Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed-Priority Scheduling and Controller Co-Design for Time-Sensitive Networks

Time-sensitive networking (TSN) is a set of standardised communication protocols developed under the IEEE 802.1 working group. TSN aims to support deterministic communication based on network schedules that are distributively configured. It is widely considered as the future in-vehicle network solution for highly automated driving, where the requirement on timing guarantee is alongside the demand of high communication bandwidth. In this work, we study a setting of periodic control and non-control packets, with implicit and arbitrary deadlines, respectively. As the FIFO (first-in, first-out) queues in the 802.1Qbv switch incur long delay in the worst case, which prevents the control tasks from achieving short sampling periods and thus impedes control performance optimisation, we propose the first fixed-priority scheduling (FPS) approach for TSN by leveraging its gate control features. In this context, we develop a finer-grained frame-level response time analysis, which provides a tighter bound than the conventional packet-level analysis. Building upon FPS and the above analysis, we formulate a co-design optimisation problem to decide the sampling periods and poles of real-time controllers with settling time as the objective to minimise, whilst satisfying the schedulability constraint.
Temporal refinement of sensory‐evoked activity across layers in developing mouse barrel cortex

Rhythmic whisking behavior in rodents fully develops during a critical period about 2 weeks after birth, in parallel with the maturation of other sensory modalities and the onset of exploratory locomotion. How whisker‐related sensory processing develops during this period in the primary somatosensory cortex (S1) remains poorly understood. Here, we characterized neuronal activity evoked by single‐ or dual‐whisker stimulation patterns in developing S1, before, during and after the occurrence of active whisking. Employing multi‐electrode recordings in all layers of barrel cortex in urethane‐anesthetized mice, we find layer‐specific changes in multi‐unit activity for principal and neighboring barrel columns. While whisker stimulation evoked similar early responses (0–50 ms post‐stimulus) across development, the late response (50–150 ms post‐stimulus) decreased in all layers with age. Furthermore, peak onset times and the duration of the late response decreased in all layers across age groups. Responses to paired‐pulse stimulation showed increases in spiking precision and in paired‐pulse ratios in all cortical layers during development. Sequential activation of two neighboring whiskers with varying stimulus intervals evoked distinct response profiles in the activated barrel columns, depending on the direction and temporal separation of the stimuli. In conclusion, our findings indicate that the temporal sharpening of sensory‐evoked activity coincides with the onset of active whisking.
Screen-space VPL propagation for real-time indirect lighting

Reproducing indirect lighting in real time for 3D scenes is a computationally intensive task, but its results add realism and visual fidelity to game-like scenarios and applications. Previous attempts to solve this task explored the use of paraboloid shadow maps for hemispherical visibility queries and Virtual Point Lights (VPLs) for light transport simulations. In our work we aimed at reproducing up to two diffuse bounces of light, while maintaining real-time framerates. We propose an extension to clusterization-based methods in order to sample less VPLs per paraboloid shadow map and capture more bounces of light efficiently. We also propose a projection-aware sampling method to improve sampling efficiency. Our experiments show that, with our approach, it is possible to generate two-bounce VPLs in real time even for low-end commodity GPUs, providing a fast method for indirect illumination.
Study of Light Degradation in High Power LEDs as a Function of the Feeding Waveform

This paper presents the analysis of the lifetime of a power LED based on its power waveforms. In this work, the luminous degradation of the LED is analyzed, based on the LM-80 and TM-21 Norm, comparing it with a curve fitting of the experimental data. The device under test consists of a 1W white LED, which operates for 6500 hours at a temperature of 85° C. The compared waveforms are the direct current and a rectified half-sine wave. The continuous wave presents minimal deterioration and its useful life corresponds to more than 50,000hrs. While the LED powered with a rectified sinusoidal half-wave presents a strong deterioration reducing its useful life to less than 7000 hrs. , and showing a strong deterioration in its elements (silicone mold), in addition to losing its phosphor layer and emitting bluish light. The power waveform reduces the useful life of the LED since it affects its structure and its PN junction as shown in this article.
AVRA: virtual learning environment with virtual reality capabilities

Digital technologies such as the Internet have considerably changed distance learning (DL), enabling new forms of sharing and consuming information. Notably, the so-called virtual learning environments (VLE) consist of educational systems designed to support DL activities. However, VLEs usually do not focus on social interactions among participants, which is also point out as a strong avoidance factor for DL courses. Thus, in this work, we propose assimilating the concepts of interactive and immersive virtual reality environments to devise a VLE, called AVRA, to offer a better learning experience. The analysis shows the AVRA prototype achieves its purpose with satisfactory usability, considering the opinions of two groups of students. In conclusion, we point out lessons learned in this research and provide insights for further developments.
Robots and Wizards: An Investigation Into Natural Human–Robot Interaction

The goal of the study was to research different communication modalities needed for intuitive Human-Robot Interaction. This study utilizes a Wizard of Oz prototyping method to enable a restriction-free, intuitive interaction with an industrial robot. The data from 36 test subjects suggests a high preference for speech input, automatic path planning and pointing gestures. The catalogue developed during this experiment contains intrinsic gestures suggesting that the two most popular gestures per action can be sufficient to cover the majority of users. The system scored an average of 74% in different user interface experience questionnaires, while containing forced flaws. These findings allow a future development of an intuitive Human-Robot interaction system with high user acceptance.
Performance Evaluation of TCP BBR and CUBIC TCP in Smart Devices Downloading on Wi-Fi

A new TCP (transmission control protocol) congestion control algorithm called TCP BBR was proposed by Cardwell et al. in 2016. They showed its excellent performance and TCP BBR has been used in some of Google's services such as Google Cloud and Youtube. They showed also that it achieved good performance fairness among TCP BBR connections in a wired network. In this paper, we evaluate the performance fairnesses among TCP BBR connections and among TCP BBR and CUBIC TCP connections in a smart device's network in which smart devices, such as smartphones and tablet PCs, are connected to a server via a Wi-Fi network. We then show that sufficient fairness is not achieved in some cases.
Diagnostic value of soluble mesothelin-related peptides in pleural effusion for malignant pleural mesothelioma

Abstract Background: Soluble mesothelin-related peptide (SMRP) is a widely studied tumor marker for diagnosing malignant pleural mesothelioma (MPM). This study discussed the diagnostic value of SMRPs in pleural effusion (PE) for MPM. Methods: Medline, Embase, Web of Science, and Cochrane library system were systematically searched on the data of SMRPs in PE for MPM diagnosis. Pooled diagnostic sensitivity, specificity, and symmetric receiver operating characteristic curve were calculated. Results: Thirteen studies fulfilled the inclusion criteria and a total of 3359 cases including 759 MPM cases, 1061 non-MM (malignant mesothelioma) malignant PE, and 1539 benign PE were brought into this meta-analysis. The pooled results of SMRPs in PE for diagnosing MPM were as follows: sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.68 (95% confidence interval [CI]: 0.64–0.72), 0.91 (95% CI: 0.86–0.94), 7.8 (95% CI: 5.0–12.0), 0.35 (95% CI: 0.31–0.40), and 22 (95% CI: 14–35), respectively. The area under the summary receiver operating characteristic curves (AUC) was 0.75 (95% CI: 0.72–0.80). Subgroup analyzes revealed that the AUC of cohort group using histological diagnosis could be improved to 0.86 (95% CI: 0.83, 0.89). The Deek's funnel plot asymmetry test showed no publication bias. Conclusion: Although the sensitivity of SMRPs was low, PE-SMRPs can be a good indicator of the existence of MPM.
Statistical Time Features-based Methodology for Fatigue Cracks Detection in a Four-Story Building

Fatigue cracks are the most common damages encountered in the civil structures. For this reason, early detection of these types of damages can reduce their accumulation in the elements that conform to a civil structure, preventing its possible collapse. In this sense, this work investigates the usefulness of the statistical time features (STFs) for identifying this type of damages in a four-story building subjected to dynamic forced excitations. The proposed methodology's efficacy is corroborated using three levels of damage (i.e., light-, medium-, and severe-fatigue crack). These conditions are artificially created. Results show that the STFs can determine a building exposed to diverse levels of fatigue cracks with high accuracy using only one sensor placed on the structure.
Text Summarization of Indonesian Folklore with Word Frequency Concept

Storytelling is a fun activity, especially for telling a story to children which can affect the character of our children based on the story. Story from the internet resource can be a long story or a short story. For the former, it needs to make a summary to become shorter. When it is a long one, it is more difficult to tell our kids and it is more relevant to make a summary. For creating a summary, it will be easier to proceed with a computer, consequently, it needs a computer application that automatically provides a short overview of the folklore. The Indonesian folklore is summarized by several processes in a web application with a python programming language as a development tool. The process of summarization begins with processing the text that is entered by the user and it is eliminated in all of the special characters. Then the result is processed in the form of tokenizing to get a set of words. From this process, all of the set’s elements that are belong to the stop word list are removed and then the rest of them are counted each for the word frequency that they appear. After finding the word frequency, the sentences are scored by the accumulation of word frequency that they have and are sorted based on this score. Lastly, the sentences are sorted based on each of them appear from the original version and make them be a single paragraph. The result of this research is that text summarization can be carried out with the computer application and python programming languages in the form of a website. The number of sentences that are determined can be selected by the user in the program. Several Indonesian folklores are applied to the application and rated by the reader. Moreover, the application delivers 87% of reader acceptance and it is almost similar to several existing online websites that generate text summary.
Probabilistic modeling of the dynamics of the electrical activity of the heart

A probabilistic model is proposed that explains the regularities of the autocorrelation properties of the time-localized RR interval is proposed. The model describes the formation of the duration of heart contraction intervals (RR intervals) due to the action of the system of "generation" of global and local random events. The sequence of such events in time is considered as a random doubly connected flow. Let’s represented two classes of random time intervals between the events of such a stream: local and global intervals. Assume that global intervals are random intervals local sum and the sum value is a discrete random variable with a binomial (or Poisson) probability distribution. The relationship between the parameters of the developed probabilistic model and existing models for describing the electrical activity of the heart as a process of propagation of an excitation wave in a complex anisotropic biological medium are showed. Equations for estimating the values of the probabilistic properties of informative parameters that reflect the dynamics of intracardiac electrical activity are obtained.
Creating A Syntactically Felicitous Constituency Treebank For Turkish

In this study, Bakay et. al [1] and Yıldız et. al.’s [2] work on Turkish constituency treebanks were developed further. Compared to the previous work, the most prominent feature of this study is the fact that every annotation and refinement process is held manually. In addition, constituency treebank created as a result of this study abides by the syntactic rules and typologic features of Turkish while the trees created by previous studies convey only the translated and simply inverted trees that completely ignore the syntactic properties of Turkish. The methodology followed in this study resulted in a significantly more accurate representation of Turkish language and simpler, relatively flatter trees. The straightforward style of trees in this study reduces the complexity and offers a better training dataset for learning algorithms.
A Fuzzy Logic-based Method for Proton Exchange Membrane Fuel Cell Fault Diagnosis

Due to the fact that proton exchange membrane fuel cell system application is restricted by its limited reliability and durability, an efficient technique for accurate and fast identification of PEM fuel cell faults is highly required. In this study, a PEM fuel cell fault diagnostic method is proposed based on fuzzy logic model and electrochemical impedance spectroscopy. The fuzzy rules used in fuzzy logic model for inferring diagnosis are derived directly from PEM fuel cell faulty mechanisms, thus avoiding inconsistent and even conflicting fuzzy rules used in previous studies. Moreover, its effectiveness in identifying different PEM fuel cell faults and various levels of faults are investigated. Results demonstrate that PEM fuel cell flooding and dehydration can be diagnosed accurately and PEM fuel cell faults at different levels can also be discriminated.
Countering Variations and Thermal Effects for Accurate Optical Neural Networks

Optical neural networks (ONNs) have emerged as a promising high-performance computing platform to accelerate deep neural networks. In ONNs, phases of light are modulated through Mach-Zehnder Interferometers (MZIs), and MZIs are connected in a gridlike layout to implement multiply-accumulate operations. However, ONNs are very sensitive to process variations and thermal effects. This sensitivity leads to a significant degradation of inference accuracy of ONNs and thus renders them unusable in practice. In this paper, we propose a framework to calibrate process variations and counter thermal effects by power compensation. Experimental results demonstrate that the proposed framework can recover the inference accuracy under variations and thermal effects, e.g., from as low as 11.05% back to 74.11% for LeNet-5 on Cifar10, so that ONNs can achieve an inference accuracy similar to the accuracy after software training while providing their high bandwidth in neuromorphic computing.
ECG Signal Processing for Early Detection of Atrial and Ventricular Fibrillation Based on R-R Interval

Atrial and ventricular fibrillation are considered as frequent cardiac arrhythmias. Therefore, early detection of both fibrillation is important to support medical treatment. In this paper, design of detector circuit for atrial and ventricular fibrillation using R-R interval of electrocardiogram (ECG) signals is reported and considered as a solution for early detection. The detector circuit was designed using easy buy components. The ECG signals were classified as Normal Sinus Rhythm (NSR) when the R-R interval is detected regularly, Atrial Fibrillation (AF) when the R-R interval is detected irregularly, and Ventricular Fibrillation (VF) when the R-R interval is not detected. The system was directly tested using three human bodies for NSR category. Due to some limitations, three recorded ECG signals for each AF and VF were taken from PhysioNet database as samples. The system obtained square waves with amplitude of 5 V where every rising edges indicate R peaks. Experiment results show that the system can classify correctly.
AxHLS: Design Space Exploration and High-Level Synthesis of Approximate Accelerators using Approximate Functional Units and Analytical Models

With the emergence of approximate computing as a design paradigm, many approximate functional units have been proposed, particularly approximate adders and multipliers. These circuits compromise the accuracy of their results within a tolerable limit to reduce the required computational effort and energy requirements. However, for an ongoing number of such approximate circuits reported in the literature, selecting those that minimize the required resources for designing and generating an approximate accelerator from a high-level specification, while satisfying a defined accuracy constraint, is a joint high-level synthesis (HLS) and design space exploration (DSE) challenge. In this paper, we propose a novel automated framework for HLS of approximate accelerators using a given library of approximate functional units. Since repetitive circuit synthesis and gate-level simulations require a significant amount of time, to enable our framework, we present AxME, a set of analytical models for estimating the required computational resources when using approximate adders and multipliers in approximate designs. We propose DSEwam, a DSE methodology for error-tolerant applications, in which analytical models, such as AxME, are used to estimate resources needed and the accuracy of approximate designs. Furthermore, we integrate DSEwam into an HLS tool to automatically generate Pareto-optimal, or near Pareto-optimal, approximate accelerators from C language descriptions, for a given error threshold and minimization goal. We release our DSE framework as an open-source contribution, which will significantly boost the research and development in the field of automatic generation of approximate accelerators.
DRAMA: An Approximate DRAM Architecture for High-performance and Energy-efficient Deep Training System

As the density of DRAM becomes larger, the refresh overhead becomes more significant. This becomes more problematic in the systems that require large DRAM capacity, such as the training of deep neural networks (DNNs). To solve this problem, we present DRAMA, a novel architecture which employs the approximate characteristic of DNNs. We make that non-critical bits are not refreshed while critical bits are normally refreshed. The refresh time of the critical bits are concealed by employing per-bank refreshes, significantly improving the training system performance. Furthermore, the potential racing hazard of bank-refresh technique is simply prevented by our novel command scheduler in DRAM controllers. Our experiments on various recent DNNs show that DRAMA can improve the training system performance by 10.4% and save 23.77% DRAM energy compared to the conventional architecture.
ReinforceWalk: Learning to Walk in Graph with Monte Carlo Tree Search

Learning to walk over a graph towards a target node for a given input query and a source node is an important problem in applications such as knowledge graph reasoning. It can be formulated as a reinforcement learning (RL) problem that has a known state transition model, but with partial observability and sparse reward. To overcome these challenges, we develop a graph walking agent called ReinforceWalk, which consists of a deep recurrent neural network (RNN) and a Monte Carlo Tree Search (MCTS). To address partial observability, the RNN encodes the history of observations and map it into the Q-value, the policy and the state value. In order to effectively train the agent from sparse reward, we combine MCTS with the RNN policy to generate trajectories with more positive rewards. From these trajectories, we update the network in an off-policy manner using Q-learning and improves the RNN policy. Our proposed RL algorithm repeatedly applies this policy improvement step to learn the entire model. At testing stage, the MCTS is also combined with the RNN to predict the target node with higher accuracy. Experiment results on several graph-walking benchmarks show that we are able to learn better policies from less number of rollouts compared to other baseline methods, which are mainly based on policy gradient method.
Alterations of Brain Functional Networks in Older Adults: A Resting-state fMRI Study Using Graph Theory

Aging is the fundamental of neurodegeneration and dementia, affecting every organ in the body. With the aggravation of global aging, more and more research is focusing on how brain changes in older adults. This study aimed to uncover the differences in brain functional networks from the perspective of graph theory between young individuals and older individuals. Here, 61 individuals in their 20s and 94 cognitively healthy old individuals in their 70s underwent a resting-state functional magnetic resonance imaging scan. Based on the graph theory method, a functional network was constructed for each participant. Our results revealed that brain functional networks in older adults maintained small-world properties. However other nodal parameters including degree centrality, betweenness centrality, shortest path lengths, local efficiency, nodal efficiency, and cluster coefficients showed significant differences in many nodes (brain regions) between the 2 groups. Moreover, we correlated these nodal parameters with age, exploring 8 brain regions significantly affected with age. 7 out of 8 brain regions including the bilateral superior parietal lobule, bilateral precuneus, right middle cingulate, right inferior parietal lobule and right transverse temporal gyri were distributed in the default mode network. Our findings, based on graph theory, provided evidence for the alteration of the default mode network in older adults from the perspective of the functional network.
Losses Model of the WPT System with Single-Phase T-Type Inverter

This paper presents a losses model of potentially highly efficient solution for primary side of inductive power transfer on the basis of a single-phase T-type inverter. The article is devoted to the losses analysis under compensation condition in the basic elements of the circuit for wireless energy transfer. The influence of the inductor's basic geometrical parameters on the losses in the semiconductors and inductors for two power levels: 110W and 550W was investigated. System modeling was performed in PSIM Simulation tool, design and study of inductors in ANSYS EM Suite. It is established that increasing of the coil`s outer diameter and the wire diameter leads to decreasing of total losses and increasing the coupling coefficient and system efficiency.