Miracle-dz/UniFAR-FTUs huggingface dataset:
Critical Offset Optimizations for Overlapping-Based Time-Triggered Windows in Time-Sensitive Network

Deterministic and low latency communications are increasingly becoming essential requirements for several safety-critical applications, such as automotive and automation industries. Time-sensitive networking (TSN) is an element of the new IEEE 802.1 standards that introduced Ethernet-based amendments to support these applications. One of these enhancements was presented in IEEE 802.1Qbv to define the time-aware shaping (TAS) technique for time-triggered (TT) traffic scheduling. The TAS mechanism is window-based scheduling using a gating system controlled by the gate control list (GCL) schedules in all nodes. Although several scheduling algorithms have been proposed to investigate the effects of window-related parameters on network performance, the offset difference (<inline-formula> <tex-math notation="LaTeX">$OD$ </tex-math></inline-formula>) between the same-class windows in the adjoining nodes has not been optimized yet. These optimizations are extremely crucial to implement less pessimistic latency schedules. This paper proposes an optimized flexible window-overlapping scheduling (OFWOS) algorithm that optimizes TT window offsets based on latency evaluations considering the overlapping between different priority windows at the same node. First, we formulate the GCL timings as mathematical forms under variable <inline-formula> <tex-math notation="LaTeX">$OD\text{s}$ </tex-math></inline-formula> between the same-priority windows. Then, an analytical model is implemented using the network calculus (NC) approach to express the worst-case end-to-end delay (<inline-formula> <tex-math notation="LaTeX">$WCD$ </tex-math></inline-formula>) for TT flows and evaluated using a realistic vehicular use case. The OFWOS model optimizes <inline-formula> <tex-math notation="LaTeX">$OD$ </tex-math></inline-formula> under all overlapping situations between TT windows at the same node. Compared with the latest works of 3-hop and 30-hop TSN connections, the OFWOS reduces the <inline-formula> <tex-math notation="LaTeX">$WCD$ </tex-math></inline-formula> bounds by 8.4% and 32.6%, respectively, accomplishing less pessimistic end-to-end latency bounds.
Assessment of Wind Power Variability in Ethiopian Power System

Due to the intermittent and variable nature of wind, grid integration of wind power is a big challenge in power system operation. Studies that evaluate and identify the impact of wind power on power system operation and mitigation techniques is indispensable. This paper has evaluated the hourly variability of hourly wind power production in Ethiopian wind farms. The evaluation has identified the maximum up and down variation of the production. The duration that the hourly step change is greater than 5% and less than −5% to see the duration of calm time also studied. The result show that the extreme maximum up and down hourly wind power production variation is observed in Adama I which is 100% of rated capacity. This is because of the small installed capacity compared to the aggregated production which has the maximum up and down hourly wind power production of 53.40% and −65.7% respectively and this shows that wind power production in Ethiopia is highly ramping up and down. From the simulation result it was clearly observed that the correlation between wind farms is reduced as the distance between them increases. Not only this but also the paper has also shown that the probability of low production is observed during autumn season and probability of high production is observed during spring and winter season. This challenge increases if the penetration level of wind increases. Thus, the authors recommend that before grid integration of wind power, it is necessary to evaluate its impact on grid operation.
High-Performance Data Analytics Techniques for Power Markets Simulation

Efficient energy trading relies on high-fidelity price prediction systems with short response times that enable producers, consumers, and traders to make informed decisions in real time. Electricity networks are typically modeled using linear programming (LP), with the uncertainty in supply and demand being represented by hundreds of thousands of different input parameters. Processing large sets of probabilistic scenarios in near real-time is imperative to effective trading. High-throughput scheduling techniques of the individual LPs provide a promising and often overlooked opportunity to reduce processing time. This work analyzes high-throughput computational techniques in the context of European electricity markets. An LP model of the electricity price that considers different load scenarios, marginal cost functions, and weather patterns is introduced and analyzed from the perspective of computational resource requirements for massively parallel simulations. We present numerical experiments that consider multiple aspects of the computational pipeline from the choice of the LP solution algorithm and its impact on memory efficiency, to the scheduling techniques that allocate individual jobs to computational resources. Results show that an optimized high-throughput computation strategy can reduce response times by 63% over a naive strategy in a real-world trading scenario.
Audio Information Retrieval and Musical Acoustics

Information retrieval and machine learning have grown to the point of playing a leading role in all aspects of sound analysis. There are some research areas, however, in which the potential of information retrieval techniques is only now beginning to have an impact on the research community. One of these areas is musical acoustics. In this area, in fact, machine intelligence and information retrieval have been widely used only for timbral analysis of the tone produced by musical instruments, thus effectively objectifying an analysis that is traditionally considered subjective. The role of information retrieval and machine learning in the analysis of vibrational and acoustic properties of musical instruments is a more recent development. In this manuscript, we offer an overview on methodologies for vibrational, acoustic and timbral analysis, based on machine intelligence. We discuss some challenges that are emerging today and will have to be faced in the near future in order to make information retrieval and machine learning become an integral part of the analysis process in musical acoustics.
Towards a Green Approach for Minimizing Carbon Emissions in Fog-Cloud Architecture

Fog computing is developed to complement cloud computing by extending the cloud services (computing, storage, networking, and management) to the edge of the network in order to reduce service latency. Correspondingly, the incremental use of cloud/fog resources and their applications has increased energy consumption and carbon emissions (CO2) of the data centers, which caused significant environmental challenges. Optimizing the placement of the requested resources and applications (e.g., in the form of virtual machines (VMs)) is one of the main solutions, which has a primary effect in reducing the energy consumption of cloud/fog architectures and consequently their CO2 emissions. However, due to the geographic distribution of cloud and fog data centers, there are varying levels of CO2 emissions to consider, which makes optimizing the placement of resources and applications in distributed cloud/fog more challenging than in centralized clouds in terms of carbon efficiency. In this paper, we propose a multi-level approach using a mixed-integer linear programming (MILP) model to minimize the CO2 emissions of data centers by optimizing the resources usage and the placement of VMs in fog-cloud environments. This approach calculates the CO2 emissions of the British Telecom (BT) network based on the carbon intensity data from the National Grid ESO, considering several scenarios of traffic demand during different times of the day and year. The results show that the optimal location to host applications highly relies on the carbon intensity and traffic demands. The results also show there is a trade-off between CO2 emission reduced by shortening network journey, and CO2 emission increased by hosting more applications into the fog nodes. In addition, the results demonstrate that the proposed green fog-cloud architecture outperforms the central cloud and the distributed clouds in terms of reducing the total CO2 emission by up to 91% and 71%, respectively. Finally, we develop a heuristic algorithm to mimic and validate the presented work, and it shows comparable results to the MILP model.
The Role of Battery Storage in PV-Diesel Microgrid Simulation-Based Analysis

This work aims to contribute to the ongoing research in the electrification solutions offered to the regions which are severely affected by power outages worldwide. On the one hand, it presents the modeling and simulation of a grid-connected PV-Diesel Microgrid. On the other hand, it demonstrates, based on a system analysis, the potential of fuel-saving by introducing a battery storage system that allows for peak shaving and reducing the required generation capacity of the diesel generator. The main components of the systems are firstly modeled in MATLAB, namely a PV plant, a stationary battery storage system (BSS) of a generic capacity, and a diesel generator. Furthermore, a load profile of an institutional building in Gaza-strip, as a representative use-case, is analyzed to highlight the power and energy requirements of the system. Three system configurations are simulated, namely: a diesel Genset only, which represents the baseline configuration, a diesel Genset assisted with a PV plant, and lastly a hybrid PV-Battery-Diesel configuration, where the BSS is used to handle the occasional peak loads. The simulation results give clear insights to decision-makers and stakeholders about the operating costs associated with each configuration as well as the expected CO2 emissions.
Evaluation of Linear Component Node Orchestration for IoT Clusters in Smart Grid Communication Networks

Linear Component node orchestration (LCNO) is a novel paradigm that focuses on the use of smart routing procedures to derive random connectivity among nodes in a Smart grid communication network (SGCN). It eliminates the limitations in distributed communication involving Machine to Machine interactions among edge nodes in the substations. To achieve the aims; first, the edge layer for data stream parameter collection (from both homogenous and heterogeneous sources) is automated. Second, homogeneous linear-like Internet of Things (IoT) clusters are introduced for modular deployment at the distribution network. Third, interface protection monitoring for linear-chain topology in SGCN is deployed. To study the effect of the LCNO for system resilience, a simple routing protocol (SRP) is compared with Power-Efficient Gathering in Sensor Information System (PEGASIS) and Low Energy Adaptive Clustering Hierarchy (LEACH). Quality of service (QoS) results show that SRP is more efficient for monitoring the frequency and voltage parameters.
Price dynamics for Net electric energy metering on a distribution network: Ising's model approach

The increased integration of decentralized renewable energies in distribution networks has brought different challenges in the analysis and planning of electrical networks. The network manager must have appropriate tools allowing rapid decision-making and fair invoicing of consumers or remuneration of producers. In this article, we introduce a novel approach based on the famous Ising's model in order to model the variation in the price of reactive energy circulating on a electric distribution network according to local interactions between subscribers but also according to the general condition of the network. A simulation on the CIGRE Test network allowed us to quantify the benefit that a network manager would have using the proposed billing strategy.
Image Quality Assessment on Identity Documents

This paper developed a method for performing Image Quality Assessment (IQA) on ID-card images. First, we built the dataset, consisting of 204 images from Chilean ID cards, containing real and tampered images with varying quality levels. Then, we evaluated different features, obtaining the best results using the BRISQUE features and a newly trained SVR, with an R2 score of 0.5868. This proposed method is called BRISQUE-ID. The IQA on ID cards can be used as a pre-processing stage for discarding lousy quality images and helping the subsequent steps in the processing pipeline.
Scalable Fully Pipelined Hardware Architecture for In-Network Aggregated AllReduce Communication

The Ring-AllReduce framework is currently the most popular solution to deploy industry-level distributed machine learning tasks. However, only about half of the maximum bandwidth can be achieved in the optimal condition. In recent years, several in-network aggregation frameworks have been proposed to overcome the drawback, but limited hardware information have been disclosed. In this paper, we propose a scalable fully-pipelined architecture that handles tasks like forwarding, aggregation and retransmission with no bandwidth loss. The architecture is implemented on a Xilinx Ultrascale FPGA that connects to 8 working servers with 10 Gb/s network adapters, and it is able to scale to more complicated scenarios involving more workers. Compared with Ring-AllReduce, using AllReduce-Switch improves the efficient bandwidth of AllReduce communication with a ratio of <inline-formula> <tex-math notation="LaTeX">$1.75\times $ </tex-math></inline-formula>. In image training tasks, the proposed hardware architecture helps to achieve up to <inline-formula> <tex-math notation="LaTeX">$1.67\times $ </tex-math></inline-formula> speedup to the training process. For computing-intensive models, the speedup from communication may be partially hidden by computing. In particular, for ResNet-50, AllReduce-Switch improves the training process with MPI and NCCL by <inline-formula> <tex-math notation="LaTeX">$1.30\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.04\times $ </tex-math></inline-formula> respectively.
Assessment of Sensor Ageing-Impact in Air Travelled Fingerprint Capturing Devices

Biometric recognition performance is affected by many factors, like varying acquisition conditions or ageing related effects, commonly denoted as biometric template ageing. Image sensor ageing, being part of biometric template ageing and a subfield of image and video forensics, leads to defective pixels due to cosmic radiation, depending on the altitude. So far, image sensor ageing has only been a peripheral target in fingerprint research. We investigate the impact of image sensor ageing on various fingerprint capturing devices, including optical, capacitive and thermal ones. We established a fingerprint ageing dataset utilising 10 capturing devices which travelled on an air-plane for 127 days (to increase the number of developed defects). By evaluating the samples captured prior to their travel and afterwards using several state-of-the-art fingerprint quality metrics as well as minutiae-based fingerprint recognition systems we quantify the effect of image sensor ageing on fingerprint recognition. Furthermore, by employing a defect detection technique we quantify the number of defects developed during that period.
Detection of Anomalies in Household Appliances from Disaggregated Load Consumption

The detection of anomalous power consumption in household appliances plays a key role for the optimization of grid operations and for reducing unwanted electrical absorptions in residential buildings. Smart Plugs, Smart Appliances and other appliance-level monitoring devices allow to continuously monitor the power consumption of individual appliances present in the house. This work is aimed at detecting electrical anomalies in household appliances by analyzing the disaggregated load consumption derived from appliance-level monitoring devices. For this purpose, we implemented an anomaly detection framework which monitors the hourly energy consumption of three common sources of power absorption: the baseline, the fridge and the electrical devices. Here, we focused our analysis on two kinds of anomalies: single-point deviations and anomalous trends. The analysis of single-point deviations allowed us to identify short-term power peaks due either to unexpected electrical faults or sudden variations in end-users routines. The analysis of anomalous trends revealed several cases in which the end-users gradually increased their ordinary power consumption profile towards more energy-intensive practices. In summary, the results of our work showed that the power consumption derived from appliance-level load monitoring can be used to detect several anomalous power consumption in household appliances.
An Embedded Three-Bit-Per-Cell Two-Transistors and One-Ferroelectric-Capacitance Nonvolatile Memory

We present a nonvolatile memory of two transistors and one ferroelectric-capacitance with the three-bit-per-cell capability. With a control transistor, the memory can be turned-off correctly when the ferroelectric capacitance is programmed such that the threshold-voltage of the unit cell shifts to a deep negative value. This unit cell can be programmed in 80 ns and erased in 20 ns. The conductance can be tuned gradually in <inline-formula> <tex-math notation="LaTeX">$2.5\times 10^{{4}}$ </tex-math></inline-formula> folds of the on-off ratio with 30 cycles by pulse operations. The eight conductance values can be cycled more than <inline-formula> <tex-math notation="LaTeX">$10^{{7}}$ </tex-math></inline-formula> times. Data stored in distinguished eight conductance values can be retained in 80 Celsius degrees for ten years. This work offers an applicable solution for embedded non-volatile memories with ferroelectric mechanisms.
Distributed Sensing with Low-Cost Mobile Sensors Toward a Sustainable IoT

Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of the COVID.19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT.
Voltage control of microgrids interconnected

Load demands variations, renewable sources integration and grid interconnections of areas constituted new challenges that facing the new power grids for its stability, reliability and for uninterrupted operations. In this paper a voltage regulation is proposed for three microgrids interconnected. The bus voltages of three microgrids are used as an indicator for the bidirectional power flow monitoring of the balance between the production and consumption. An optimal control strategy is presented to control the interconnected system by maintaining the fluctuation of their bus voltage magnitude within a permitted range. The efficiency of the presented model is proven by the results obtained after simulations.
Cycling Induced Metastable Degradation in GeSe Ovonic Threshold Switching Selector

Ovonic threshold switching (OTS) selector is a promising candidate to suppress the sneak current paths in emerging memory arrays, but there is still a gap between its performance and the rigorous requirement from memory devices, especially its endurance improvement is hindered by insufficient understanding of the mechanism. In this work, cycling induced degradation of GeSe-based OTS selectors is studied with electrical characterization techniques. The existence of metastable state between the on- and off-state during cycling is observed and statistically analyzed alongside with the gradual off-state leakage current increase. Such metastable degradation may be attributed to the generation of unstable Ge-Ge bonds that might be induced by element segregation, which is also responsible for the higher off-state leakage current in GeSe selectors after large cycling or with higher Ge component. This work provides experimental guidance for optimizing OTS selectors.
Mathematical and Circuit Modeling of the Human Cardiovascular and Ventilatory Kinetic Response in Exercise

In this paper, we investigate a non-invasive procedure for static and dynamic modeling of cardiac output and oxygen consumption in response to cardiopulmonary exercise during exercise. The results are presented on 2 athletes - a football player and cross-country runners who have undergone 10 stress tests for 5 years. The results can also be used to model some other physiological parameters. Measurements were performed in laboratory conditions that allowed measurable exercise settings and well-measurable load responses, such as heart rate, ventilation, and oxygen consumption.
Influence of Test Protocols on Biometric Recognition Performance Estimation

The performance of a biometric system is commonly evaluated by the obtained recognition rates and comparing the results against the ones reported in the literature on the same database. An aspect that has not received the deserved attention in the literature concerns the influence, on the achieved rates, of the test protocol employed to select the enrol and probe data. We provide a detailed analysis of the impact of the experimental choices on the estimated performance, considering the recom-mendations provided by ISO/IEC 19795 standard. We use the UTFVP finger vein database, reproducing results presented in the literature using multiple protocols. Our experiments highlight the possibility of obtaining equal error rates reduced by half simply by changing the test protocol.
Hierarchical Coordination of a Vehicle-to-Grid System to Improve Self-consumption in Building MicroGrids

Aiming to take full advantage of Electric Vehicles' (EVs) batteries despite unpredictability in their arrival and departure time, in this paper, a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in building microgrids is proposed. Besides providing a complete framework for its design, this paper also analyses different scenarios of EVs' batteries exploitation that would be beneficial for both building microgrids and EVs' owners. The simulation results conducted in MATLAB Simulink demonstrated that the proposed hierarchical controller could assure that most EVs are completely charged before their departure time while enabling building microgrids to increase their annual photovoltaic self-consumption rate by about 3 percent point compared to the uncontrolled strategy.