Miracle-dz/UniFAR-FTUs huggingface dataset:
Electricity Load Forecasting Using an Ensemble of Optimally-Pruned and Basic Extreme Learning Machines

In this paper the application of artificial intelligence on one-step ahead forecasting of electricity consumption is investigated. The total electricity consumption data sampled on a monthly basis (monthly consumption) from January 1985 to December 2011 in South Africa is used. Neural networks, Neuro-fuzzy systems, support vector machines and optimally-pruned and basic extreme learning machines (ELM) were used to develop nonlinear ensemble models for forecasting and their performance is compared. It was found that extreme learning machines significantly outperform traditional techniques except for support vector machines.
Using GANs to Enable Semantic Segmentation of Ranging Sensor Data

Ranging sensors, such as radar and lidar, onboard the vehicle are considered to be very robust under changing environmental conditions. Largely owing to this reputation, they have found broad applicability in driver assistance, and consequently in autonomous driving systems. On the other hand, they lack precision. This makes classification tasks of the measurement data rather difficult. In this paper, we propose a method for semantic segmentation of the ranging sensors data using generative adversarial networks. Utilizing the fully unsupervised learning algorithm, we convert the sensor data to artificial, camera-like, environmental images that are further used as input for semantic image segmentation algorithms.
A Scalable Patient Monitoring System Using Apache Storm

The growth in wearable medical sensor-based technologies has made it possible to capture high volume physiological data of patients, both within and outside the hospital. The acquired physiological data are analyzed, usually in real-time, using a patient monitoring application for early disease detection or to detect any other changing conditions of a patient. In some cases, it is desirable to have a distributed, scalable patient monitoring system to which the physiological data of different patients can be submitted for online analysis. Such a system should be able to support the concurrent analysis of multiple data streams of different patients, allowing a clinician to remotely monitor more than one patient from a single location. This type of system also conserves resources, since in this case, there is no need to provision computational resources for every single patient being monitored. In this paper, we explore the usability of Apache Storm, an open-source real-time processing engine, in the development of such a scalable patient monitoring system. The contribution of this work, therefore, is to demonstrate that it is possible to achieve a more resource-efficient alternative to the isolated patient monitoring systems by using a distributed real-time computation platform, Apache Storm, to develop a scalable health monitoring system that can support the concurrent monitoring of multiple patients. To show how the proposed system can be developed, we describe a prototype implementation of a multi-tenant health monitoring application that monitors the arrhythmia status of multiple patients, based on a simple ECG analysis, using Apache Storm.
Coordination of Modular Multilevel Converter Based HVDC Terminals for Ancillary Services

This paper deals with the utilization of the energy storage capacity, inherently present in the Modular Multilevel Converter (MMC), in providing ancillary services to ac grids. The service considered in this paper is Power Oscillation Damping (POD), which requires active power injection and hence, causes distortion (oscillation) in the dc voltage. In a multi-terminal or meshed dc grid, the oscillation is compensated by active power taken from other terminal participating in dc voltage regulation. This action leads to propagation of the oscillation into other connected ac grids, which is undesirable. This can be avoided or reduced if the power injection is taken from the arm capacitors of the MMC. Such utilization of the MMC energy storage has already been proposed in literature. However, the amount of power that can be injected from one converter is very limited. Therefore, this paper proposes a method to coordinate such a functionality among multiple MMC terminals with the aim of obtaining higher capacity to accommodate more demanding services. Furthermore, communication between the terminals is not required because local measurement of the dc voltage is used as a feedback signal.
Altered dynamics of scaRNA2 and scaRNA9 in response to stress correlates with disrupted nuclear organization

ABSTRACT Small Cajal body-specific RNAs (scaRNAs) are part of small Cajal body-specific ribonucleoproteins (scaRNPs) that modify small nuclear RNA (snRNA) in Cajal bodies (CBs). Several scaRNAs (scaRNA 2, 9 and 17) have been found to generate smaller, nucleolus-enriched fragments. We hypothesize that the fragments derived from scaRNA 2, 9 and 17 form regulatory RNPs that influence the level of modifications within rRNA by altering small nucleolar RNP (snoRNP) activity. Here we show that external factors such as DNA damaging agents can alter the scaRNA9 full length to processed fragment ratio. We also show that full-length scaRNA2 levels are likewise impacted by DNA damage, which correlates with the disruption of SMN, coilin and WRAP53 co-localization in CBs. The dynamics of scaRNA9 were also shown to be affected by Drosha levels, which suggests that this protein may participate in the biogenesis and processing of this non-coding RNA. Identification of factors that contribute to scaRNA 2, 9 and 17 processing may facilitate an assessment of how external stress can lead to changes in rRNA modifications. Summary: Stress conditions, including DNA damaging agents, and knockdown of nuclear proteins, including SMN and Drosha, alter the processing dynamics of scaRNA 2 and 9.
On the space complexity of randomized synchronization

The “waite-free hierarchy” provides a classification of multiprocessor synchronization primitives based on the values of <italic>n</italic> for which there are deterministic wait-free implementations of <italic>n</italic>-process consensus using instances of these objects and <italic>read-write</italic> registers. In a randomized wait-free setting, this classification is degenerate, since <italic>n</italic>-process consensus can be solved using only <italic>O(n) read-write</italic> registers. In this paper, we propose a classification of synchronization primitives based on the <italic>space complexity</italic> of randomized solutions to <italic>n</italic>-process consensus. A <italic>historyless object,</italic> such as a <italic>read-write</italic> register, a <italic>swap</italic> register, or a <italic>test&set</italic> register, is an object whose state depends only on the lost nontrivial operation thate was applied to it. We show that, using <italic>historyless</italic> objects, &OHgr;(<inline-equation><f><rad><rcd>n</rcd></rad></f> </inline-equation> object instances are necessary to solve <italic>n</italic>-process consensus. This lower bound holds even if the objects have unbounded size and the termination requirement is <italic>nondeterministic solo termination</italic>, a property strictly weaker than randomized wait-freedom. We then use this result to related the randomized space complexity of basic multiprocessor synchronization primitives such as <italic>shared counters, fetch&add</italic> registers, and <italic>compare&swap</italic> registers. Viewed collectively, our results imply that there is a separation based on space complexity for synchronization primitives in randomized computation, and that this separation differs from that implied by the deterministic “wait-free hierarchy. ”
AWAIT: An Ultra-Lightweight Soft-Error Mitigation Mechanism for Network-on-Chip Links

Networks-on-Chip have become a widely accepted communication paradigm for many-core Systems-on-Chip. However, with the ever-shrinking transistor size, the network's sensitivity to transient faults on the physical links cannot be ignored since even a single transient fault can lead to a network-wide congestion and a system failure. This paper proposes the AWAIT mechanism, an ultra-lightweight transient fault mitigation mechanism for Network-on-Chip links. The proposed mechanism covers all single event transients. The experimental results show that the AWAIT mechanism prevents network-wide failure even in harsh environments (up to 80 million random faults on links per second). The AWAIT mechanism is also scalable and imposes only 5.1 % area overhead with very negligible critical path delay overhead.
Low-Power Current-Sensing Circuit for Boost Converter

This paper presents an improved current-sensing circuit for boost converter. The proposed circuit replaces n-channel switching transistors with p-channel switching transistors to eliminate glitch current. The proposed circuit therefore consumes less current when the control voltage $\mathbf{V}_{\mathbf{GN}}$ changes from $\mathbf{V}_{\mathbf{DD}}$ to GND. The proposed current-sensing circuit has been designed using standard TSMC 0.18 $\mu$ m 1P6M CMOS technology. In a simulation, the circuit achieved a high sensing accuracy of 96.38% at a 484 mA inductor current. Additionally, a boost converter using the proposed current-sensing circuit can regulate a 1.2 V supply to 1.8 V at a 50 mA load current.
A Hybrid Simulation Tool for Distributed Generation Integration Studies

This work presents a hybrid simulation tool that combines fast analysis of quasi-static time series or transient stability programs and electromagnetic transients ones to evaluate the dynamic behaviour of electric power systems with distributed generation sources. The interaction between the two programs is performed by means of controllable current and voltage sources, which are used to interface external and detailed systems. The double second order generalized integrator (DSOGI) is used to extract the positive-sequence phasor from the detailed system to the external one. A local network server controls the data communication between the two simulation environments by means of the TCP/IP protocol. In the present paper, the proposed tool is used to simulate the integration of a wind power plant, based on a doubly-fed induction generator (DFIG), into a 29-bus electrical network. Results and computational timings are then compared with the ones obtained with an electromagnetic transients program, which demonstrate the accuracy and speed of the proposed strategy.
Disparity Refinement Through Grouping Areas and Support Weighted Windows

In this work, we propose a simple but an effective technique to adjust a disparity map in a more appropriate configuration. This proposal consists of three main steps: segmentation process, statistical analysis and by using adaptive weighted windows. Furthermore, we investigate if a disparity map, yielded by a robust stereo method, can be improved by the proposed methodology. Thus, we implement some stereo vision methods to compare. The experimental results show that the proposed method is efficient and it can make some enhancements in disparity maps, as reducing the disparity error measure.
Accelerating Viterbi Algorithm using Custom Instruction Approach

In recent years, the decoding algorithms in communication networks are becoming increasingly complex aiming to achieve high reliability in correctly decoding received messages. These decoding algorithms involve computationally complex operations requiring high performance computing hardware, which are generally expensive. A cost-effective solution is to enhance the Instruction Set Architecture (ISA) of the processors by creating new custom instructions for the computational parts of the decoding algorithms. In this paper, we propose to utilize the custom instruction approach to efficiently implement the widely used Viterbi decoding algorithm by adding the assembly language instructions to the ISA of DLX, PicoJava II and NIOS II processors, which represent RISC, stack and FPGA-based soft-core processor architectures, respectively. By using the custom instruction approach, the execution time of the Viterbi algorithm is significantly improved by approximately 3 times for DLX and PicoJava II, and by 2 times for NIOS II.
A robust convolutional neural network for online smartphone-based human activity recognition

The online smartphone-based human activity recognition (HAR) has a variety of applications such as fitness tracking, healthcare… etc. Currently, the signals generated from smartphone-embedded sensors are used for HAR systems. The smartphone-embedded sensors are utilized in order to provide an unobtrusive platform for HAR. In this paper, we propose a deep convolution neural network (CNN) model that provides an effective and efficient smartphone-based HAR system. For automatic local features extraction from the raw time-series data, we use the CNN while simple time-domain statistical features are used to extract more distinguishable features. Furthermore, we explore the impact of a novel data augmentation on the recognition accuracy of the proposed model. The performance of the proposed method is evaluated using two public data sets (UCI and WISDM) which are collected using smartphones
Empirical Analysis of MAVLink Protocol Vulnerability for Attacking Unmanned Aerial Vehicles

Recently, unmanned aerial vehicles (UAVs), or the so-called drones, have been used in various applications. In particular, UAVs are used for rescue systems, disaster detection, and military purposes, as well as for leisure and commercial purposes. UAVs that are controlled over networks by ground control stations (GCS) can provide various services with expanded activity area. It is thus of critical importance to investigate the vulnerability of the drone system. In this paper, we focus on UAVs controlled by GCS over networks. We analyze the vulnerability of the micro-air-vehicle communication (MAVLink) protocol, which is one of the most widely adopted communication protocols for GCS-based control of UAVs. Then, by exploiting the vulnerability of the MAVLink protocol, we propose an attack methodology that can disable an ongoing mission of a UAV. Our empirical study confirms that the proposed attack can stop the attacked UAV and disable the mission.
WBAN Path Loss Based Approach For Human Activity Recognition With Machine Learning Techniques

Wireless Body Area Networks are nowadays attracting both academic and industrial worlds. Combining collected data related to patient context with original health measurement can enhance the general health state monitoring and help to better understand the patient disease evolution. Daily activity is one of the important features that may influence the patient health state. Thus, recognizing the user activity can be a useful way for improving quality of health services. Relying on supervised learning, we study the feasibility of extracting and classifying the human activities from channel gain measures, which is an important feature that characterizes the WBAN channel links.
An Insect Prostaglandin E2 Synthase Acts in Immunity and Reproduction

Eicosanoids, oxygenated metabolites of C20 polyunsaturated fatty acids (PUFAs), mediate fundamental physiological processes, including immune reactions and reproduction, in insects. Prostaglandins (PGs) make up one group of eicosanoids, of which PGE2 is a relatively well-known mediator in various insect taxa. While PG biosynthesis has been reported, the specific biosynthetic pathway for PGE2 is not known in insects. Here, we posed the hypothesis that Se-mPGES2 mediates biosynthesis of physiologically active PGE2 through its cognate protein. To test this hypothesis, we interrogated a transcriptome of the lepidopteran insect, Spodoptera exigua, to identify a candidate PGE2 synthase (Se-mPGES2) and analyzed its sequence and expression. Its predicted amino acid sequence contains a consensus thioredoxin homology sequence (Cys-x-x-Cys) responsible for catalytic activity along with an N-terminal membrane-associated hydrophobic domain and C-terminal cytosolic domain. It also shares sequence homology (36.5%) and shares almost overlapping three dimensional structures with a membrane-bound human PGES2 (mPGES2). Se-mPGES2 was expressed in all developmental stages with high peaks during the late larval instar and adult stages. Immune challenge significantly up-regulated its expression levels in hemocytes and fat body. Injecting double-stranded RNA (dsRNA) specific to Se-mPGES2 significantly impaired two cellular immune responses, hemocyte-spreading behavior and nodule formation following bacterial challenge. Humoral immunity was also significantly suppressed, registered as reduced phenoloxidase activity and antimicrobial peptide expression levels. The suppressed immune responses were reversed following PGE2, but not arachidonic acid (AA), treatments. RNAi treatments also reduced the egg-laying behavior of females. Control females mated with the RNAi-treated males led to substantially reduced egg-laying behavior, which was also reversed following PGE2 injections into females. These results strongly bolster our hypothesis that Se-mPGES2 acts in the biosynthesis of PGE2, a crucial biochemical signal mediating immune and reproductive physiology of S. exigua.
Controlled Growth of Freestanding Silicon Nanostructures with High Aspect Ratio

In this work freestanding silicon nanostructures with high aspect ratio are fabricated using a chemical-electrochemical etching technique. <100> silicon samples first are textured, covered with hillocks with pyramid shapes via anisotropic wet etching of the samples in TMAH/IPA solutions. Effects of various combinations of TMAH/IPA on the pyramidal texture of silicon are studied in detail. Then, the textured silicon samples are subjected in an electrochemical etching in HF/Ethanol solution to form the nanostructures. A third step of fine etching of the samples in diluted TMAH/IPA solutions was necessary to remove residual and un-etched walls between the structures. Effect of various fabrication parameters controlling the length and tip-to-tip separation of the final structures are investigated. Potential applications of the fabricated structures are remarked.
Model Predictive Control System Based on Ladder-Structured GOBF Models

In this work, we address the problem of identification and control of dynamical systems where a generalized orthonormal basis functions (GOBF) model with ladder-structure is used to represent the system. In the identification process, we took a genetic algorithm to optimize the number of functions and the model poles. The identified model is then used as the basis for the implementation of a predictive controller, which incorporates the advantages of this type of modeling in the lack of output feedback. A magnetic levitation system was identified and a model predictive controller was used to stabilize the system. Results show the feasibility of this technique in the control of a real system.
Energy Efficient Sink Placement in Wireless Sensor Networks by Brain Storm Optimization Algorithm

Wireless sensor networks represent one of the most promising technologies whose use has significantly increased in the past years. They are used in various applications such as health care monitoring, surveillance and monitoring in agriculture, industrial monitoring, habitat and underwater monitoring, etc. Deployment of the wireless sensor networks introduces number of hard optimization problems. Placement of the elements such as sensors, gateways, sinks and base stations, depend on different conditions and constraints such as signal propagation, distance, energy preservation, reliability. In this paper, we propose a method based on brain storm optimization algorithm for placing multiple sinks in a network consisting of regular sensors and gateways with higher battery capacity. Regular sensor nodes are statically organized in clusters around gateways considering not only distance but also energy efficiency. Gateways communicate with sinks for which optimal positions need to be determined. Location problems are in general difficult and in this case requirement of minimizing the energy additionally complicates the problem. The simulation results report estimation of the network life time. Obtained results have shown that our proposed method outperformed particle swarm optimization based method from literature in terms of mentioned metrics.
Load Detection Method and Control of Class-E Inverter for Dynamic Wireless Power Transfer

Due to the electrical isolation between the transmitter side and the receiver side in dynamic wireless power transfer (WPT), load detection becomes an essential function for saving energy and safety issues. This paper proposes a reliable load detection method without any auxiliary switch and communication device. The proposed method, which is based on the energy equilibrium equations, only needs to sample the input DC current, avoiding sampling the high frequency signals in the resonant tank. This paper firstly determines the threshold current for detecting load by analyzing the power dissipation in the Class-E inverter. Then the details of the control procedure and the design of parameters are also discussed. Finally, experiments of the dynamic WPT system with Class-E inverter are implemented to verify the availability of the load detection method and the control method.
Automatic Localization of Optic Disc using Modified U-Net

The optic disc (OD) localization plays an important role in the automatic retinal image analysis for many applications such as glaucoma detection, macular localization, and retinal vessel analysis. In this paper, we propose a method based on U-net and Depth-First-Select Graph to accurately and efficiently locate the optic disc. The adopted U-net architecture is based on ResNet-50, and it predicts the center of OD and produces a probability map. Then based on the probability map, we use the Depth-First-Select algorithm to select the brightest and largest region, which is most likely to be the OD. The proposed method is evaluated on the ORIGA and Messidor dataset. Our experiment shows that the proposed method achieves 100% accuracy in ORIGA and 99.83% accuracy in Messidor. It outperforms other optic disc localization algorithms.
RibORF: Identifying Genome‐Wide Translated Open Reading Frames Using Ribosome Profiling

Ribosome profiling identifies RNA fragments associated with translating ribosomes. The technology provides an opportunity to examine genome‐wide translation events at single‐nucleotide resolution and in an unbiased manner. Here I present a computational pipeline named RibORF to systematically identify translated open reading frames (ORFs), based on read distribution features representing active translation, including 3‐nt periodicity and uniformness across codons. Analyses using the computational tool revealed pervasive translation in putative ‘noncoding’ regions, such as lncRNAs, pseudogenes, and 5′UTRs. The computational tool is useful for studying functional roles of non‐canonical translation events in various biological processes. © 2018 by John Wiley & Sons, Inc.