Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Day-ahead Clearing Model of Spot Electricity Market Containing Renewable Energy

In the context of energy transformation, the process of power system reform has been further accelerated. As an important part, the power spot market can achieve power balance in a smaller time scale, thus promoting the further optimization of resources. This paper established a clearing model for the day-ahead electricity market, simulated the complete day-ahead clearing process, explored the law of market operation through the analysis of examples, and discusses the influence of various market factors on the clearing results, including different market mechanisms, access to renewable energy, etc.
An Enhanced GPSR Protocol for Vehicular Ad hoc Networks

Several routing protocols have been proposed for ad hoc vehicle networks. They affect directly but differently the quality of the transmitted data. In this paper, we introduce a multipath feature to decrease packet loss in greedy perimeter stateless routing protocol (GPSR). Since using only one path during transmission may cause packet loss due to congestion and saturation in this path. Our enhancement is based on sending the same packet over two different paths to maximize its reception probability. This scheme is evaluated in a highway environment using Omnet++, Veins, and SUMO. The results show that the proposed solution overcomes GPSR in packet delivery ratio.
Research of Neural Network Methods of Text Information Classification

Automatic categorization of texts according to their content is an urgent task in the conditions of constant increase in the volume of text information. In this case, the assessment of subjectivity or objectivity of the text in relation to the subject is called the task of analyzing the tonality of the text and is fundamental in the analysis of opinions. The paper analyzes both theoretical and practical aspects of text classification methods, as well as proposes new approaches to text classification using recurrent neural networks, allowing to analyze the tonality of reviews.
Deep-RL: Deep Reinforcement Learning for Marking-Aware via per-Port in Data Centers

In this paper, we propose Deep-RL—a marking decision with Deep Reinforcement Learning (DRL) via per-port for solving the erroneously marking problems in multi-queue multi service scenarios of Data Center Networks (DCNs). We formulate the statement as a DRL problem and use Deep Neural Network (DNN) to achieve the best possible policy for the agent. In this way we can model the complex DCNs in order to obtain the optimal threshold in the output port when the marked packets from queue buffers are not a concern. Unlike prior research that focused on mathematical models or used machine learning in DCN, Deep-RL is a novel DRL based method, which optimizes the real value of threshold with continues action space. Thus, this fact makes our work incomparable with previous research. To the best of our knowledge, we are the first to discuss the problem with DRL and DNN. Simulation results demonstrate that Deep-RL utilizes the buffer capacity at exactly 30% and achieves near optimal flow completion time.
Random Weighting-Based Nonlinear Gaussian Filtering

The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged. This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering. It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics. Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy.
Classification of Alzheimer's Disease Using Stacked Sparse Convolutional Autoencoder

Alzheimer's disease is a neurodegenerative disease that affects the brain structure and its functions. Early and accurate detection of AD through medical imaging may improve lifespan and overall quality of life for patients and their caretakers. In this paper, a specially developed sparse autoencoder is used to accurately detect AD in PET/CT (Positron Emission Tomography/ Computerized Tomography) brain images. Sagittal and coronal images were created from axial images, and those were trained separately to compare classification results. Two-stage training is utilized; first stage, a supervised training to train the classifier to identify the AD, and an unsupervised learning in order to produce an image output. In the created dataset, state-of-the-art classification models are trained and compared to the developed model. A 98.67% accuracy is reached for sagittal images. Detailed information is provided in chapters three and four.
Compact Directional Coupler for Centimeter and Millimeter Wavelength Ranges

In this paper the compact directional couplers for centimeter and millimeter frequency bands are investigated. Using the NI-AWR Design Environment software, four layouts of miniaturized couplers were synthesized at the central frequencies of 1, 10, 20 and 30 GHz, which dimensions are smaller by 77.75%, 58.8%, 48.4% and 49.4%, respectively, in comparison to the standard designs. Miniaturization was carried out based on equivalent replacement of quarter-wave segments with low-pass filters of the 3rd order.
Privacy-enhancing Cloud Computing Solution for Big Data

variety of users cyber data are daily collected by enterprises and governments. These data are processed in order to improve users lifestyle, e.g. improving healthcare and optimizing traffic. Cloud computing is often the only possible strategy which allows processing these big amount of information due to the associated costs. However, data owners remain reluctant to outsource their data to a cloud which may read, use or even sell these data leading on user privacy leakage. These threats have been already observed by EU organization and reflected in many regulations and strategies.This paper present data splitting techniques as one of the most promising technology for privacy-preserving computation in a cloud. At first, we propose a new protocol for secure scalar product resistant against honest-but-curious colluding cloud. Then, we compared our new proposal with two most efficient state-of-the-art schemes. These schemes are not secure in a colluding security model. While using higher security level of our proposal, we achieved comparable performance. The protocols were implemented in real environment based on Amazon Web Services and, then, an FPGA processor cards implementation is considered in order to speed up the computations.
Smart Antenna Design And Implementation For Vehicles

Wireless communication has eased out human life by eradicated wires jargons running around electronic systems for extending connections. Wireless sensors have made human life comfortable. Self-driving cars will be the era of next generation vehicles. These vehicles will be housing many integrated sensors for taking autonomous decisions while in route. These vehicles have communication capability with infrastructure as well as self. They are equipped with facilities of infotainment and entertainment. Communication wirelessly with self and surrounding have utmost need of compact and efficient integrated antennas for these automotive vehicles. MIMO based antenna has been proposed for diversity reception along with compact size and futuristic aesthetic design. Experimental results have been compared with simulated with close proximity. Self-reliance vehicles embedded with Nano DRA have been unique features of this research. Futuristic design of automotive vehicles should be self-driving and self-charging with possible integration of Nano antennas and Long Range Radar (LRR) as sensor. Overview of smart vehicle antennas has also been included. Possible efficient and compact antennas for use of smart vehicles have been proposed.
Statistical Information Theory and Geometry for SAR Image Analysis : Invited Paper

This paper is intended as a road map for the literature on Synthetic Aperture Radar (SAR) image processing and analysis with Information-Theoretic and Information-Geometric Statistical tools. We do this by commenting upon seemingly disjoint problems, and showing that they can be tackled successfully within the same framework: tools that arise from using Information Theory (entropies and divergences) and Information Geometry (geodesic distances) enhanced with Statistics.
PERCeIDs: PERiodic CommunIty Detection

Many complex networked systems, both natural and human-made, exhibit periodic behavior driven by underlying seasonal processes: election cycles and regular sporting events in social networks, cell cycle phases in gene networks, and load variation in infrastructure networks due to weather or daylight patterns. The "natural" periodicity may vary across network communities. At the same time this periodic community behavior is central to (i) understating the overall system dynamics and (ii) for detection of the communities themselves. The predominant approach to dynamic community detection first detects communities and then as a second step quantify seasonality in their activity. How to jointly detect communities and their inherent periodicity, while also accounting for non-periodic one-off events? We propose PERCeIDs, a framework for periodic overlapping community detection from temporal interaction data. We model observed pairwise interaction activity as a mixture of periodic and outlier (non-periodic) components. We explicitly enforce periodic structure within our model by learning a succinct Ramanujan basis dictionary for community behaviors. By explicitly modeling periodicity, PERCeIDs outperforms baselines on both detecting highly overlapping communities with up to 2-fold improvement in NMI compared to state-of-the-art baselines, while offering an interpretable temporal structure for discovered communities in the dataset. Implementation of our method can be found at http://www.cs. albany.edu/~petko/lab/code.html.
Investment Benefit Evaluation of Incremental Distribution Network Considering Regional Power Supply Growth

The incremental power distribution market, which is a key link in the new round of power system reform, is attracting more and more attention for power industries. Firstly, the investment business of incremental distribution network is defined in this paper, and the investment business of incremental distribution network is divided according to its function and property rights. Then the operation modes of the incremental distribution network are analyzed and the differences between incremental distribution business and electricity sale business are compared. On this basis, an evaluation model of investment benefit of the incremental distribution network considering regional incremental power supply is constructed. Finally, the results of case study show that incremental distribution network is helpful to the vigorous promotion of the pilot reform of the incremental distribution network.
Carbon Nanotube FET based Power Shaping Circuit used in Biomedical Applications

This paper provides full-wave rectifier architecture based on carbon nanotubes Field Effect Transistors (CNTFETs). In this shaping (rectifier) circuit the resultant threshold voltage of switching transistor is diminished with the use of bootstrapped capacitors. This architecture has a very good overall power efficiency, ripple and low voltage drop even at very low input voltage (typical voltage $\sim$0.5V) over a ample range of frequency (30-50 MHz). Hence, this rectifier architecture is suitable to use in the environment of low power like implantable biomedical circuits. Dynamic Bulk Switching (DBS) technique has been used to bias the bulk of chosen transistors. This technique lower the leakage current to the bulk reducing on resistance. This architecture works very well at low voltage (0. 5V to 0. 6V) and high operating frequency (50MHz).
Hierarchical Optimal Control of the Resilient Community Microgrid in Islanded Mode

This paper studies a three-level hierarchical control strategy for the resilient community underground microgrid in Potsdam, NY, operated in the islanded mode. Specifically, an AC Optimal Power Flow (ACOPF) problem is solved via rank-relaxed semidefinite programming (SDP) method on the tertiary level to derive an optimal operating point of the microgrid. Two decentralized secondary control methods based on proportional-integral (PI) control are proposed, one is to make PI controllers continually act and the other is to periodically reset the PI outputs to zero. Both methods allow distributed generation (DG) units to closely track the optimal economic dispatch commands by generating frequency correction terms to the primary P - f droop controllers. Simulation studies are carried out with PSCAD/EMTDC, where the proposed methods are compared.
500 Mbps One Cycle On-Off Keying Modulator Using Resonant Power Converter

This paper presents the integration of digital modulator and power amplifier using resonant power converter. The proposed concept can be referred as Digital Power Modulator. The aim is to improve the energy efficiency in modern digital communication systems. The converter has specification of 48 V input voltage, 50 ohm output load, 30 W output power, 500MHz switching frequency. The voltage stresses of semiconductor devices and capacitors will be analyzed in this paper. In addition, the behavior of current and voltage waveforms will be simulated and analyzed with PSPICE software. The combination analysis of the converter in DCM (Discontinuous Conduction Mode) operation to modulate current and voltage at 50 ohm load in a resonant mode based on One Cycle On-Off Keying digital modulation technique is also provided. Simulation results show that the On-Off Keying modulation with data rate of 500 Mbps can be produced using the proposed resonant power converter.
MEC-assisted Immersive Services: Orchestration Framework and Protocol

Immersive media applications demand both a huge amount of data processing and low latency. The former requirement can be fulfilled by offloading user equipment (UE) tasks to a baseband unit (BBU) pool (or data center). As for the low latency, it can be achieved with Mobile Edge Computing (MEC), which allows offloading the tasks close to the UEs. In this paper, we take advantage of the cooperation between conventional BBUs and MEC to handle the UE tasks. Specifically, we propose an orchestration framework and a protocol to enable seamless immersive media applications. Our extensive simulations show that our solution can significantly reduce the number of failed tasks.
Speech Emotion Recognition Using 1D CNN with No Attention

Speech emotion recognition (SER) has gained much attention in recent years. SER system may be efficient depending on how much useful information contained in the extracted emotional features. Many research works have achieved state-of-the-art results using Convolutional Neural Network with different extracted speech features. These kinds of models can’t collect relative emotional salient features from speech signal. In this paper, we present a novel complementary feature extraction method to extract salient emotional features. We compute Melspectrogram and Mel Frequency Cepstral Coefficients (MFCC) to capture time-frequency domain information, aimed at converting raw speech into emotional informative features from speech signals. Moreover, we adopt complementary property strategy to extract features and construct 1D CNN model which selects emotional features effectively and evaluate the model’s performance on IEMOCAP, RAVDESS and Emo-DB speech corpus. Our method achieves better performance than baselines and competitive results using complementary features as input.
Effect of probiotic preparations (EM) and sex on morphometric characteristics of the digestive system and leg bones, and caecal microflora in broiler chickens

ABSTRACT The objective of the study was to determine the effect of probiotic preparations Pro-Biotyk EM-15 and EMFarma™ and sex on morphometric characteristics of the digestive system and leg bones, and caecal microflora in Ross 308 broiler chickens. One-day chicks were distributed into two groups in two rooms (control, experimental). Each facility contained 9000 Ross 308 chicks and was divided into four compartments. A total of 48 Ross 308 broiler chickens aged 42 days were evaluated, including 12 males and 12 females which received no probiotics, and 12 males and 12 females supplemented with EM preparations. At the age of 42 days, EM supplemented broilers had significantly greater total intestine length, higher intestine–body length ratio and higher proventriculus percentage in body weight. Regardless of the treatment, males exhibited higher body weight, duodenum, jejunum, ileum lengths and total intestine length, higher intestine–body length ratio and spleen weight, and lower duodenum diameter and liver percentage. The group × sex interaction was significant for smallest breadth of the corpus of femur bone and greatest diagonal of proximal end, smallest breadth of the corpus and smallest breadth of distal end of tibia bone.
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978-1-7281-4790-1/19/$31.00 ©2019 IEEE Abstract — The application of the Green's functions to solve the problems of excitation, radiation, propagation, and diffraction of electromagnetic waves is shown. The representation of Green’s functions for free space in the Cartesian, cylindrical and spherical coordinate systems is considered. The electromagnetic field is represented as a superposition of electric and magnetic waves in all of the three coordinate systems. Unbounded space is represented in the form of rectangular, cylindrical and radial-spherical waveguides. A modification of Green's functions for layered inhomogeneous media is shown. A model of equivalent electrical circuits is used to determine the spectral components of the electromagnetic field. Layer and boundary transfer matrices and terminal loads are used to describe the layered structure and to find modal currents and voltages in the equivalent electrical circuits. The application of the Green’s function method to solve the problems of radiation of patch and slot antennas on flat and cylindrical surfaces, as well as the leaky wave antenna comprising a rectangular waveguide with transverse slots is shown. The solution of the Luneburg lens radiation problem is also considered. In addition, the application of Green's functions to solving diffraction problems of layered, flat and cylindrical structures is shown.
A new brain CT reference line: the lower eyelid to the inner occipital base line closely parallels the Talairach–Tournoux line

Background There is no standardized brain computed tomography (CT) reference line that can be determined on both scout and reformatted sagittal CT images. Here, a tangential line from the anterior edge of the lower eyelid to the inner table of the occipital base (LEL/O line) appears nearly parallel to the Talairach–Tournoux (T/T) line, which shows a consistent intracranial anatomical relationship among subjects, and acts as a standard reference line for magnetic resonance (MR) imaging. Purpose To quantitatively validate the LEL/O line as a new standard brain CT reference line. Material and Methods We measured: angle 1 = the LEL/O line on scout images from the LEL/O line on parasagittal CT images (n=93); and angle 2 = the LEL/O line on scout images from the T/T line on high resolution midsagittal MR images (n=97). Angles in a clockwise direction were defined as positive and were expressed as mean±SD with the 95% confidence interval (CI) of the SD. Angle 2 was measured independently by two observers and intraclass correlation coefficients (ICCs) were calculated. Results Angle 1 was –0.4°±1.2° (95% CI of SD 1.1°–1.5°); angle 2 was –0.4°±2.0° (95% CI of SD, 1.8°–2.4°). The ICC in the angle 2 measurement was 0.780 (P<0.001), which indicated high inter-observer reliability. Conclusion The LEL/O lines on scout and parasagittal CT images show practically the same gradient, and these LEL/O lines are almost parallel to the T/T line. Use of the LEL/O line either for direct scanning or reformation can minimize intra- and inter-subject variations on CT images and mismatch between CT and MR images.
Antioxidant, Anti-inflammatory, and Antiproliferative Activity of Mori Cortex Radicis Extracts

Mori Cortex Radicis (MCR) is a well-known Korean and Chinese folk medicine with anti-obesity, anti-inflammatory, anti-asthmatic, and hypoglycemic activities. This study was aimed to evaluate the total phenolic and flavonoid contents, as well as intracellular antioxidant and anti-inflammatory effects of water and 70% (v/v) ethanol extracts of MCR. The antioxidant activities of MCR extracts were determined with diphenyl-2-picrylhydrazyl and 2,2′-azinobis[3-ethylbenzothiazoline-6-sulfonic] scavenging activity assays. The suppressive activities of MCR extracts on the production of nitric oxide (NO*) and the expression of cytokines, c-Fos, activated p38-Mitogen-activated protein kinase (MAPK), and Nuclear factor Kappa B (NF-κB) and splenocytes proliferation in lipopolysaccharide-treated macrophages were determined. Furthermore, this study demonstrated the effects of MCR on reactive oxygen species production in murine macrophages. Mori Cortex Radicis restored deoxyribonucleic acid damages at higher concentrations of the extracts and significantly suppressed free radicals and NO* production. In this study, MCR significantly restored inflammatory responses and intracellular antioxidant activities in murine macrophages (RAW 264.7), which anticipated that MCR could be used as a natural anti-inflammatory agent.