the best plant configuration is deemed necessary. Thus, this study presented a flexible method to determine the optimal plant parameters while considering the electricity demand curve of the location. Modeling and performance simulation were done in the System Advisor Model. Elitist Nondominated Sorting Genetic Algorithm of MATLAB was used to solve the multi-objective optimization problem. Results showed the optimal performance of the plant in terms of its energy, economic, and environmental aspects. Also, it was established that the parabolic trough CSP-TES plant can provide high electrical output to meet peak demands during nighttime.
Crack Detecting by Recursive Attention U-Net

Crack detecting is a specific domain of semantic segmentation task for solving real-world applications such as pavement crack detection, bridge bottom crack inspection, solar cells or battery components defect detection. Different with general image with rich texture sematic information, road/bridge crack images are often in lack of semantic information which is vital for guiding segmentation. Thus, crack detecting task remains challenge in spite of great success of semantic segmentation neural networks. In this paper, we proposed a simple but powerful U-Net, named as “RAU-Net”, to boost the crack detecting performance by recursive attention mechanism. First, the recursion residual block is used to extract low-level edge features. Then, attention mechanism is introduced for separating the saliency area and the irrelevant background area for accurate locating the cracks. Finally, recursive attention is equipped into U-Net architecture for efficient segmentation. Experimental results demonstrate that the proposed “RAU-Net” outperforms other state-of-the-art neural networks based crack detecting methods.
Chatbot Implementation to Collect Data on Possible COVID-19 Cases and Release the Pressure on the Primary Health Care System

Nowadays, the evolution of artificial intelligence is present in almost every field of the modern science and technology, because it is a powerful tool for analysis and quick decision making, enabling capacity to automate various processes in companies, government institutions, or research groups through one of the preferred techniques for automated software processes. Thus, the use of this kind tool has allowed us to develop a Chatbot (robot) that has the ability to simulate the conversation with a human being, to answer very specific questions [1]. Artificial intelligence has the ability to acquire, and achieve goals in the computer world, trying to emulate the way human intelligence process information. Currently, there are several creations in Chatbot that allows automating recurring processes, can decrease the response times of an activity, guarantee the availability of the service, and or omit the presence of a person, and it is very useful for process where there is a certain type of prevented behavior. In the health area, it is essential to have an assistant who knows how to give information at any time of the day, specially in a worldwide pandemic crises, but in funny hours is more complicated to have an online attendant to answer specific questions about the illness. The present work shows the results of an implementation of a Chatbot based on Artificial Intelligence to help the Medical Department at UDLA University in Ecuador, to detect possible cases of COVID-19, and for help to alleviate the saturated health system, and more importantly to collect information to prevent the dissemination of the infectious disease, we have created a platform to interact with possible infected persons who get the support from the University facilities.
Evaluation of the real-time indoor location and motion direction estimation system applying DNN to RSSI Fingerprints of BLE beacons

We investigated a method to detect the position and direction of travel of a pedestrian walking indoors simultaneously. Multiple time instances of Received Signal Strength Indicator from multiple Bluetooth Low Energy beacons, detected on smartphones carried by pedestrians moving in any of eight directions, are used to train a Deep Neural Network to estimate the location and direction of motion of this smartphone. We compare the estimation accuracy when the RSSIs were interpolated, and when they were not. The position estimation accuracy without interpolation decreased by 40.5% and the moving direction estimation accuracy decreased by 26.9%, suggesting some form of interpolation is required.
Hybrid System to Minimize Damage by Zero-Day Attack based on NIDPS and HoneyPot

This paper presents hybrid system to minimize damage by zero-day attack. Proposed system consists of signature-based NIDPS, honeypot and temporary queue. When proposed system receives packet from external network, packet which is known for attack packet is dropped by signature-based NIDPS. Passed packets are redirected to honeypot, because proposed system assumes that all packets which pass NIDPS have possibility of zero-day attack. Redirected packet is stored in temporary queue and if the packet has possibility of zero-day attack, honeypot extracts signature of the packet. Proposed system creates rule that match rule format of NIDPS based on extracted signatures and updates the rule. After the rule update is completed, temporary queue sends stored packet to NIDPS then packet with risk of attack can be dropped. Proposed system can reduce time to create and apply rule which can respond to unknown attack packets. Also, it can drop packets that have risk of zero-day attack in real time.
A Study of Tourism Proposal System Using AI

It takes a lot of man-hours to add new information and update to the tourist information website. Therefore, we created a website dedicated to Kanazawa City. In the website, users post photos and comments, these data are automatically classified, and the website is updates by utilizing these data. Users can collect fresh information specific to Kanazawa using this website. In addition, we have constructed a mechanism to provide tourist information using chatbot by utilizing the collected information. This paper describes a sightseeing proposal system that utilizes these information which are collected automatically.