Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping and Priority Assignment for Real-Time Network-on-chip with Static and Dynamic Applications

Networks-On-Chip (NoC) has emerged as a solution for addressing the limitation of bus-based communication in multi-processor system designs. Some of these systems are designed for executing real-time services which are associated with deadlines, failing to meet these deadlines may lead to failure of the system. The problem of Task Mapping and Priority assignment becomes more crucial when multiple applications are targeted on a platform in realtime scenario. Real-time schedulability analysis is performed assuming worst case scenario to get the lower bound of schedulability for tasks and flows. The term schedulability defines the number of tasks and flows meeting the deadlines. A broader range of Embedded System consists of both static and dynamic applications. The resources for static and dynamic applications are to be allocated separately. Considering mixed requirement of static and dynamic application and with major focus on dynamic scenario in real-time system, a novel approach for mapping and flow priority assignment has been reported in this paper. It extends the state of the art works in Deadline and Energy Aware Dynamic Mapping and Scheduling (DEAMS) algorithm with flow priority assignment approach obtained by modifying Graph based Exhaustive Search Algorithm (GESA). Current DEAMS algorithm does not consider the network congestion and flow priorities while mapping the tasks instances. In order to get better schedulability in dynamic scenarios, GESA has been modified to work in dynamic situations, which considers the flow dependencies as well. The graph based algorithm puts check for schedulability in a more deterministic way, making it more appropriate for real-time systems. The motivation behind the work presented in this paper lies in probable application of Cyber Physical Systems, having static and dynamic resource allocation for applications in real-time.
Design and Optimization of a Real-Time Asynchronous BCI Control Strategy for Robotic Manipulator Assistance

This paper presents the design and evaluation of a robotic system controlled by motor imagery based Brain-Computer Interface (BCI), which allows users to perform reach-grasp-release activities in 3D space. The sequential axis control methodology is used in the research to address a low information-transfer rate problem, where a participant has to provide several low-level commands to the robot, such as position on x, y, and z-axes, and close/release gripper actions. With the advantage of the motor imagery paradigm, the system is asynchronous, and the operator performs self-paced control. The BCI system is based on sensory-motor rhythms elicited by electroencephalographic (EEG) signals in the frequency domain to classify mental intent in real-time by a soft voting ensemble classifier model. The experimental results have shown the feasibility of controlling a six-degree-of-freedom robot manipulator with the classification performance in the range of 64 - 86% tested on seven healthy individuals.
Characterization of CdZnTe detector for spectral Computed Tomography

The Cadmium Zinc Telluride (CZT) is a room temperature semi-conductor detector with the photon counting capability at a very low X-rays energy and thus can be used for spectral Computed Tomography (CT) imaging. The goal of the study is to characterize the CZT detector for such applications. The device investigated (MXA-128) in this study which comprises of one-twenty-eight pixelated CZT detectors with a pixel dimension of 0.9*0.9 mm2 and a crystal dimension of 16.7*4.4*2.0 mm3. The device is powered by a high voltage module. The power module is adjustable to deliver power from 0 to 1 kVp. The pixelated detectors are arranged in four-pixel array modules with each one containing a linear array digital base board and a read-out module. Each module has 32 channels. Each of these channels can be configured via different channel parameters. The data can be readout in serial mode. The energies of the detected X-ray are recorded and separated in five different energy bins using a 16-bit system. The minimum and maximum X-ray energy detection capability of the detector is 20 and 160 keV respectively. A tungsten anode X-ray source with a power rating from 0 to 35 kVp and a current rating from 0 to 1 mA was used to investigate the detector sensitivity using a conical shaped water phantom having different thickness (range: 29 to 78 mm). The results indicate that the CZT detector is sensitive to the small thickness variation and can be used for both preclinical and clinical spectral CT imaging applications.
A Universal ADC for Sensor Applications

This paper proposes and discusses a universal ADC for sensor applications that can covers almost all sensor applications with one ADC. The combination of SAR ADC and incremental ASADC can cover high speed with low resolution and low noise with low speed applications with a unified ADC. Two prototype ADCs have been developed for general purpose sensing systems and for CMOS image sensors. The former ADC realized open-loop integrators using dynamic amplifiers. High SNR of 84 dB has been achieved and high FoMs over the 170 dB can be kept in wide signal frequency from 62.5 to 625 kHz and the power dissipation is scalable with sampling rate. The SAR+ΔΣADC for CMOS image sensor can reduce the noise down to 66 μV and realized small occupied area of 20 μm x 770 μm.
Model Construction of the Electric Methane Generation Based on Biocatalyst

There are rich renewable solar and wind sources in China, but large amounts of carbon dioxide (CO2) emissions are urgent at the same time. Electromethanogenesis (EMG) can convert electricity and CO2 into methane by using the biocatalyst of methanogen, which can provide an effective solution for electricity storage and reduction of CO2. The advantages are as follows. (i)It can achieve the long-term and large-capacity electricity consumption to avoid large power curtailment in the distributed generation system. (ii) CO2 is applied as the reactant, which can mitigate the greenhouse effect caused by CO2 . (iii) The self-propagated biocatalysts are superior to the chemical catalysts in the cost and stable performance. (iv) EMG can be considered as the junction among electricity energy, chemical energy and heat energy, which can promote research on the Integrated Energy Internet. In this paper, a well-designed electrolytic cell structure and methane-generated principle in the EMG system are introduced firstly. The author try to analyze the influence and the growth of cathode methanogens in this new area based on Nyquist diagram of the electrochemical impedance. Finally, the EMG electrical model is constructed firstly by recording working current and methane production with the terminal voltage change.
Audio Feature Analysis for Trombone

The trombone is a mid-bass instrument in the brass family, its tone is high, brilliant, solemn and full, and the sound is bright and powerful. When it is weak, it is gentle and euphemistic, and the tone is clear and unified. This paper introduces the classification and characteristics of the trombone, as well as the basic playing skills such as single spit method and double spit method. Then, the characteristic analysis of the trombone audio signal is carried out, and the audio of the single spit method and the double spit method is zero-crossing rate and short-time auto-correlation experiments, experimental analysis shows that the short-term auto correlation function can effectively obtain the pitch information of the audio.
Identifying error features in a MI-BCI system using microstates

Motor-Imagery Brain-Computer Interface (MI-BCI) is a useful method for identifying a user's motor intention when they imagine a certain movement. However, the accuracy of this approach is low, and varies depending on the performance of users. In order to overcome the limitation, we focused upon the detection of error due to the low MI-BCI performance. We used microstate analysis to produce a sparse feature set of large-scale brain network activity. Using this analysis, we developed a new feature from the error component. We conducted experiments with the 10 subjects (6 male, 4 female, aged 26.6±2.91 years) by presenting visual stimulus in real time. In the experiment, 30% of all trials were configured to randomly generate error responses. The top five microstates to show the best correct/false responses were calculated. Microstates MS1 to MS4 produced the same results for both correct and incorrect responses, but microstate MS5 showed a difference between correct and false responses. Microstate MS5 is a representation of EEG response occurred within 400 ms after visual stimulus. We would say that ‘correct’ and ‘false’ responses can be distinguished using the microstate MS5.
A Driving Attention Detection Method Based on Head Pose

Head pose is an important indicator of driving attention detection. During driving, head pose, including head position and head movement, can infer the driver's attention. This paper presents a novel method for collecting driver's head pose information using a built-in accelerometer and gyroscope head-mounted inertial sensor. In our experimental study, we designed 10 scenes that are easy to distract from driving. And five subjects were asked to wear a head-mounted inertial sensor to drive the driving simulator. This driving simulator is equipped with real driving conditions such as brakes, steering wheels, accelerators and so on. While driving, subjects need to complete the designed driving scene in order. Subsequently, We perform pre-processing such as Savitzky-Golay filtering and windowing on data collected by inertial sensors with built-in accelerometers and gyroscopes. The time domain and frequency domain features of the data are then extracted in the corresponding window. Finally, we designed a random forest model to detect driving attention. Our simulation experiments show that our proposed method of collecting data using the built-in accelerometer and gyroscope's head-mounted sensor can achieve higher precision, recall and F1_Score.
Extended Rationale-Based Model for Tacit Knowledge Elicitation in Requirements Elicitation Context

The Requirements elicitation involves knowledge intensive and collaborative activities. Requirements engineering research has proposed a range of knowledge elicitation and requirements gathering techniques, few of which apply specific strategies for eliciting tacit knowledge from stakeholders. The difficulty of taking the deliberate advantage of important tacit knowledge of stakeholders is one of the eminent problems in requirements elicitation. This paper proposes a model to define and elicit the tacit knowledge that has been generated during the requirements elicitation process. The model is based on adopting and extending the rationale model for requirements rationale knowledge elicitation in the requirements elicitation process context. Moreover, this paper presents a representation code to express the tacit knowledge in this context. Finally, to evaluate the feasibility of the model, a survey instrument was applied on domain experts to gather their opinion regarding the ability of the proposed model in facilitating tacit knowledge elicitation. In addition, controlled experiment used to evaluate the proposed model. To explore the model, a post-questionnaire was used to identify participants’ feedback. The findings of the evaluation methodologies that were used showed that the proposed model can facilitate tacit knowledge elicitation in the requirements elicitation context. Also, the proposed model has a significant effect on improving the requirements elicitation.
Edge Computing to Support Message Prioritisation in Connected Vehicular Systems

The Internet of Things (IoT) is rapidly evolving, and its need for low-latency communication, instant computation and dynamic security demands a novel approach. Three-tier architectures comprising a centralised cloud, decentralised edge cloud and end nodes, is increasingly being considered to address these requirements. The Internet of Vehicles IoT) is a particularly important flavour of the IoT that is attracting significant academic, governmental and industrial interest. This research considers a three-tier architecture approach to providing edge cloud-assisted connected and autonomous vehicles. The paper describes the characteristics of edge cloud-assisted CAV systems and presents some associated applications based upon the architecture. We investigate the security requirements for such systems. We describe a new method for prioritising Cooperative Awareness Messages(CAMs) based upon these requirements in the edge cloud.
Sampled-data consensus control of MUSV systems with channel fading and transmission delay

This paper studies sampled-data consensus control of a collection of unmanned surface vehicles (USV) operating in network environments with fading channels and time-varying transmission delay. The channel fading is modeled as each independent stochastic process whose probability distribution is known. By considering the effects of channel fading and transmission delay from sampler to the controller, a new MUSV system model is formulated in the framework of network. With the novel established model, stability analysis is given at first, then the sampled-data consensus controller is designed, which also extends to the robust control with wave-induced disturbance. The effectiveness of the presented method is demonstrated by numerical simulation.
Probe Configuration Design for Closed-loop Multi-Channel fNIRS-tDCS BCI

This paper suggests a probe configuration and its attachment on the human scalp to simultaneously measure brain signal and stimulate brain. Concurrent brain measurement and stimulation is essential in fully closed-loop BCI. This paper selects functional near-infrared spectroscopy(fNIRS) and transcranial direct current stimulation(tDCS) as a brain decoding and encoding modalities respectively. Probes of fNIRS optodes and tDCS electrodes should be configured for properly attachment on the head. We design probe array to locate output channel by fNIRS and input channel by tDCS in the same position. tDCS cap and attachment are newly fabricated to apply concurrent multi-channel fNIRS- tDCS BCI.
DOD and DOA estimation for MIMO radar based on combined MUSIC and sparse Bayesian learning

The direction of departure (DOD) and direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar is discussed, and a method combining the multiple signal classification (MUSIC) and sparse Bayesian learning (SBL) is proposed. The proposed method firstly finds the relationship between DOD and DOA via reduced-dimension (RD) MUSIC, and then the dictionary can be constructed with only one-dimensional angle. Finally, DOD and DOA are estimated via SBL. The proposed method estimates two-dimensional parameter without the loss of degrees of freedom (DOF) and uses fully the signal and noise subspaces. Simulation results show that it works better than RD-MUSIC, especially under low signal to noise ratio (SNR).
TB-DNN: A Thin Binarized Deep Neural Network with High Accuracy

Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) applications. However, due to the huge demand for computing and storage resources as well as the high power consumption, deploying DNN models on embedded devices is full of challenges. Recent works have shown that DNN models can be compressed by removing their inner redundancy without obviously performance decay. In this work, we propose a two stage pipeline way to compress the ResNet-14 model and test it on CIFAR-10 and SVHN dataset respectively. Firstly, we use a filter level pruning method to remove the less important filters with different compression rate, and a considerable computation costs are reduced. Secondly, we binarize the pruned model to further reduce the model size and computational complexity. The training results show that we achieve 87.7% accuracy with only 1.86Mb model size on CIFAR-10 and 96.2% accuracy with 1.34Mb on SVHN. Compared to the original model, we have 57% to 68% FLOPs reduction and 45.6× to 63.1× model size compression at the cost of roughly 4% accuracy drop. Finally, we implement the thin binarized ResNet-14 model on the Xilinx KC705 board with a shared, flexible accumulator, which can save 46.8% logic resources. And the entire network parameters are store into on-chip RAM, which can greatly reduce the energy consumption and memory overhead caused by off-chip accesses. The experimental results show that on CIFAR-10 dataset, we achieve an overall performance of 1200 FPS, energy efficiency of 571 FPS/W, which denote 2.3× and 3.6× improvements over the most recent work.
Low-Profile Wideband Conjoined Open-Slot Antennas Fed by Grounded Coplanar Waveguides for $4\times4\,\,5$ G MIMO Operation

Four grounded coplanar waveguide (GCPW)-fed open-slot antennas conjoined into a cross shape with a central circular slot are presented for <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> fifth-generation (5G) multi-input multi-output (MIMO) operation in 3.3–4.2 GHz. The four conjoined slot antennas have a 1 mm low profile (about <inline-formula> <tex-math notation="LaTeX">$0.01\lambda $ </tex-math></inline-formula> at 3.3 GHz) and a compact size of 42 mm <inline-formula> <tex-math notation="LaTeX">$\times42$ </tex-math></inline-formula> mm (about <inline-formula> <tex-math notation="LaTeX">$0.46\lambda \times 0.46\lambda $ </tex-math></inline-formula> at 3.3 GHz) to provide four wide operating bands (about 24% centered at 3.75 GHz) with antenna efficiency better than 40%. The conjoined circular slot leads to enhanced operating bandwidth of the four slot antennas and good decoupling thereof. Owing to the 1 mm low profile, the conjoined slot antennas are promising to be integrated within the dielectric back cover of the 5G terminal device, thereby decreasing the antennas’ volume inside the device. Details of the GCPW-fed conjoined slot antennas for 5G-MIMO operation are presented.
C-arm CT Perfusion Study Using Angiography System

Objective: A newly developed application for cerebral C-arm computed tomography perfusion imaging (C-arm CTP) using an angiography system was investigated. Case Presentation: C-arm CTP protocol continuously collects X-ray projection images during 10 back and forth C-arm rotations. From the collected data, cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and time to peak (TTP) images and multiphase vascular images are reconstructed. C-arm CTP images acquired in patients with acute and chronic major artery occlusion are presented. Conclusion: C-arm CTP using an angiography system is capable of evaluating perfusion parameters in real time, similar to conventional evaluation using multi-detector row CT perfusion (MD-CTP), suggesting its usefulness for examination of ischemic stroke in the angiographic suite.
A Comparative Analysis of Classifiers for Image Classification

Image classification is a supervised learning method used to classify images. There is a challenge in today’s world that Image classification is complex and can be solved using machine learning algorithms. The paper focuses on these tasks using classical machine learning algorithms namely K-Nearest Neighbour (KNN), Multi-Layered Perceptron (MLP) and Random Forest classifier (RF). A comparative analysis is performed on the dataset on the parameters of accuracy, time complexity, F1 score, recall and precision. It is observed that MLP has the highest accuracy of 89.57% followed by random forests having accuracy of 89.2% and lastly a KNN model with an accuracy of 85.87%. Further, it is observed that RF has the lowest time complexity of 34.89 seconds followed by KNN having time complexity of 106.92 seconds and lastly MLP having time complexity of 521.78 second per 100 epochs. This paper can help to realize the potential of neural networks in classification-based tasks where non binary classifications are required which is a typical expectation when real world data is considered.
CrowdTravel: Leveraging Cross-Modal CrowdSourced Data for Fine-Grained and Context-Based Travel Route Recommendation

Travel route planning is generally a very time-consuming task due to massive travel information and diverse travel needs. In this paper, we propose CrowdTravel to automatically extract context-related scenic route recommendation through cross-modal mining and crowd intelligence extraction.First, we leverage the hybrid CNN-RNN model to learn the relationship between the photos and descriptive texts in travelogues. Second, we propose the CrowdRank algorithm to select diverse and representative photos for each scenic spot. Finally, according to users requests and particular contexts, we leverage sequential pattern mining and context filtering to generate visual and context-based scenic routes. We conduct experiments over a dataset of 11,542 travelogues and 11,228 travel albums of eight popular scenic spots in China. Extensive experiments show the effectiveness of the proposed framework.
Awareness of text neck syndrome in young-adult population

Background: Text neck is a repeated stress injury and pain sustained from excessive watching or texting on handheld devices for long periods of time. Dependence of mobile phone is increasing rapidly and people spend long hours on mobile phone that lead to various musculoskeletal problems. This study will help us find the awareness of text neck syndrome and awareness of hazards which are caused by excess usage of phone. This study also aimed at finding the knowledge regarding the preventive measures of text neck syndrome.Methods: The study design was an observational study with a sample size of 311. A self-administered questionnaire was distributed to all subjects. Results were calculated in percentile format.Results: This study stated that 35% population has heard of text neck syndrome out of which only 8% population has knowledge of this syndrome. The results also stated that 21% population have knowledge regarding the preventive measures of this syndrome.Conclusions: This study has demonstrated a low level of awareness of text neck syndrome amongst young adult population. Also it mentioned about lack of knowledge of preventive measures in this population.
The Research on the 3L-Neural Point Clamp Isolation Bidirectional DC/DC Converter

Isolated bidirectional DC-DC converters-IBDCs, with the two-way power flow ability and the high frequency transformer isolation, are the critical units to connect the different kind power such as battery, generator and supercapacitors etc. The three level neural point clamp-3L-NPC IBDC can realize high voltage converting with the lower rated power switches and expand the power grade, and improve the electromagnetic compatibility as well. In this paper a two ports 3L-NPC IBDC was proposed and the control strategies of the 3L-NPC IBDC was invested in detail and the operation were divided into two modes according to relationship between the duty cycle of the inner bridge and the phase shift ratio of the two bridges. The leakage inductance current, the output voltage and the transfer power characteristics are analyzed and the analytical expressions were given according the two operation modes respectively. The average current of the neural point must be kept at zero to ensure the two clamp capacitors have the approximately equal voltage in one period in the both operation modes. The results show that the 3L-NPC IBDCs are feasible and have a good prospect application in the high power conversion.