Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Practical Detection of the Top-k Flows

Monitoring network traffic is an important building block for various management and security systems. In typical settings, the number of active flows in a network node is much larger than the number of available monitoring resources and there is no practical way to maintain per-flow state at the node. This situation gave rise to the recent interest in streaming algorithms where complex data structures are used to perform monitoring tasks like identifying the top-k flows using a constant amount of memory. However, these solutions require complicated per-packet operations, which are not feasible in current hardware or software network nodes.In this paper, we take a different approach to this problem and study the ability to perform monitoring tasks using efficient built-in counters available in current network devices. We show that by applying non-trivial control algorithms that change the filter assignments of these built-in counters at a fixed time interval, regardless of packet arrival rate, we can get accurate monitoring information. We provide an analytical study of the top-k flows problem and show, using extensive emulation over recent real traffic, that our algorithm can perform at least as well as the best-known streaming algorithms without using complex data structure or performing expensive per-packet operations.
Classification of Traffic Signs: The European Dataset

Classifying traffic signs is an indispensable task for autonomous driving systems. Depending on the country, traffic signs possess a wide variability in their visual appearance making it harder for classification systems to succeed. Either the classifier should be fine-tuned or a bigger collection of images should be used. In this paper, we introduce a real-world European dataset for traffic sign classification. The dataset is composed of traffic sings from six European countries: Belgium, Croatia, France, Germany, The Netherlands, and Sweden. It gathers publically available datasets and complements French traffic signs with images acquired in Belfort with the equipped university autonomous vehicle. It is composed of more than 80000 images divided in 164 classes that at the same time belong to four main categories following the Vienna Convention of Road Signs. We analyzed the intra variability of classes and compared the classification performance of five convolutional neural network architectures.
Effective utilization of voltage control capabilities of the grid assets under the impact of renewable distributed generation

In recent years, grid modernization has become vital to achieve a balance between energy economics and consumer comfort. As the cost of distributed energy resources (DERs) such as photovoltaics (PV) has declined, utility companies have become increasingly interested in incorporating these technologies into the smart grid. Even though the advantages from DER integration outweigh the challenges, its implementation is easier said than done. To overcome these issues, a grid apparatus with advanced monitoring and dynamic control capabilities must be efficiently commissioned. In turn, this enables reliable operation of a high-performance, economically viable power grid. This study utilizes an IEEE 34 bus system modified to integrate high penetration DERs alongside dynamically controllable voltage control devices (Eaton’s Voltage Regulator controller) to provide a broad understanding of the system reliability and resiliency. The three advanced apparatus operating control modes showcased are Sequential, Time-integrating and Voltage-averaging. In summary, this paper provides a comprehensive review of the commonly established voltage control modes and configurations in a voltage regulator as well as recommendations for enhancing performance of the system using various degrees of control.
A real-time surface EMG decomposition system for non-invasive human-machine interfaces

Real-time surface EMG decomposition, to extract neural activity of spinal motor neurons, provides a non-invasive solution for establishing direct interfaces with the central nervous system. In this paper, we present a real-time EMG decomposition system, validate it through both synthetic and experimental high-density surface EMG (HD-sEMG) data, and demonstrate the system in an upper-limb prosthetic control scenario. The proposed system achieves (in real-time) median decomposition accuracy comparable to offline methods (within 0.5 %) with minimal utilisation of computational resources (x20 faster compared to the literature).
Multi-Agent Consensus Design for Heterogeneous Energy Storage Devices with Droop Control in Smart Grids

This paper proposes a distributed control architecture for battery energy storage systems (BESSs) based on multi-agent system (MAS) framework. The active/reactive power sharing, the frequency/voltage, and the energy of BESSs are synchronized by exchanging local information with a few other neighboring BESSs. Two consensus algorithms namely leaderless and leader-follower are proposed. The proposed control architecture offers unique features. Firstly, the heterogeneous nature of BESSs is explicitly taken into account in BESS models and consensus designs, while it is usually ignored in the literature. Secondly, the proposed designs bring the plug-and-play capability to the smart grid system by operating in both islanded and grid-connected modes. Next, the nominal frequencies and nominal voltage magnitudes of BESSs are used in the consensus design instead of their frequencies and voltage magnitudes. This makes the proposed structure much easier to be implemented in the real power grids. Lastly, an additional control input is designed to synchronize the energy levels of BESSs directly, whereas the energy levels of BESSs are synchronized indirectly through their powers in other existing research. Time-domain simulations on a modified IEEE 57-bus power system are then carried out to validate the proposed control structure and the consensus designs.
Degradation of Surface Passivation and Bulk in p-type Monocrystalline Silicon Wafers at Elevated Temperature

The surface passivation quality and the bulk lifetime of boron-doped p-type Czochralski silicon wafers were studied in response to dark annealing at 175 °C, using <italic>in situ</italic> effective lifetime measurements. We investigated non-fired and fired silicon nitride (SiN<italic><sub>x</sub></italic>), aluminum oxide (AlO<italic><sub>x</sub></italic>) capped with SiN<italic><sub>x</sub></italic>, and thermally-grown silicon oxide (SiO<sub>2</sub>). Modulation in surface passivation quality and bulk lifetime was detected only in cases where hydrogen is assumed to be released into the silicon wafer from the dielectric (AlO<italic><sub>x</sub></italic>/SiN<italic><sub>x</sub></italic> stack and fired SiN<italic><sub>x</sub></italic> layer). Interestingly, the degradation of both the surface and the bulk were followed by a recovery. It is also interesting to note that the changes in the surface and the bulk seem to be related, as the surface degradation starts when the initial bulk degradation ends. This study indicates a possible involvement of hydrogen in both the degradation and the recovery processes. The evolution of the effective lifetime as a function of time is similar to that reported for carrier-induced degradation in multicrystalline wafers; however, occurring on a different time scale. Hence, these findings may also be valuable for investigation of other degradation mechanisms in different silicon materials.
Temperature Compensated Tactile Sensing Using MOSFET With P(VDF-TrFE)/BaTiO3 Capacitor as Extended Gate

This paper presents poly(vinylidene fluoride–trifluoroethylene)/barium titanate [P(VDF-TrFE)-BT] nanocomposite-based touch sensors tightly coupled with MOSFET devices in extended gate configuration. The P(VDF-TrFE)-BT nanocomposite exploits the distinct piezoelectric and pyroelectric properties of P(VDF-TrFE) polymer matrix and BT fillers to suppress the temperature response when force and temperature are varied simultaneously. The reasons for this unique feature have been established through structural and electrical characterization of nanocomposite. The proposed touch sensor was tested over a wide range of force/ pressure (0–4N)/(0–364 Pa) and temperature (26 °C–70 °C) with almost linear response. The sensitivity toward force/pressure and temperature sensor is 670 mV/N/7.36 mV/Pa and 15.34 mV/°C, respectively. With this modified touch sensing capability, the proposed sensors will open new direction for tactile sensing in robotic applications.
Enhancing the Browsing Cache Management in the Clavy Platform

Clavy is an experimental platform for the management of learning object repositories with dynamically reconfigurable structures. Clavy does not use a concrete metadata schema for repositories but allows repository authors to define the schema that best adapts to each repository. Clavy also promotes an iterative and incremental definition of the schemata, allowing repository authors to reconfigure them throughout their life cycle. So, Clavy integrates information management strategies capable of dealing with this constant change. In this sense, a critical component of Clavy is the browsing system, which allows the user of a repository to carry out the navigation guided by the structure of the schema. One of the strategies used in Clavy to support navigation is the combination of inverted indexes with a browsing cache. However, although this strategy adapts to the most common changes in the schemata it does not adapt to changes in the contents of the repositories (that is, the addition, modification or deletion of learning objects). This paper proposes an enhancement to solve this limitation, and evaluates the approach proposed in a concrete repository in the domain of archeology.
Fusion of Visual and Thermal Imagery for Illumination Invariant Face Recognition System

Visible light face recognition systems have been well researched and in controlled environments can reach excellent accuracy. Variation in lighting conditions results in performance degradation and illumination is the one of the major limitations in visible light face recognition systems. Using infrared facial images can provide a solution to this problem. Nearly invariant to changes in illumination, thermal IR imagery provides ability for recognition under all lighting conditions, including complete darkness. The system proposed in this paper takes advantages from both spectra and provides an effective algorithm for illumination invariant face recognition system.
Intelligently Controlled Flywheel Storage for Wind Power Smoothing

Fast acting energy storage like flywheel are used for power smoothing of stochastic sources thereby ensuring their controllable dispatch. The efficacy of the flywheel storage for intermittency mitigation of the stochastic sources depends on how efficiently it is controlled. A stable intelligent adaptive controller based on recurrent fuzzy neural network (RFNN) is proposed for an induction machine driven flywheel system. The proposed scheme being more stable, reliable and efficient than conventional control schemes in tracking rapid changes in the power setpoint, ensures secure steady state performance. Additionally, the proposed scheme is immune to system parameter variations, nonlinearities and changes in operating conditions. Nonlinear time-domain simulations considering actual wind speed data, demonstrate the effectiveness of the proposed scheme by comparing its response to a nonlinear control scheme based on quadratic lyapunov function.
Joint Independent Subspace Analysis: Uniqueness and Identifiability

This paper deals with the identifiability of joint independent subspace analysis (JISA). JISA is a recently-proposed framework that subsumes independent vector analysis (IVA) and independent subspace analysis (ISA). Each underlying mixture can be regarded as a dataset; therefore, JISA can be used for data fusion. In this paper, we assume that each dataset is an overdetermined mixture of several multivariate Gaussian processes, each of which has independent and identically distributed samples. This setup is not identifiable when each mixture is considered individually. Given these assumptions, JISA can be restated as coupled block diagonalization (CBD) of its correlation matrices. Hence, JISA identifiability is tantamount to CBD uniqueness. In this work, we provide necessary and sufficient conditions for uniqueness and identifiability of JISA and CBD. Our analysis is based on characterizing all the cases in which the Fisher information matrix is singular. We prove that non-identifiability may occur only due to pairs of underlying random processes with the same dimension. Our results provide further evidence that irreducibility has a central role in the uniqueness analysis of block-based decompositions. Our contribution extends previous results on the uniqueness and identifiability of ISA, IVA, coupled matrix and tensor decompositions. We provide examples to illustrate our results.
$\mathcal H_{\infty }$ Control for 2-D Markov Jump Systems in Roesser Model

This paper considers the problem of asynchronous <inline-formula><tex-math notation="LaTeX">$\mathcal H_{\infty }$</tex-math></inline-formula> control for two-dimensional (2-D) Markov jump systems. The underlying system is described based upon the Roesser model. Especially, the hidden Markov model is employed when dealing with the asynchronization between a controlled system and a controller, and the relation between them is constructed through a conditional probability matrix. Based on the Lyapunov function technique, the asymptotic mean square stability and <inline-formula><tex-math notation="LaTeX">$\mathcal H_{\infty }$</tex-math></inline-formula> noise attenuation performance are investigated for the closed-loop 2-D system. Moreover, the controller gain can be obtained by solving a convex optimization problem. An example is presented to show the effectiveness and potential of the proposed new design technique.
Impact of Channel Thickness on the Large-Signal Performance in InAlGaN/AlN/GaN HEMTs With an AlGaN Back Barrier

The impact of varying the GaN channel layer thickness (<inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}}$ </tex-math></inline-formula>) in InAlGaN/AlN/GaN HEMTs with C-doped AlGaN back barriers is investigated. <inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}}$ </tex-math></inline-formula> was 50, 100, and 150 nm, and the gate length of the fabricated HEMTs ranged from 50 to 200 nm. It is found that short-channel effects (SCEs) are significantly mitigated with a small <inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}}$ </tex-math></inline-formula>. For HEMTs with a gate length of 50 nm, the drain-induced barrier lowering changes from 40 to 93 mV/V as <inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}}$ </tex-math></inline-formula> is increased from 50 to 150 nm. On the other hand, it is shown that dispersive effects are more severe for a smaller <inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}}$ </tex-math></inline-formula>, as demonstrated by a sixfold increase in the dynamic ON-resistance for <inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}} = 50$ </tex-math></inline-formula> nm compared to <inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}} = 150$ </tex-math></inline-formula> nm. The tradeoff between dispersion and SCEs is reflected in large-signal measurements at 30 GHz. The 50-nm channel, mainly limited by dispersion, exhibits an output power of 3.5 W/mm. The thicker channels reach a maximum of around 5 W/mm, but for different gate lengths due to the difference in severity of the SCEs. This paper elucidates the interplay between SCEs and dispersion related to <inline-formula> <tex-math notation="LaTeX">${t}_{\text {ch}}$ </tex-math></inline-formula>, its consequences for the large-signal performance and for the limitation in downscaling of the gate length.
Triggering BESS Inertial Response with Synchronous Machine Measurements

Synthetic inertia devices must deliver power during maximum RoCoF, which is observed to occur during the first 500 ms post fault. The use of a droop characteristic with a deadband is demonstrated to be unfit for purpose. The ideal response (with present technologies) is demonstrated to be a maximum power ramp in minimum time post fault. This paper demonstrates a method of rapidly detecting and categorizing under frequency events by monitoring synchronous machines. PMU and point on wave data, sampled at 6.4 kHz, from a power station and a laboratory set up, under transient events, is presented. A rapid inertial power ramp signals the onset of the events. Sub-cycle voltage and current variations are demonstrated as a potential method for reliably detecting frequency transients and triggering a synthetic inertia response.
Evaluation of Mental Stress in Automated Following Driving

Automated driving and auxiliary driving functions are expected to improve safety and efficiency, and reduce the burden on drivers. These driving assist functions change the task of the driver not to control the vehicle but to monitor the driving operation, which could lead to new kind of stress. In this paper, the driver stress during automated follow-up driving is evaluated based on subjective evaluation and physiological measurement in a driving simulator environment. We confirmed that the subjective anxiety change due to acceleration / deceleration intensity varies depending on the inter-vehicle distance.
Microstrip Line-Based Glucose Sensor for Noninvasive Continuous Monitoring Using the Main Field for Sensing and Multivariable Crosschecking

Microstrip line (MLIN)-based glucose sensors have been used for noninvasive glucose monitoring. However, the current MLIN-based solutions do not provide enough sensitivity. The reason for this is that they rely on the fringing field of an MLIN that leads to a shallow penetration depth and thus low sensitivity. In this paper, we propose a sensitive MLIN-based glucose sensor using the main field. The idea is illustrated using the following three steps. Step 1 is sensing the glucose level by using the main field, which uses the material under test (MUT) as the substrate of an MLIN terminated with a load; Step 2 is identifying the contribution of different parameters and different components of parameters to sensitivity, which leads to Step 3, in which different parameters and/or different components of parameters are crosschecked for sensing. Two frequency bands, 100 to 500 MHz and 1.4 to 1.9 GHz, were investigated. In Step 1, the main field configuration was compared with its fringing field counterpart. It is shown that the proposed MLIN configuration presents an average sensitivity of $1.8\times 10^{-3}$ dB/(mg/dL) in terms of the reflection coefficient that is more than 10 times higher than that of its counterpart. In Step 2, different components of the reflection coefficient and the input impedance of the proposed configuration were examined, and their diverse contributions to the sensitivity were shown, which allows crosschecking to improve sensing accuracy. This was successfully shown in Step 3, in which algorithms for crosschecking were proposed, and the sensing accuracy was examined. In terms of the optimal sensing frequency band, 1.4 to 1.9 GHz shows higher sensitivity than 100 to 500 MHz, where the molecules may interact the most with the waves. This implies that the interaction between a wave and the sensing structure with the MUT contributes more to sensitivity than the direct interaction between the wave and the MUT. The radiofrequency safety of the proposed configuration was examined by checking the specific absorption rate of the MUT with the proposed structure. This paper offers a new avenue for the development of an MLIN-based noninvasive glucose sensor for continuous monitoring with high sensitivity in the near future. It can be used both as a single sensor and in a multiple-sensor glucose monitoring system.
Real-time Algorithms for Facial Emotion Recognition: A Comparison of Different Approaches

Emotion recognition has application in various fields such as medicine (rehabilitation, therapy, counseling, etc.), e-learning, entertainment, emotion monitoring, marketing, law. Different algorithms for emotion recognition include feature extraction and classification based on physiological signals, facial expressions, body movements. In this paper, we present a comparison of five different approaches for real-time emotion recognition of four basic emotions (happiness, sadness, anger and fear) from facial images. We have compared three deep-learning approaches based on convolutional neural networks (CNN) and two conventional approaches for classification of Histogram of Oriented Gradients (HOG) features: 1) AlexNet CNN, 2) commercial Affdex CNN solution, 3) custom made FER-CNN, 4) Support Vector Machine (SVM) of HOG features, 5) Multilayer Perceptron (MLP) artificial neural network of HOG features. The result of real-time testing of five different algorithms on the group of eight volunteers is presented.
Silicon Diode Uncooled FPA With Three-Dimensional Integrated CMOS Readout Circuits

This paper reports the design, fabrication, and test of a 3-D integrated uncooled focal plane array (FPA) using monocrystalline silicon diodes as thermosensitive devices. The diode array is fabricated from the silicon device layer of a silicon-on-insulator wafer, and the readout integrated circuits (ROICs) are fabricated on a bulk wafer using the CMOS technology. The silicon diode array is vertically integrated with the ROIC using the 3-D integration technology. Electroless nickel (Ni) plating is developed for fabricating substantial Ni posts to mechanically support the diode pixels to suspension and electrically connect the diode pixels to the ROIC. It allows the integration of monocrystalline device arrays with CMOS circuits fabricated using separate technologies and wafers in vertically suspended configuration. It also improves the filling factors of the FPA chips, shortens the wires between the diodes and the ROIC, and facilitates the releasing process to suspend the diode array. A $160 \times 120$ small-scale FPA has been developed as a test vehicle for concept verification. The test results and successful thermal imaging demonstrate the feasibility of the 3-D integration technology and verify the application of 3-D integration in the development of integrated FPAs using monocrystalline thermosensitive devices.
Watt-Level Nanosecond 4.42- $\mu$ m Raman Laser Based on Silica Fiber

We report the demonstration of a 4.42-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> silica-fiber-based Raman laser delivering a record average output power of 1.4 W and operating in a single transversal mode. The Raman laser is based on a hydrogen-filled hollow-core silica fiber, which is pumped by a nanosecond erbium-doped fiber laser at <inline-formula> <tex-math notation="LaTeX">$1.56~\mu \text{m}$ </tex-math></inline-formula>. In spite of a large quantum defect of a 1.56–4.42-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> conversion, the Raman laser demonstrates no heat-initiated problems. Due to the implementation of a linearly polarized pump and virtually all-polarization-maintaining configuration of the Raman laser, the quantum efficiency has reached 53%, which is a theoretical limit imposed by optical losses (1.1 dB/m) at 4.42 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> in a state-of-the-art revolver hollow-core silica fiber. The developed laser demonstrates the prospects of gas fiber Raman lasers to serve as pump sources for high-power supercontinuum generation in the mid-infrared spectral range. Such sources can find numerous scientific and biomedical applications.