Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Excitations by the Current Injected Through the Carbon Nanotube Surface of an Intracortical Electrode in Vivo

We fabricated carbon-nanotube-coated neural electrodes that can be inserted into the cerebral cortex in vivo. Using the fast voltage-sensitive dye imaging technique, we visualized spatio-temporal neural responses in the mouse cortex to microstimulation delivered through these electrodes. The experimental results showed the usability of the electrodes for inducing spatially confined neural excitations in vivo. This type of stimulation electrode is expected to have higher charge conductivity and cell membrane affinity at the electrode-tissue interface as the coating layer of the carbon nanotube becomes thicker while maintaining the geometric cross-sectional area. This might offer an advantage over the conventional metal-surface electrode.
HeteroCore GPU to Exploit TLP-Resource Diversity

Graphics processing units (GPUs) are widely adopted as compute accelerators in cloud computing environments and supercomputers. Sharing GPU resources in such environments requires effective multitasking support. Unfortunately, conventional GPUs lack the ability to adapt to diverse thread-level parallelism (TLP) resource demands among co-executing kernels. Previous work such as SM partitioning and simultaneously multitasking (SMK) increase system throughput, however, they degrade per-application performance significantly. This paper proposes the HeteroCore GPU to significantly improve multitasking performance with a similar area cost as a conventional GPU. After rebalancing TLP-related SM resources, a HeteroCore GPU consists of two types of SMs to support diverse TLP-resource demands. Dynamic scheduling performs low-overhead spatial profiling during runtime across the different SM types and steers scheduling decisions based on the TLP-resource demands of the co-executing kernels. Compared to a conventional GPU, HeteroCore GPU improves system throughput by 20.1 percent on average (up to 80.9 percent) and per-application performance by 29.8 percent on average (up to 50.3 percent), for workload mixes composed of kernels with different TLP-resource demands.
Development of Dual Energy Container Inspection System for Harbor Security in KAERI

The use of multiple energy X-rays can provide additional information as compared to a single energy X-ray for the security inspection of cargo such as containers. Using the 9/6 MeV dual energy X-ray and material discrimination algorithms, it is possible to distinguish between low-density (organic) and high-density (inorganic) materials through the intended color mapping. The material discrimination algorithm can obtain the material information of the image used for screening by exploiting the characteristic that the energy-dependent attenuation coefficient is different even though the same material is used. Based on these technologies, we have developed a security inspection system for container cargo that consists of a radiation source, a detector array, four-collimators, a moving stage, and an imaging system. The radiation source was developed based on a dual-energy radiofrequency (RF) electron linear-accelerator (LINAC) with a spot size of 0.9 mm, and a 5.5 MW S-band magnetron was used as a RF generator. The detector is composed of 43 modules in one array and 32 channels per module, and the moving stage is designed and manufactured to transport cargo of up to 70 tons in the horizontal direction of the radiation beam at an optimized speed of 300 mm per second. The imaging system controls the whole process of the security inspection system from the operation of equipment to the acquisition and discrimination of images. In this paper, we introduce the characteristics of the developed inspection system and explain the results obtained by ANSI N42.46, the international recommendation standard for a container scanner performance test.
Robust Frequency Control for Varying Inertia Power Systems

Increased penetration of fluctuating Renewable Energy Sources (RES) adds a significant uncertainty in the dynamic behavior of the power system. Varying power infeed from RES significantly affects the number of conventional generators to be dispatched at any time instant. As a result, system parameters, such as inertia and damping can no longer be considered constants, but instead they obtain a time-varying profile. In this paper, a robust frequency control scheme is introduced to account for the time-varying system inertia and damping under increased RES penetration. The proposed method is based on an H∞ loop-shaping design procedure, and it guarantees good frequency response for varying levels of inertia and damping. After presenting the impact of varying system parameters to the system dynamic behavior, the design method for the proposed controller is presented, and its performance is demonstrated in case studies.
A Survey of the EIGRP Standard and Following Open-Source Implementations

This paper surveys all currently available open-source EIGRP implementations and provides an overview of the EIGRP protocol itself as defined in its open specification released in RFC 7868. The reader will also become acquainted with the architecture and history of Quagga routing suite and its fork. , FRR. Features and the state of all the current open-source EIGRP implementations are compared to each other and the RFC. Emphasis is placed on Quagga one developed on Department of InfoCom Networks. , University of Žilina.
Vision Inspection with Neural Networks

This work describes a system for extracting and classifying defects inside bottles for cosmetic and pharmaceutical use. The system integrates various defects identification and automatic classification algorithms based on neural networks (NN). The aim is to be able to identify defective bottles at the end of production chain. In a set of 60 bottles, 3600 images were taken, 60 for each. We extracted 4161 defects of which, 70% was used for training and 30% for testing the neural network. We considered five defect classes (rubber, aluminum, glass, hair and tissue) with more than 90% accuracy on the test set.
Effect of Different Circuit Training on Cardiovascular Responses in Cirrhotic Patients

Abstract The purpose of this study was to analyze and compare heart rate (HR), heart rate variability (HRV), and blood pressure (BP) responses during and after circuit resistance training with 1 min. (CIR1) vs. 2 min. (CIR2) rest intervals between exercises in liver cirrhosis patients. 10 patients (7 women, 3 men) participated in 2 experimental sessions (CIR1 and CIR2) in random order. Each experimental session consisted of 6 exercises performed for 3 sets of 10 repetitions with 70% of the 10-RM load. The only difference between experimental sessions was taking either 1 min. (CIR1) or 2 min. (CIR2) of rest between exercises. Prior and during each session, BP, HR and HRV (beat-to-beat intervals) were assessed. Training efficiency were significant higher for CIR1 when compared to CIR2, since same total training volume was performed in less time. Both protocols elicited significant changes in HR, SBP, and SampEn. Based on the ∆HR and ∆IRR between protocols, higher cardiovascular stress was observed for CIR1 vs. CIR2. However, the patients presented a similar recovery pattern between experimental sessions, and did not present significant alterations in the HRV at 60 min post-workout. In summary, the fast adjustment of autonomic modulation in HRV and BP suggests that circuit resistance training with 1-min or 2-min rest intervals between exercises elicits typical cardiovascular responses in liver cirrhosis patients.
Goal Formulation: Abstracting Dynamic Objectives for Efficient On-chip Resource Allocation

Run-time resource management of mobile heterogeneous systems is challenging due to the limited energy budget that has to be allocated among diverse workloads. User interaction within these systems alter the performance requirements, which often conflicts with concurrent applications’ objectives and system constraints. Current resource allocation approaches focus on optimizing fixed objectives, ignoring the variation in system and applications’ constraints at run-time. For adaptive resource allocation, it is necessary to abstract the applications’ and system’s requirements into goals, which can be dynamically formulated as a weighted combination of different objectives. We highlight the problem by illustrating the limitation of state-of-the-art resource allocation approaches and motivate the need of a goal management solution, which dynamically prioritizes different objectives and switches between them to adapt to the environment.
Survey on Communication in Internet of Things Environment

In Internet of Things network connects a big amount of different devices. These devices can gather or send data and they are used in many areas e.g. Education, Environment, Health security and others. In order to build an effective network with a great communication ability it is necessary to know what communication in Internet of Things offers. With communication capability knowledge we can build effective Internet of Thing networks. In this paper, we examine different protocols, technologies, routings, quality of communication and some negative impacts on communication. We research these things to better understand, where the potential for improvement is.
Error Resilient Transmission for Block Compressed Sensing of Color Images

Block compressed sensing (BCS) is a new research branch in compressed sensing, which is famous for its superior performances. Evaluations between compression and reconstructed quality are based on the error-free transmission, and we look for the error resilient transmission of BCS over lossy channels. We propose to employ multiple description coding (MDC) to protect the BCS coefficients at the encoder. Then, protected coefficients are ready for transmitting over multiple lossy channels. Finally, we apply the capability of MDC and the high correlations between color planes for image reconstruction. Simulations have presented the enhancements of protected image qualities for the proposed algorithm.
A Study on Indoor Positioning System Based on Attitude Estimation by Sensors and Distance Estimation by SOKUIKI Sensor

In this paper, we propose a new positioning method based on an attitude estimation by the accelerometer, gyroscope and so on, a distance estimation by the SOKUIKI sensor and a reflection surface information. The proposed system estimates the attitude of the terminal by using measurement data from the sensors. At the same time, the SOKUIKI sensor provides an estimate of the distance between the terminal and reflection points. We estimate the location of the terminal so as to minimize squared error between the measurements and a hypothesis based on reflection surface information of surrounding structure.
S-Band GaN Single-Chip Front End for Active Electronically Scanned Array With 40-W Output Power and 1.75-dB Noise Figure

The design, realization, and tests of an S-band gallium nitride (GaN) monolithic microwave integrated circuit (MMIC) single-chip front end (SCFE) is presented. The MMIC, realized on the 250-nm gate length GaN process available from the united monolithic semiconductor, integrates high-power and low-noise amplification together with the transmit (Tx)-receive (Rx) switch in a <inline-formula> <tex-math notation="LaTeX">$7\times 7$ </tex-math></inline-formula> mm<sup>2</sup> chip area. The SCFE has been conceived for active electronically scanned antenna applications in S-band. In the Rx mode, noise figure (NF) lower than 1.75 dB and gain better than 30 dB have been measured. In the Tx mode, output power and power added efficiency better than 45 dBm and 42%, respectively, have been achieved, with an associated gain higher than 35 dB. Such performance has been measured over a 13% fractional bandwidth in S-band. To the best of the authors’ knowledge, this is the first GaN SCFE operating in S-band.
Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades

As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the corresponding uncertainty to the classical FMEA approach still needs further study. In this paper, we argue that the subjective assessments of FMEA experts can be adopted to model the weight of each FMEA expert, which can be regarded as a data-driven method for ambiguity information modeling in FMEA method. Based on this new perspective, a modified FMEA approach is proposed, where the subjective uncertainty of FMEA experts is handled in the framework of Dempster–Shafer evidence theory (DST). In the improved FMEA approach, the ambiguity measure (AM) which is an entropy-like uncertainty measure in DST framework is applied to quantify the uncertainty degree of each FMEA expert. Then, the classical risk priority number (RPN) model is improved by aggregating an AM-based weight factor into the RPN function. A case study based on the new RPN model in aircraft turbine rotor blades verifies the applicable and useful of the proposed FMEA approach.
A License Plate Recognition using Neural Network and Autonomous Mobile Robot in Intelligent Parking Lot Management System

In this paper, we developed not only the ROS based autonomous mobile robot (AMR) in order to control the parking lot management (PLM), but also the central control system (CCS) of the PLM through heterogenous wireless network. The AMR is equipped with RBG-D and LIDAR so as to capture the images from the parked cars as well as to execute the SLAM of the AMR. The images are extracted from information of RGB-D taken by the PLM for the license plate recognition (LPR) and the LPR is based on the support vector machine while maneuvering the autonomous management of the parking lot. Through applying the developed autonomous mobile robot for PLM and the SVM based LPR to the real intelligent parking lot and analyses of the extracted data, we confirmed the usefulness of the proposed algorithm.
A Cyber-Physical Production System Framework of Smart CNC Machining Monitoring System

Computer numerical control (CNC) machine tools are now moving toward high precision, high speed, and complex functional machining. Machining monitoring system is important to achieve intelligent control of machining process to produce complex geometric features and precision parts. Traditional NC machining monitoring system is limited in data features, low in adaptability, and slow in transmission efficiency, and hard to achieve intelligent online assessment. To overcome these difficulties, this study proposes a smart monitoring system for CNC machining based on Cyber-Physical Production System (CPPS) framework. It is built on the CNC machine tool physical and virtual modeling, process monitoring, and big data analytics, and then synergized into a system through a distributed network. Under the CPPS framework, the smart monitoring system is divided into control layer, network layer, and decision layer. The function of each layer and the key technologies of the CPPS involved are discussed. Case studies of machining process monitoring are studied in the applications.
A novel case of Greenberg dysplasia and genotype–phenotype correlation analysis for LBR pathogenic variants: An instructive example of one gene‐multiple phenotypes

Greenberg skeletal dysplasia is an autosomal recessive, perinatal lethal disorder associated with biallelic variants affecting the lamin B receptor (LBR) gene. LBR is also associated with the autosomal recessive anadysplasia‐like spondylometaphyseal dysplasia, and the autosomal dominant Pelger–Huët anomaly, a benign laminopathy characterized by anomalies in the nuclear shape of blood granulocytes. The LBR is an inner nuclear membrane protein that binds lamin B proteins (LMNB1 and LMNB2), interacts with chromatin, and exerts a sterol reductase activity. Here, we report on a novel LBR missense variant [c.1379A>G; p.(D460R)], identified by whole exome sequencing and causing Greenberg dysplasia in two fetuses from a consanguineous Moroccan family. We revised published LBR variants to propose a genotype–phenotype correlation in LBR associated diseases. The diverse phenotypes are correlated to the functional domain affected, the heterozygous or homozygous state of the variants, and their different impact on the residual protein function. LBR represents an instructive example of one gene presenting with two different patterns of inheritance and at least three different clinical phenotypes.
Performance Comparison of Machine Learning Models Trained on Manual vs ASR Transcriptions for Dialogue Act Annotation

Automatic dialogue act annotation of speech utterances is an important task in human-agent interaction in order to correctly interpret user utterances. Speech utterances can be transcribed manually or via Automatic Speech Recognizer (ASR). In this article, several Machine Learning models are trained on manual and ASR transcriptions of user utterances, using bag of words and n-grams feature generation approaches, and evaluated on ASR transcribed test set. Results show that models trained using ASR transcriptions perform better than algorithms trained on manual transcription. The impact of irregular distribution of dialogue acts on the accuracy of statistical models is also investigated, and a partial solution to this issue is shown using multimodal information as input.
Bistable Perception Is Biased by Search Items but Not by Search Priming

During visual search, selecting a target facilitates search for similar targets in the future, known as search priming. During bistable perception, in turn, perceiving one interpretation facilitates perception of the same interpretation in the future, a form of sensory memory. Previously, we investigated the relation between these history effects by asking: can visual search influence perception of a subsequent ambiguous display and can perception of an ambiguous display influence subsequent visual search? We found no evidence for such influences, however. Here, we investigated one potential factor that might have prevented such influences from arising: lack of retinal overlap between the ambiguous stimulus and the search array items. In the present work, we therefore interleaved presentations of an ambiguous stimulus with search trials in which the target or distractor occupied the same retinal location as the ambiguous stimulus. Nevertheless, we again found no evidence for influences of visual search on bistable perception, thus demonstrating no close relation between search priming and sensory memory. We did, however, find that visual search items primed perception of a subsequent ambiguous stimulus at the same retinal location, regardless of whether they were a target or a distractor item: a form of perceptual priming. Interestingly, the strengths of search priming and this perceptual priming were correlated on a trial-to-trial basis, suggesting that a common underlying factor influences both.
A Few of the Most Popular Tools for Evaluating Supercomputers

Computer benchmarks are computer programs that form standard tests of the performance of a computer and the software through which it is used. In this paper we discuss the purpose, significance and method of benchmarking supercomputers, describe the state of the art, and review a few of the mainstream benchmarks for supercomputer evaluation, among them, Linpack is the most popular for the famous TOP500 ranking list. In addition to this, there is the benchmark for the Graph500 ranking list which pays attention to the data processing ability, there are the Rodinia and SHOC benchmarks for evaluating the heterogeneous supercomputers composed of both GPUs and multi-core CPUs, there is still the DEISA benchmark for various other purposes.
Current Harmonics Mitigation in a Wind Energy Conversion System Through the Selection of the PI Control Parameters in the Rotor Side Converter

This paper presents the mitigation of current harmonics in the stator of a Wind Energy Conversion System based in a doubly fed induction generator and a back to back converter. This converter consists in a Rotor Side Converter and a Grid Side Converter, and the current harmonics mitigation occurs through the selection of the PI parameters in the Rotor Side Converter control. The case of study is a test rig based on a 372W doubly fed induction generator, and the considered grid voltage harmonics components are fifth and seventh. Simulations are implemented in PSIM in sub-synchronous speed, testing different frequencies designs in the Rotor Side Converter control in order to evaluate the current harmonics compensation.
Power loss comparison of DC Side and AC Side Cascaded Modular Multilevel Inverters

High-voltage DC (HVDC) transmission systems based on voltage source converter technology are increasingly being used for interconnecting power networks and for transporting energy from remote renewable energy sources. Modular multilevel converters (MMCs) are emerging as the technology of choice for future HVDC transmission systems. Several MMC topologies have been introduced for high power applications and among them the AC side and the DC side cascaded topologies have received most interest because of their high efficiency, low switching losses and good modularity. In high power applications, the efficiency of the converter is an important consideration. Hence it is essential converter power loss analysis is addressed at design stage. Due to the high number of switches the various MMC topologies, the power loss calculations is particularly complex. This paper presents the analysis of the power losses in both DC side and AC side cascaded converters and compares their overall efficiency for a 500 MW power rating. The nominal values of efficiency quoted for an existing HVDC interconnector between Ireland and Wales are used to verify the methodology used for power loss calculations presented in this paper.
An Improved Human Activity Recognition by Using Genetic Algorithm to Optimize Feature Vector

Nowadays, human activity recognition is emerging as one of the most trending research areas since it supports a wide range of applications, especially in human tracking and sensing. Together with the advancements of embedded devices and sensors, we recognize that smartphones can be exploited as an efficient way to perform human activity recognition since they have become an essential part of humans’ daily life. However, the accuracy, execution time and memory consumption are some major challenges in developing a recognition algorithm on smartphones. In this paper, we aim to tackle those challenges by adopting the Genetic Algorithm to optimize the feature vector. The experimental results show that the size of the feature vector is notably diminished using our proposed method. When using the optimized feature vector in machine learning models like Support Vector Machine and 2-Stage Continuous Hidden Markov Models, it has attained high accuracy while the execution time and memory consumption are greatly reduced in comparison with using the non-optimal one.