Miracle-dz/UniFAR-FTUs huggingface dataset:
ANN-PSO Optimization of PV Systems Under Different Weather Conditions

Conventional maximum power point tracking (MPPT) techniques like Perturb& 0bserve perform ineffectively under partial shading condition due to its inability to effectively track the global maximum power point (GMPP). Particle swarm optimization (PSO) is a recent metaheuristic technique and has been used to extract maximum power from PV systems but takes time to iteratively locate the GMPP. This paper presents a novel hybrid ANN-PSO technique using series-connected distributed MPPT configuration. The simulation MPPT results using ANN- PSO configuration, PSO, and Perturb& observe (P& O) were compared with theoretical power values under different weather conditions to determine the most efficient MPPT method that can be considered for MPPT task in PV systems under uniform irradiance and partial shading conditions. Obtained results show that ANN-PSO can achieve the best performance.
EMPOWERING, a Smart Big Data Framework for Sustainable Electricity Suppliers

This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received about energy bills and offering online tools. The main contributions of EMPOWERING are the creation of a novel workflow in the electricity utility sector regarding the implementation of data analytics for their customers and the fast implementation of data-mining techniques in massive datasets within a Big Data platform to achieve scalability. The results obtained show that EMPOWERING can be of use for customers of electrical suppliers by changing their energy habits to decrease consumption and so increase environmental sustainability.
Enabling Online Robust Barcode-Based Visible Light Communication With Realtime Feedback

Barcode-based visible light communication (VLC) over screen-camera links has attracted great research interest recently due to its many desirable properties, including being free of charge, free of complex network configurations, and its well-controlled communication security. However, existing VLC systems over screen-camera links suffer from low-communication capacity and reliability, and a lack of transmission feedback from the receiver. In this paper, we design RainBar+, an online robust high-goodput color barcode-based VLC system with realtime feedback, to fully guarantee the communication reliability and exploit the communication capacity under a different link quality. To the best of our knowledge, this is the first system that provides a feasible feedback mechanism to enable the sender to retransmit the lost frames and optimize the barcode configurations. To this end, RainBar+ is designed from three orthogonal perspectives: optimizing the encoding capacity of each frame, fully utilizing the transmission capacity of the sender by solving the frame synchronization problem and adaptively adjusting the barcode configurations, and enabling the selective retransmission via realtime feedback with the speaker-microphone link. Extensive experiments with two commercial off-the-shelf smartphones show that RainBar+ outperforms existing systems and can support realtime high-goodput data communication in dynamic environments.
Hera Object Storage: A Seamless, Automated Multi-Tiering Solution on Top of OpenStack Swift

Over the last couple of decades, the demand for storage in the Cloud has grown exponentially. Distributed Cloud storage and object storage for the increasing share of unstructured data, are in high focus in both academic and industrial research activities. At the same time, efficient storage and the corresponding costs are often contrasting parameters raising a trade-off problem for any proposed solution. To this aim, classifying the data in terms of access probability became a hot topic. This paper introduces Hera Object Storage, a storage system built on top of OpenStack Swift that aims at selecting the most appropriate storage tier for any object to be stored. The goal of the multi-tiering storage we propose is to be automated and seamless, guaranteeing the required storage performance at the lowest possible cost. The paper discusses the design challenges, the proposed algorithmic solutions to the scope and, based on a prototype implementation it presents a basic proof-of-concept validation.
A Read-Out System for Disposable Paper-Based Sensors

This study presents design, implementation and test of a read-out system for disposable paper-based sensors. Proposed system basically consists of a Strathmore®300 series Bristol paper-based cantilever with the dimension of 1cm × 3.5cm and the thickness of roughly 400µm which is covered with a thin piezoresistive layer and a read-out circuitry that is composed of a Wheatstone Bridge circuit and instrumentation amplifier (INA128). Proposed read-out system is simulated, implemented and tested. The sensitivity of the proposed read-out system is measured as 0.25mV/µm for $\Delta V_{out}$ and 15mΩ/µm for $\Delta R_{PR}$.
Smart Space Concepts, Properties and Architectures

Smart spaces have been actively emerging recently, and researchers are working on developing and testing smart spaces in the real world. They facilitate smart applications that are adaptive to user preferences and contexts. In doing so they must satisfy applications’ dynamically changing resource needs. These objectives are achievable by cooperation among connected devices and ubiquitous interaction. Smart space architecture designs in the literature are mostly application specific, their concepts and components defined based on the specific needs of one application. In this paper, we formally define general smart space concepts and architectural models rigorously and discuss related architectural components (both hardware and software) in detail. Based on a literature review we summarize the discriminating properties that a smart space must possess, and its basic components and services to realize these properties. We present a comparative analysis of the architectural designs proposed thus far. A comprehensive smart space architecture is proposed and its semantic interoperability is discussed in detail. In addition, we provide a case study of a smart lighting system, where the properties of smart spaces are analyzed. Finally, we provide a roadmap for future smart space development.
Low-Power Approximate Unsigned Multipliers With Configurable Error Recovery

Approximate circuits have been considered for applications that can tolerate some loss of accuracy with improved performance and/or energy efficiency. Multipliers are key arithmetic circuits in many of these applications including digital signal processing (DSP). In this paper, a novel approximate multiplier with a low power consumption and a short critical path is proposed for high-performance DSP applications. This multiplier leverages a newly designed approximate adder that limits its carry propagation to the nearest neighbors for fast partial product accumulation. Different levels of accuracy can be achieved by using either OR gates or the proposed approximate adder in a configurable error recovery circuit. The approximate multipliers using these two error reduction strategies are referred to as AM1 and AM2, respectively. Both AM1 and AM2 have a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared with a Wallace multiplier optimized for speed, an $8\times 8$ AM1 using four most significant bits for error reduction shows a 60% reduction in delay (when optimized for delay) and a 42% reduction in power dissipation (when optimized for area). In a $16\times 16$ design, half of the least significant partial products are truncated for AM1 and AM2, which are thus denoted as TAM1 and TAM2, respectively. Compared with the Wallace multiplier, TAM1 and TAM2 save from 50% to 66% in power, when optimized for area. Compared with existing approximate multipliers, AM1, AM2, TAM1, and TAM2 show significant advantages in accuracy with a low power-delay product. AM2 has a better accuracy compared with AM1 but with a longer delay and higher power consumption. Image processing applications, including image sharpening and smoothing, are considered to show the quality of the approximate multipliers in error-tolerant applications. By utilizing an appropriate error recovery scheme, the proposed approximate multipliers achieve similar processing accuracy as exact multipliers, but with significant improvements in power.
Driver State Monitoring with Hierarchical Classification

While vehicle automation increases in the near future human drivers will still be responsible for monitoring of the driving environment or as a fallback for critical situations. Thus, systems for autonomy levels 2 to 3 require proper knowledge about the driver's state including, for example, the seating position, activity, or hands-on-steering-wheel status. We introduce an interior sensing system to classify the state of the driver inside a vehicle based on images of a single time-of-flight camera. We present an efficient and robust approach based on a hierarchical label structure and a cost function which focuses only on relevant labels inside this hierarchical label structure. Our approach reduces the number of output neurons while creating a fallback option inside the classifier simultaneously. This leads to improved classification results compared to common approaches. In our experiments we demonstrate a solution for combined assessment of seat occupancy, driving position, hands on steering wheel, and driver-object interaction.
The Role of Glial Cells and Synapse Loss in Mouse Models of Alzheimer’s Disease

Synapse loss has detrimental effects on cellular communication, leading to network disruptions within the central nervous system (CNS) such as in Alzheimer’s disease (AD). AD is characterized by a progressive decline of memory function, cognition, neuronal and synapse loss. The two main neuropathological hallmarks are amyloid-β (Aβ) plaques and neurofibrillary tangles. In the brain of AD patients and in mouse models of AD several morphological and functional changes, such as microgliosis and astrogliosis around Aβ plaques, as well as dendritic and synaptic alterations, are associated with these lesions. In this review article, we will summarize the current literature on synapse loss in mouse models of AD and discuss current and prospective treatments for AD.
Identification Method of Power Spectral Signal Based on Fourier Series Fitting and Density Clustering

A method for obtaining the specific usage of spectrum in the power spectrum sequence is proposed in this paper. For the obtained power spectrum, baseline noise is fitted by the Fourier series and then subtracted from the original power spectrum signal value firstly. Afterwards, power spectrum was converted into an image and is further processed by the image-filtering method. More specifically, the image was mean-filtered with the appropriate window function template, and the filtered image was converted into a power spectrum by valuing on the abscissa. Finally, DBSCAN clustering is used to the processed spectrum to identify the specific use of the signal power spectrum. In the experiment, the proposed method was compared with the classic energy detection method. The results showed that proposed method could well restore the proposed of spectrum resources.
Protective Effect of Anthocyanin on Neurovascular Unit in Cerebral Ischemia/Reperfusion Injury in Rats

Treating cerebral ischemia continues to be a clinical challenge. Studies have shown that the neurovascular unit (NVU), as the central structural basis, plays a key role in cerebral ischemia. Here, we report that anthocyanin, a safe and natural antioxidant, could inhibit apoptosis and inflammation to protect NVU in rats impaired by middle cerebral artery occlusion/reperfusion (MCAO/R). Administration of anthocyanin significantly reduced infarct volume and neurological scores in MCAO/R rats. Anthocyanin could also markedly ameliorate cerebral edema and reduce the concentration of Evans blue (EB) by inhibiting MMP-9. Moreover, anthocyanin alleviated apoptotic injury resulting from MCAO/R through the regulation of Bcl-2 family proteins. The levels of inflammation-related molecules including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6), which were over-expressed with MCAO/R, were decreased by anthocyanin. In addition, Nuclear factor-kappa B (NF-κB) and the NLRP3 inflammasome pathway might be involved in the anti-inflammatory effect of anthocyanin. In conclusion, anthocyanin could protect the NVU through multiple pathways, and play a protective role in cerebral ischemia/reperfusion injury.
VHF, UHF Voltage-Controlled Bandpass Filter Employing Tunable Active Inductor

In this paper, a voltage-controlled bandpass filter employing tunable active inductor for VHF and UHF applications is presented. In order to provide a systematic approach to the realization of tunable active filters with high selectivity, low power consumption and small chip area, several optimization techniques and reconfiguration methods are adopted in this design. Designed with 0. 13μm CMOS technology, the presented BPF prototype occupies a 0.00025 mm2area with pads, draws 2.34/3.45 mA from 1- V voltage supply for an operating center frequency from 145.2 MHz up to 436.2 MHz respectively, and demonstrates 1-dB compression points respectively of 1.23/2.67 dBm. For this tuning, the transmission loss are between −3.52 dB (UHF) and 14.77 dB (VHF) with a minimum noise figure varying respectively from 1.77 up to 1.82 dB.
An Extensive Comparison of Feature Extraction Methods for Paraphrase Detection

Paraphrase detection is one of the fundamental tasks in natural language processing. Designing a system to detect the paraphrase pairs requires a good understanding of different feature extraction methods. To tackle this challenge, lots of work have been done to extract various types of features. Knowing which types of features are discriminant for paraphrase identification, saves a lot of time for researchers and helps them obtain better result in their works. In this paper we compare various types of feature extraction methods that neither need any prior knowledge nor any external resources, so they can be used in every language. Our experiments show that those types of methods which specify the importance of each word in documents or break down the document into specific parts, have a better result compared to those methods that try to capture the meaning of a given document as a whole and treat the document as a single component.
Entropy-Based Feature Selection for Network Anomaly Detection

Over the decades, ensuring security of networks has been a major concern; the open-ended nature of networks though promoting its ease of use and connectivity has also contributed its security challenges. The growth in technology fosters the developments of varies gadgets but this growth has also been harnessed by intruders to develop new and more sophisticated ways of breaking into a network, hence the need for continual research in developing more sophisticated methods that can detect new day attacks in a various types of networks. Different algorithms have been used in building network security systems; however, in spite of their effectiveness, these algorithms also have different disadvantages that reduce its performance. A major way of improving the performance is through feature selection. This research focuses on how feature selection methods can be used to improve the performance of the minimum distance classifier using the UNB/IDS 2012 dataset. The performance of the classifier is optimized using two basic feature selection methods: entropy and variance, then a k-fold cross validation is performed to validate the accuracy results.
Accelerated Multi-Sensor Control for Selective Multi-Object Tracking

This paper focuses on selective multi-object tracking applications where objects with particular labels are of interest, and multiple sensors need to be controlled to achieve the optimum tracking performance for those objects. We formulate a novel solutions for a centralized multi-sensor multi-object system with labeled multi-Bernoulli filters in each sensor node. Our solution includes optimizing a closed-form objective function that can be directly calculated after prediction step in the central node of the sensor network. Importantly, our proposed cost function does not need to be computed after pseudo-update operations, hence a large amount of computation is saved. Simulation results indicate how the proposed methods can lead to significant improvements in terms of tracking accuracy of objects of interest, compared to using the generic (non-selective) sensor control methods. Numerical experiments involving a challenging multisensor target tracking application demonstrate that while our proposed method significantly outperforms the common (non-selective) sensor control methods, it performs similar to the state of art method for selective sensor control (selective-PEECS) in terms of the mean-square-error of tracking of the targets of interest. Despite similar performance in terms of tracking error, our method is significantly faster than the state of art (eight times faster in our experiments).
Arabic Speech Synthesis Based on HMM

This paper describes the synthesis of the Arab system based on Hidden Markov Models (HMM). HMMs have been successfully used in automatic speech recognition (ASR) from the 1970s, but recently they have been used for speech synthesis. Our synthesis system developed uses phonemes as HMM synthesis unit, Arabic database was developed for the first test. The main objective is to maintain the coherence of the consolidated text is interpreted by concatenating HMM phoneme. This method has several advantages. As it is parametric, it is possible to play on the HMM parameters change the characteristics of the voice producer. The developed model improves the quality of naturalness and intelligibility of speech synthesis in Arabic language environment.
Multistatic Doppler-Based Marine Ships Tracking

Multistatic Doppler radar measurements for multiple targets are typically corrupted by noise, missed detections and false alarms. In addition, when targets are close together, it becomes more difficult for the tracker to resolve tracks due to the highly nonlinear and low observability nature of the observation. This paper proposes a solution to the problem of tracking multiple marine ships from multistatic Doppler measurements. We use close form labeled multitarget Bayes filter, which can accommodate unknown and time-varying number of targets, clutter misdetection and association uncertainty. The efficiency of the proposed algorithm is illustrated via numerical simulation examples.
Frequency Control Reserve via Micro Grid for the Future Renewable Malaysian Power System

This paper investigates the technical features of frequency control reserve (FCR) potential based on multiple micro grids composition with distributed generators e.g. solar photovoltaic and wind turbine. The main problem is to manage the frequency excursions towards maintaining frequency stability without implementing emergency load control, transmission grid splitting and power plants disconnecting. The process of frequency containment reserve must be executed within seconds to minutes via grid's primary control regulation. Definitely, the response must be quick and has enough capacity to meet short and long term energy needs for the main grid. However the response of conventional generating unit during contingencies is progressively slow for supplying active power reserve especially for distance location but this can be facilitated with micro grids which are more dynamical in active power transfer hence provide FCR capability as well. In this investigation, a new model and controller for FCR is developed with using flat frequency control (FFC) and tie-line bias control (TBC) approach. Extensive simulation with various micro grids components i.e. load characteristic, network size are carried out to outperform a relevant state-of-the-art controller. The effectiveness of proposed method are verified for Malaysia Electric Power System (MEPS) via Matlab Simulink.
A Capacitance Design Guideline of Snubber Capacitors for Soft Switching in Bi-directional Inductive Power Transfer System Considering Battery Charging Cycle

Previously, soft switching methods for unidirectional inductive power transfer (IPT) systems were proposed. However, these methods were not applicable to bidirectional IPT system as it is. In addition, the appropriate capacitances of snubber capacitors for soft switching were not discussed in these papers. In order to achieve soft switching which can reduce switching loss, it is necessary to design the capacitances appropriately. Furthermore, it is necessary to consider the battery charging and discharging cycle in order to obtain higher efficiency in the charging pattern which is often used. Therefore, this paper shows the design guideline of the snubber capacitors for IPT systems by using the visual evaluation maps considering battery charging and discharging cycles. By using the design method, it becomes easy to decide the appropriate capacitances of the snubber capacitors. In addition, the effectiveness of the capacitances design guideline is clarified by the experiments. As the results, the soft switching could be achieved and the efficiency of the whole system with snubber capacitors could be improved by 0.9 % compared to the efficiency of the system without the snubber capacitors in the experimental condition.
A Metric for Linear Temporal Logic

We propose a measure and a metric on the sets of infinite traces generated by a set of atomic propositions. To compute these quantities, we first map properties to subsets of the real numbers and then take the Lebesgue measure of the resulting sets. We analyze how this measure is computed for Linear Temporal Logic (LTL) formulas. An implementation for computing the measure of bounded LTL properties is provided and explained. This implementation leverages SAT model counting and effects independence checks on subexpressions to compute the measure and metric compositionally.
Optimal Discrete-Time Sliding Mode Control for Nonlinear Systems

This paper presents an optimal discrete-time sliding mode control for simple-input simple-output nonlinear systems subject to input constraints. The proposed controller is developed on the basis of quasi-sliding mode concept. It ensures the finite-time convergence, the stability of the closed-loop system, as well as the follow-up of reference model. Chattering phenomenon is eliminated using the saturation function as a smooth switching function in the controller design. Moreover, particle swarm optimization algorithm is used in the design of the sliding surface in order to optimize the system response characteristics in presence of control input constraints. The effectiveness of the developed scheme is illustrated with an inverted pendulum system.