Miracle-dz/UniFAR-FTUs huggingface dataset:
Tradeoff between accuracy and computational cost of Euler and Runge Kutta ODE solvers for the Izhikevich spiking neuron model

The Izhikevich spiking neuron model is one of the most used in neural engineering and computational neuroscience. Due to its trade-off between physiological plausibility and computational efficiency it is being used also in embedded systems with constrained computational resources. Thus, it is crucial to find a compromise between computational cost and error while numerically integrating the equations of the model. This work aims at quantifying the error produced by fixed step Ordinary Differential Equation (ODE) solvers. Our focus is to provide design hints that could be useful for embedded neural engineering applications. We evaluated three types of input and three ODE solvers: Euler, Runge Kutta 2, Runge Kutta 4. First, we generated a dataset of spike trains to draw conclusions on their general behavior while varying the discretization step. Then we showed that, within a single non-interrupting spike train, the spike delay is positive and accumulates linearly with the spike count. Finally, we introduced a robust method to assess the discretization limits. This method exploits the Victor Purpura distance and confirms that the limits depend on the spike train duration. Our results lead the way to a robust and systematic investigation of the trade-off between computational cost and discretization accuracy of fixed step ODE solvers for neuronal models.
Near-Storage Acceleration of Database Query Processing with SmartSSDs

Smart solid-state drives (SmartSSDs) with onboard FPGAs are becoming mainstream, providing opportunities for near-storage computation, which is appealing for increasing the performance of data-intensive workloads such as database query processing. This paper demonstrates the performance and energy improvements by offloading the filter and aggregation operations to the FPGA on a SmartSSD with real-system experiments. We make the observation that efficiently handling null entries in the data is important. Hence, we propose a novel design to manage the metadata to handle null entries. Our real system evaluation shows that offloading the query operations to the FPGA results in 9.6× improvement in performance while consuming 10.9× less energy compared to a conventional CPU-only query processing.
Interactions Between Fe(III)-Oxides and Fe(III)-Phyllosilicates During Microbial Reduction 1: Synthetic Sediments

ABSTRACT Fe(III)-oxides and Fe(III)-bearing phyllosilicates are the two major iron sources utilized as electron acceptors by dissimilatory iron-reducing bacteria (DIRB) in anoxic soils and sediments. Although there have been many studies on microbial Fe(III)-oxide and Fe(III)-phyllosilicate reduction with both natural and specimen materials, no controlled experimental information is available on the interaction between these two phases when both are available for microbial reduction. In this study, the model DIRB Geobacter sulfurreducens was used to examine the pathways of Fe(III) reduction in Fe(III)-oxide stripped subsurface sediment that was coated with different amounts of synthetic high surface area (HSA) goethite. Cryogenic (12K) 57Fe Mössbauer spectroscopy was used to determine changes in the relative abundances of Fe(III)-oxide, Fe(III)-phyllosilicate, and phyllosilicate-associated Fe(II) [Fe(II)-phyllosilicate] in bioreduced samples. Analogous Mössbauer analyses were performed on samples from abiotic Fe(II) sorption experiments in which sediments were exposed to a quantity of exogenous soluble Fe(II) (FeCl2⋅2H2O) comparable to the amount of Fe(II) produced during microbial reduction. A Fe partitioning model was developed to analyze the fate of Fe(II) and assess the potential for abiotic Fe(II)-catalyzed reduction of Fe(III)-phyllosilicates. The microbial reduction experiments indicated that although reduction of Fe(III)-oxide accounted for virtually all of the observed bulk Fe(III) reduction activity, there was no significant abiotic electron transfer between oxide-derived Fe(II) and Fe(III)-phyllosilicatesilicates, with 26–87% of biogenic Fe(II) appearing as sorbed Fe(II) in the Fe(II)-phyllosilicate pool. In contrast, the abiotic Fe(II) sorption experiments showed that 41 and 24% of the added Fe(II) engaged in electron transfer to Fe(III)-phyllosilicate surfaces in synthetic goethite-coated and uncoated sediment. Differences in the rate of Fe(II) addition and system redox potential may account for the microbial and abiotic reaction systems. Our experiments provide new insight into pathways for Fe(III) reduction in mixed Fe(III)-oxide/Fe(III)-phyllosilicate assemblages, and provide key mechanistic insight for interpreting microbial reduction experiments and field data from complex natural soils and sediments.
AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Road traffic forecasting is crucial in Intelligent Transportation Systems (ITS). To achieve accurate results, it is necessary to model the dynamic nature and the complex non-linear dependencies governing traffic. The goal is particularly challenging when the prediction involves more than just one traffic variable. This paper proposes a novel multi-task learning model, called AST-MTL, to perform multi-horizon predictions of the traffic flow and speed at the road network scale. The strategy combines a multilayer fully-connected neural network (FNN) and a multi-head attention mechanism to learn related tasks while improving generalization performance. The model also includes the graph convolutional network (GCNs) and the gated recurrent unit network (GRUs) to extract the spatial and temporal features of traffic conditions. Our experiments employ new sets of GPS data, called OBU data, to perform traffic prediction in the freeway and urban contexts. The experimental results prove our model can effectively perform multi-horizon traffic forecasting for different types of roads and outperform state-of-the-art models.
Reliability-Driven Voltage Optimization for NCFET-based SRAM Memory Banks

Negative Capacitance Field-Effect Transistors (NCFET) are promising significant power reductions while maintaining performance due to their internal voltage amplification. However, the addition of the ferroelectric layer also introduces a higher gate capacitance, which has to be charged and discharged resulting in higher power consumption. This results in trade-offs when employing NC-FinFET with respect to the thickness of the ferroelectric layer and their operating voltage on power, performance and reliability in circuits. This design-space is currently not explored, as existing research focused on a transistor-to-transistor comparison to show the superiority of NC-FinFET at the same voltage. In this work, we evaluate NC-FinFET employment in a full SRAM memory array (including write driver, sense amplifier, pre-charging, etc.) to obtain circuit delay, read and hold power and reliability metrics. This work shows, that solely evaluating SRAM cells results in inaccurate delay and power estimations compared to a full SRAM array. We explore iso-voltage and iso-performance NC-FinFET operation. Additionally, we explore two new operation modes: operating NC-FinFET within the same overall power consumption (iso-power) and operating at the same noise margins (iso-reliability). This exploration shows, for the first time, how ferroelectric layer thickness plays a role on reliability as a 4 nm layer features a 47% loss compared to FinFET. Lastly, we obtain the activity of a register file in a processor simulator to obtain the ultimate impact on power and energy consumption of employing NC-FinFET in a microprocessor.
The Complex World of Oscillator Noise: Modern Approaches to Oscillator (Phase and Amplitude) Noise Analysis

The study of fluctuations in oscillators has been a classical research topic in mathematics, physics, and engineering since the first half of the 20th century [1]-[4]. Besides the intellectual fascination for mathematically difficult problems, the importance of the topic is deeply rooted in practical applications, mainly in the fields of RF and microwave electronics and also telecommunications. In fact, defining a precise frequency reference is fundamental for many applications, both electrical (e.g., transmitters and receivers) and optical (e.g., lasers). The broadening of the generated spectral line is mainly due to the phase-noise component of oscillator fluctuations, which consequently is the most commonly studied feature of oscillator noise (see [5] for a recent and exhaustive review). In a dual perspective, the definition of a precise time reference is also extremely important for digital applications, thus implying the necessity to keep under control the time jitter in clocked and in sampled systems. From a theoretical standpoint, phase noise and time jitter are simply two sides of the same coin, a manifestation of the oscillator? s noisy behavior. As the microwave engineer is more often interested in the phase-noise characterization, we discuss only the latter. The time-jitter estimation is discussed, for instance, in [6].
Machine Learning for Anomaly Detection: A Systematic Review

Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role in this area. In this research paper, we conduct a Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application. Our review analyzes the models from four perspectives; the applications of anomaly detection, ML techniques, performance metrics for ML models, and the classification of anomaly detection. In our review, we have identified 290 research articles, written from 2000-2020, that discuss ML techniques for anomaly detection. After analyzing the selected research articles, we present 43 different applications of anomaly detection found in the selected research articles. Moreover, we identify 29 distinct ML models used in the identification of anomalies. Finally, we present 22 different datasets that are applied in experiments on anomaly detection, as well as many other general datasets. In addition, we observe that unsupervised anomaly detection has been adopted by researchers more than other classification anomaly detection systems. Detection of anomalies using ML models is a promising area of research, and there are a lot of ML models that have been implemented by researchers. Therefore, we provide researchers with recommendations and guidelines based on this review.
Power Distribution System Synchrophasor Measurements With Non-Gaussian Noises: Real-World Data Testing and Analysis

This short paper investigates distribution-level synchrophasor measurement errors with online and offline tests, and mathematically and systematically identifies the actual distribution of the measurement errors through graphical and numerical analysis. It is observed that the measurement errors in both online and offline case studies follow a non-Gaussian distribution, instead of the traditionally assumed Gaussian distribution. It suggests the use of non-Gaussian models, such as Gaussian mixture models, for representing the measurement errors more accurately and realistically. The presented tests and analysis are helpful for the understanding of distribution-level measurement characteristics, and for the modeling and simulation of distribution system applications, such as state estimation.
Brain Functional Connectivity Dynamics in Autism in the Context of Restrictive, Repetitive and Stereotyped Behaviors

We investigate potential underlying differences in the functional connectivity (FC) profiles of individuals with autism spectrum disorder (ASD) who have severe issues with restrictive, repetitive and stereotypical patterns of behaviour (RRBs), those who have only mild issues, and controls. The focus is on the time-frequency dynamics of FCs that exhibit significant inter-group differences in strength. We introduce a novel measure of coherence volatility, and one of randomness based on 2D entropy over the time-frequency plane. These, together with a raw coherence measure, are computed over distinct frequency bands in wavelet-transform coherence maps. The raw coherence measure inversely correlates with severity of RRBs for multiple FCs which involve the frontal lobe. Volatility positively correlates with RRB severity for a number of FCs, suggesting more frequent attention shifts in ASD, but negatively in one case. Raw coherence suggests ASD groups with severe and mild RRBs share characteristics that distinguish them from controls, while the analyses of volatility, entropy, and phase support the notion of distinct profiles for severe and mild RRBs. Overall findings support the concept of broad heterogeneity in ASD.
Security Hardening of Botnet Detectors Using Generative Adversarial Networks

Machine learning (ML) based botnet detectors are no exception to traditional ML models when it comes to adversarial evasion attacks. The datasets used to train these models have also scarcity and imbalance issues. We propose a new technique named Botshot, based on generative adversarial networks (GANs) for addressing these issues and proactively making botnet detectors aware of adversarial evasions. Botshot is cost-effective as compared to the network emulation for botnet traffic data generation rendering the dedicated hardware resources unnecessary. First, we use the extended set of network flow and time-based features for three publicly available botnet datasets. Second, we utilize two GANs (vanilla, conditional) for generating realistic botnet traffic. We evaluate the generator performance using classifier two-sample test (C2ST) with 10-fold 70-30 train-test split and propose the use of ’recall’ in contrast to ’accuracy’ for proactively learning adversarial evasions. We then augment the train set with the generated data and test using the unchanged test set. Last, we compare our results with benchmark oversampling methods with augmentation of additional botnet traffic data in terms of average accuracy, precision, recall and F1 score over six different ML classifiers. The empirical results demonstrate the effectiveness of the GAN-based oversampling for learning in advance the adversarial evasion attacks on botnet detectors.
Improving SSVEP Identification Accuracy via Generalized Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a widely used method for the identification of steady-state visual evoked potential (SSVEP). Many studies dedicated to improving the performance of CCA via incorporating individual training data, whereas its variants are still not comparable to other state-of-the-art subject-specific training methods such as task-related component analysis (TRCA). This study employs a generalized canonical correlation analysis (GCCA) method that simultaneously maximizes the correlation among three sets of data, i.e. the individual training data, the individual template, and the predefined sine-cosine signal for improving SSVEP detection. The proposed GCCA-based method and its ensemble version (eGCCA) were validated on a 40-class SSVEP benchmark dataset. The study results showed that GCCA significantly outperformed TRCA and the improvement was more significant as training data decreased. Similarly, eGCCA achieved a higher performance than eTRCA and the improvement was significant with insufficient training data. Therefore, the proposed GCCA-based method is promising in the SSVEP-based brain-computer interfaces, owing to its advantages of high performance in target identification and robustness against less calibration.
Improving Movement-Related Cortical Potential Detection at the EEG Source Domain

Decoding movement-related cortical potential (MRCP) plays an important role in the Brain-computer interface (BCI) system. MRCP is not easy to be detected on sensors due to the volume conduction effect. This work combines the scout EEG source imaging (ESI) and Locality Preserving Projection (LPP) followed by a linear discriminant analysis (LDA) classifier to detect MRCP of motor imagery and execution. Seven healthy subjects participated in this study and performed cue-based ballistic dorsiflexion. Our results showed that the source domain-based method achieved a significantly higher true positive rate (TPR) than that obtained from the sensor domain in both motor imagery (MI) (76.65±4.26% vs. 70.3±5.4%) and motor execution (ME) (81.66±2.55% vs. 74.49±6.48%) tasks. The false-positive rate (FPR) calculated in the source or sensor space for MI and ME was (24.46±5.07% vs. 28.59±5.17%) and (22.52±3.35% vs. 25.99±7.37%), respectively. Therefore, we demonstrated that EEG signals obtained from the source domain could improve the MRCP detection rather than those in the sensor domain.
Rotor Pole Based Dual Stator Hybrid Excitation of Flux Switching Motor

This paper briefly presents Dual Stator Hybrid Excitation of Flux Switching Motor (DSHEFSM) having three rotor poles of various number and designs. Source of flux are limited stator, making the rotor fully passive which suits the highspeed applications. Detailed summary of no load and full load analysis is in the main section of DSHEFSM. DSHEFSM feasibility design experiments based on 2D- FEA is analyzed using the proposed machine is three phase behavior. Moreover, the coil test analysis, magnetic flux characteristic, cogging torque, induced voltage at open circuit condition, torque versus armature current, and instantaneous torque are analyzed.
A Secure Authentication and Key Exchange Protocol for M2M Communication

Machine-to-machine (M2M) communication is undergoing rapid growth as more devices are connected. In order to ensure secure communication between devices, certain security requirements apply to devices that are connected together. Authentication and key exchange are important in providing security for M2M communication. Mutual authentication is a security property that prevents man-in-the-middle and replay attacks via the network. Numerous research papers have introduced protocols for authentication and key exchange, but these lack effectiveness and efficiency in terms of security, and are not lightweight. This paper proposes a mutual authentication and key exchange protocol to protect against attacks on M2M communication. The proposed protocol is also shown to be more effective and efficient than alternative approaches with respect to security, and is lightweight. We also prove and analyze the completeness of the proposed protocol using the automated security protocol checking tools Scyther and AVISPA.
Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies

In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in Bangladesh) hybrid microgrid systems consisting of wind turbine, storage unit, solar PV, diesel generator and a load profile of 27.31 kW for five dispatch techniques: (i) generator order, (ii) cycle charging, (iii) load following, (iv) HOMER predictive dispatch and (v) combined dispatch strategy. The considered microgrids are optimized for the least CO2 gas emission, Net Present Cost, and Levelized Cost of Energy. The two microgrids are analyzed for the five dispatch techniques using HOMER software, and subsequently, the power system performance and feasibility study of the microgrids are performed in MATLAB Simulink. The results in this research provide a guideline to estimate different component sizes and probable costing for the optimal operation of the proposed microgrids under various load dispatch conditions. The simulation results suggest that ‘Load Following’ is the best dispatch strategy for the proposed microgrids having a stable power system response with the lowest net present cost, levelized cost of energy, operating cost, and CO2 emission rate. Additionally, the combined dispatch strategy is determined to be the worst dispatch technique for the proposed off-grid hybrid microgrid design having the maximum levelized cost of energy, net present cost, operating cost and CO2 emission.
Deep Real-Time Decoding of bimanual grip force from EEG & fNIRS

Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interfaces (BCIs). While non-invasive methodologies are safer, cheaper and vastly more accessible technologies, signals suffer from either poor resolution in the space domain (EEG) or the temporal domain (BOLD signal of functional Near Infrared Spectroscopy, fNIRS). The non-invasive BCI decoding of bimanual force generation and the continuous force signal has not been realised before and so we introduce an isometric grip force tracking task to evaluate the decoding. We find that combining EEG and fNIRS using deep neural networks works better than linear models to decode continuous grip force modulations produced by the left and the right hand. Our multi-modal deep learning decoder achieves 55.2 FVAF[%] in force reconstruction and improves the decoding performance by at least 15% over each individual modality. Our results show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging has immediate impact for rehabilitation, restoration and consumer applications.
Machine Learning–Based Mobility Robustness Optimization Under Dynamic Cellular Networks

In this paper, we propose a machine learning−based mobility robustness optimization algorithm to optimize handover parameters for seamless mobility under dynamic small-cell networks. Small cells can be arbitrarily deployed, portable, and turned on and off to fulfill wireless traffic demands or energy efficiency. As a result, the small-cell network topology dynamically varies challenging network optimization, especially handover optimization. Previous studies have only considered dynamics due to user mobility in a specific static network topology. To optimize handovers under dynamic network topologies, together with user mobility, we propose an algorithm consisting of two steps: topology adaptation and mobility adaptation. To adapt to a dynamic topology, the algorithm obtains prior knowledge, which presents a belief distribution of the optimal handover parameters, for the current network topology as coarse optimization. In the second step, the algorithm fine-tunes the handover parameters to adapt to user mobility based on reinforcement learning, which utilizes the knowledge obtained during the first step. Under a dynamic small-cell network, we showed that the proposed algorithm reduced adaptation time to 4.17% of the time needed by a comparative machine–based algorithm. Furthermore, the proposed algorithm improved the user satisfaction rate to 416.7% compared to the previous work.