Miracle-dz/UniFAR-FTUs huggingface dataset:
Underwater Image Denoising Based on Non-Local Methods

Due to the medium scattering and light absorption, the acquired underwater optical images suffer from poor visibility. Absorption and scattering effects are caused not only by the water itself but also by other floating particles. Moreover, backscattering is strengthened during the light is reflected and deflected multiple times by the suspended particles before reaching the camera. The backscattering gives rise to specific strong correlated noise together with a constant level degrading the image contrast. Although there are many effective dehazing methods to remove the veil background, they have ignored the influence of backscattering noise, which still exists in the dehazed images. In addition, because of the special characteristics of backscattering noise, the traditional image denoising methods are difficult to make sense, even cause distortion. Accordingly, this paper employed the non-local denoising methods which are referred to Non-Local Means algorithm and Block-Matching 3D collaborative filtering to suppress the backscattering noise. The experimental results show that they can effectively remove the backscattering noise of underwater image and achieve better image quality.
Hybrid Classification of WEB Trojan Exploiting Small Volume of Labeled Data Vectors

This research paper introduces a Denoising auto encoder (Unsupervised Deep Neural Network) combined with a typical Back Propagation (BP) Artificial Neural Network (ANN), capable of efficiently detecting WEB Trojan malware. Several researchers in the literature, employ Machine Learning (ML) to detect WEB Trojans. The data used in this paper, come from the WEB security Gateway, since there is less tagged data than unlabeled ones. Based on the literature, simple Supervised Learning (SULE) is not efficient enough for this task. The algorithm proposed herein is hybrid. It employs Unsupervised Learning (UNLE) based on a Stack Denoising Auto encoder (SdAE) to pre-train the data (one layer at a time). This results in more robust feature vectors. Then, in the fine-tuning process, minor adjustments are made through Supervised Learning (SUL) based on a BP ANN. The proposed approach, ensures that the developed model, can still perform accurately, even when the training data set has a small number of tagged data vectors. This research, verifies this hybrid Deep Learning approach used for WEB Trojan detection, outperforms other common classification methods.
A Deconvolved Method for Suppressing the Sidelobes in the DOA Estimation

Conventional beamforming (CBF) is one of the most important nodes in the underwater array signal processing. For the direction of arrival (DOA) estimation, CBF is robust but it suffers from the wide beamwidths and the high sidelobe levels. The background levels of bering-time recording (BTR) using CBF algorithm are high when the signal-to-noise ratio (SNR) of the receiving data is low. This paper proposes a deconvolved conventional beamforming (ADDCBF) for suppressing the sidelobes levels of CBF in the DOA estimation. The algorithm utilizes an analogous impulse function as the point scattering function (PSF), and then the direction of the source can be estimated by iterating the azimuth spectrum of CBF based on the Richardson-Lucy (R-L) algorithm. ADDCBF yields the narrow beams and the low sidelobe levels than CBF. The performance of ADDCBF are verified by the numerical simulations and the data collected by a horizontal linear array during an experiment in the South China Sea.
Key management in tree shaped hierarchies

ABSTRACT We refer to an access control system based on subjects and objects. Subjects are active entities, e.g. processes, while objects are passive entities, e.g. messages exchanged between the nodes of a distributed computing environment. The system is partitioned into security classes organized into a tree shaped hierarchy. A subject assigned to a given class can access the objects in this class and in all the classes that descend from this class in the class hierarchy. To this aim, a key is associated with each class. A mechanism of the protection system, called key derivation, allows a subject that holds the key of a given class to transform this key into the keys of the descendant classes. This mechanism is based on a single, publicly known one-way function. If the class hierarchy is modified, by adding a new class or deleting an existing class, the necessary form of key redistribution is partial, and is limited to the classes in the subtree of the root that is involved in the change.
A Concurrent Transmission Based Broadcast Scheme for Urban VANETs

Many applications in vehicular ad hoc networks (VANETs) are based on broadcast to disseminate information among vehicles. In conventional broadcast protocols, only one vehicle is scheduled to rebroadcast a message at a certain time to avoid collisions among vehicles. In this paper, we derive the maximum temporal displacement required by the concurrent transmissions in VANETs and propose a Concurrent Transmission based Broadcast (CTB) protocol. The CTB includes two parts, broadcast in a street and broadcast at intersections. We divide the transmission range in the broadcast direction into segments and schedule the concurrent transmissions of the forwarders in the same segment. In our protocol, even if some selected forwarders fail to receive the message, other forwarders having received the message can still rebroadcast it, which reduces the broadcast delay and increases the broadcast reliability. Simulation results show that our protocol is faster and more reliable, compared to conventional broadcast protocols.
Development of a vision-based feature extraction for food intake estimation for a robotic assistive eating device

Our research aims at making a mock-up of a multi grip tool for a robotic assistive device and a camera system which enable frail elderly to live more independently and to keep track of their food intake. In this research, the development of a vision-based feature extraction for food intake report for a robotic assistive eating device is introduced. The proposed vision system is composed by a RGB-D camera and the eating aid device Bestic. In this paper, the authors proposed an algorithm for estimating the amount of intake food (e.g. candies) after the end of the meal. The proposed vision-based feature extraction was designed and implemented in order to take pictures of the plate during the meal as well as estimate the amount of intake food. A set experiments were carried out in order to verify the performance of the proposed vision system while estimating the amount of intake food with different amount of multicoloured candies as well as different light conditions. Based on the experimental results, we could verify the system performance is not affected by the amount of candies as well as the light conditions.
Robust Visual Odometry in Underwater Environment

The accurate estimation of pose and velocity of an autonomous underwater vehicle (AUV) is critical to ensure the repeatability and validity of scientific data that is captured using sensors onboard the AUV. A low-cost and effective way is by using stereo camera sensors to perform visual odometry (VO). However, this is a difficult problem in underwater due to poor imaging condition and inconsistent motion caused by water flow. This paper proposes a robust and effective stereo underwater VO system that can overcome aforementioned difficulties and accurately localize the AUV. Experimental results demonstrate that the proposed pipeline outperforms existing VO systems in underwater environment, as well as obtains a comparative performance on the KITTI benchmark dataset.
Draft Genome Sequence of Pseudomonas sp. LS-2, Isolated from a Diseased Gastrodia elata Plant

We report here the draft genome sequence of Pseudomonas sp. LS-2, isolated from the decaying aerial stem of a Gastrodia elata plant. ABSTRACT We report here the draft genome sequence of Pseudomonas sp. LS-2, isolated from the decaying aerial stem of a Gastrodia elata plant. This genome harbored 96 potential genes implicated in bacterium-plant interactions, which may facilitate the adaptation of strain LS-2 to plant environments.
Specific Absorption Rate Evaluation in Case of Exposure of the Human Body to Radiofrequency Electromagnetic Field Generated by Mobile Communications

EMC is a permanent challenge for many items associated with radio communications. Inside this large frame work, human exposure issue should be always considered. For that, it is worth of knowing the impact on the short and on the long term of electromagnetic field irradiation over biological tissues of human body. The present paper is a study over the estimation of specific absorption rate (SAR) on a developed 3D model of the human body in case of exposure to electromagnetic fields generated by mobile communications in some areas (indoor and outdoor) with specific instrumentation (Spectran HF-60105 V4 spectrum analyzer with HyperLOG 60100 antenna). The SAR values was determined by simulation using the CST MW Studio® software and the values were compared with limits values for SAR imposed by ICNIRP (1998) restriction limits and European Directive 2004/40/CE standards, adopted also in Romania by HG number 1136 from 30 august 2006. Additionally, the RSAR, while dealing with simultaneous exposure to different frequency fields should be considered.
A Low-Profile Beam-Scanning Continuous Transverse Stub Array Based on SIW Technology

A low-profile continuous transverse stub (CTS) array antenna based on substrate integrated waveguide(SIW) is presented for beam-scanning. This array is comprised by 8-element CTS antenna excited with quasi-TEM wave. The overall height of array structure is as thin as 0. 38λ0. A good beam-scanning capability of 60deg is achieved by changing the angle between SIW-CTS array and quasi-TEM wave, and the gain is larger than 18 dB within scanning range from 34.175 GHz to 35.875 GHz.
16S rRNA Gene Amplicon Data Set-Based Bacterial Diversity in a Water-Soil Sample from Pangong Tso Lake, a High-Altitude Grassland Lake of the Northwest Himalayas

We report here 16S rRNA-based bacterial diversity existing during freezing conditions in a high-altitude Himalayan lake through sequencing a 16S rRNA gene amplicon data set. A total of 121,857 high-quality reads were obtained; 40.78% of the bacterial population was classified to the genus level, while 1.26% was classified to the species level. ABSTRACT We report here 16S rRNA-based bacterial diversity existing during freezing conditions in a high-altitude Himalayan lake through sequencing a 16S rRNA gene amplicon data set. A total of 121,857 high-quality reads were obtained; 40.78% of the bacterial population was classified to the genus level, while 1.26% was classified to the species level.
Performance Assessment of Low-pass Filters for Standalone Solar Power System

Nowadays, electricity demand significantly increases along with reduction of negative impacts to environment leads to a strong development of renewable energy which includes solar energy. In order to be able to provide power for the load, photovoltaic (PV) has to receive solar radiation to convert into electricity, through the converters to obtain such responses as voltage, frequency, etc. suitable for the load. However, this conversion creates the harmonics, one of the factors that reduce power quality and damages the devices. This paper studies two passive single-tuned and double-tuned passive filters coupled with converters for off-grid solar system. By means of simulation from Matlab/Simulink software, a solar conversion system and filters are built to meet the load requirements of 1kW and rated voltage of 220V. The results show that the double-tuned filter offers better harmonics filtering than the single-tuned filter, very low output voltage distortion for stable loads.
SVM-Based System for Broken Rotor Bar Detection in Induction Motors

In this paper we propose a system for the detection of broken bars (BB) in induction motors (IM) based on a binary SVM classifier. Statistical features obtained from a IM current are used as inputs of the classifier. We train the classifier with simulation data, using a qd model of IM. Through simulation, 360 records of 120 cycles were generated in which different IM conditions were considered, healthy, rotor fault and disturbances in torque and voltage. Finally, the classifier was validated with experimental laboratory data, obtained from an IM of 5.5 kW in healthy, 2 and 3 BB, with different load and supply voltage conditions. The results show that the proposed classifier allows detection with 91.67% accuracy, and without false negatives (faulty IMs classified as heathy).
Near Field Wireless Power Transfer for Multiple Receivers by Using a Novel Magnetic Core Structure

A Wireless Power Transfer (WPT) method is proposed by using a novel magnetic core structure. Similar to those transformers, magnetic cores are utilized as main object that conducts magnetic flux in the proposed cell. Power is transferred through near magnetic field and the distance between the receiver and the transmitter is thus relatively short. Every basic cell has only one primary side, where the magnetic cores of different permeabilities are combined to build a multi-transmitter structure for multiple receivers. By arranging and guiding the flux via a specific magnetic path, it offers some space of freedom for the locations of the multiple receivers. The usage of the magnetic core also increases the coupling coefficients between the primary-side transmitter and secondary-side receivers compared to core-less WPT using spiral coils. Analysis of the three working modes of the proposed WPT cell is presented. Experimental results show that the proposed basic cell with three receivers achieves 85% efficiency at 100W.
Passive Network State Monitoring for Dynamic Resource Management in Industry 4.0 Fog Architectures

The use of technology from the information world in the factory is enabling the development of Industry 4.0. This information and operation technologies convergence can be accomplished by the Fog paradigm, in which distributed devices collaborate among them. This paradigm allows to benefit from the main advantages of Cloud Computing but closer to the factory. Dynamic resource management is a widely spread concept in the Cloud. It selects how applications are deployed based on device resource availability. However, network resources are an important part of these distributed Fog architectures, and have to be considered in Fog resource management algorithms. This paper presents a method to obtain network metrics (latency and packet lose rate) in a distributed architecture focusing on being scalable and lightweight so that they can be used to enable new dynamic resource management algorithms.
An Enhanced Multi-frequency Small-signal Model for the Close-loop Design in Buck Converters

Due to the effective frequency limitation of the small-signal model derived from the average model for buck converters, the multi-frequency small-signal model was found to solve this issue by taking another dominant sideband frequency component into consideration. Based on it, this paper proposes an enhanced multi-frequency small-signal model considering two dominant sideband frequency components, which has a better prediction of the high-frequency performance. What's more, the modeling process is simplified by replacing the describing function with frequency domain analysis. And the loop gain for close-loop design derived from the new model still maintains simplicity, which guarantees the using-friendly of this new model. In addition, an important factor which influence the accuracy of this model is also shown to guide the application. Simulation and experimental results are presented as verifications.
New Reference Genome Sequences for 17 Bacterial Strains of the Honey Bee Gut Microbiota

We sequenced the genomes of 17 strains isolated from the gut of honey bees, including strains representing the genera Lactobacillus, Bifidobacterium, Gilliamella, Snodgrassella, Frischella, and Commensalibacter. These genome sequences represent an important step forward in the development of a comprehensive reference database to aid future analysis of this emerging gut microbiota model. ABSTRACT We sequenced the genomes of 17 strains isolated from the gut of honey bees, including strains representing the genera Lactobacillus, Bifidobacterium, Gilliamella, Snodgrassella, Frischella, and Commensalibacter. These genome sequences represent an important step forward in the development of a comprehensive reference database to aid future analysis of this emerging gut microbiota model.
Formation of students' body build as an element of western educational strategy (in m. Foucault's theory)

Human corporeality is directly related to the space of education, although education is interpreted as, first of all, an intellectual process of cultural heritage transmission. Nevertheless, the formation of student’s physical body build is an important component of the state educational strategy. In accordance with the objectives and requirements of state and society there can be observed global changes in human corporeality within educational activities: the transformation of the body as a biological phenomenon into a socio-cultural phenomenon or “politically subjected body”. The authors use Michel Foucault’s ideas as the methodological base of their study of a student’s body transformation processes. Foucault’s ideas suggest that power influences an individual and their authentic behavior through the disciplining of their body, that the aim of the new type of power – the bio-power – is to increase the productivity and efficiency of a human body in the conditions of capitalistic industrial production, and that school is a specific disciplinary area, which allows to meet governmental demand for a new method of control via the production of “obedient bodies”. The authors come to the conclusion that in the modern educational model, despite democratization and humanization processes Foucault’s trends persist because education continues to be the part of the technocratic discourse. Modern school is a disciplinary space fenced off from the outside world, where there is a rigid hierarchy and a system of differences, physical drill, regulation, etc. All these are conditioned by the fact that school honors a governmental request for a technocratic person.
Chiller Plant Operation Optimization with Input and Model Uncertainties

Given high energy costs of chiller plants, plant operation optimization is important. Optimized results may not satisfy actual demand if ignoring input uncertainties. Without considering model uncertainties (e.g., parameters), more energy may be consumed than what is expected. Therefore, optimization with input and model uncertainties is studied. However, models with multiple parameters in the presence of uncertainties are complicated and the number of constraints increases exponentially to satisfy possible conditions caused by uncertainties. Additionally, with continuous and discrete decision variables (e.g., the number of active chillers), the problem is a mixed-integer problem. In this paper, to overcome above difficulties, uncertainties are considered for key parameters selected based on sensitivity analysis. For bound constraints, min/max values of variables with uncertainties are used to reduce the number of constraints. To obtain near-optimal solutions efficiently, a decomposition and coordination method is used. Chillers and cooling towers with the same uncertain temperature are solved together to avoid involving additional coupling constraints for temperature. Results show near-optimal solutions, short CPU times, and scalability of our method. Our method achieves significant energy savings as compared with the baseline, and has higher probability of satisfying cooling demand as compared with the method without considering uncertainties.
Model Predictive Control for Isolated DC/DC Power Converters with Transformer Peak Current Shaving

In this paper a Model Predictive Control, suitable for isolated DC/DC power converters and featuring optimization on converter operations, is proposed. The control is designed based on a specific a current-fed DC/DC converter topology, named Active-Bridge-Active-Clamp converter. This topology is particularly suitable for batteries interface, utilizing its interleaved structure in order to achieve an effective current ripple cancellation at the low voltage terminals. However, the increased complexity of the ABAC converter requires a specific modulation implementation and a detailed converter modeling. For such reasons, the operating principle of the ABAC converter is firstly introduced and mathematical models of the ABAC are developed in this paper. Subsequently, a Model Predictive Control is proposed and implemented aiming to achieve terminal current regulation, improve dynamic performance and reduce current stress in a wide DC voltage operating range, whilst maintaining a fixed switching frequency and a reduced prediction horizon. Simulation results for a 10kW ABAC converter are provided to validate the theoretical claims.
RL-BAGS: A Tool for Smart Grid Risk Assessment

The security of critical infrastructure such as Smart Grid is of significant concern because cyber-physical attacks are becoming a frequent occurrence. Cybercriminals compromise cyberinfrastructure to control physical processes maliciously. It is the system administrator's goal to find vulnerabilities in the smart grid functions and patch them before they are compromised. Unfortunately, limited resources and a large attack surface make it difficult to decide which function to protect in a particular system state. In this research paper, we tackle the problem of resource allocation in the smart grid system by proposing a tool, Reinforcement Learning-Bayesian Attack Graph for Smart Grid System (RLBAGS), which provides functionality to the system engineers to compute optimal policies on regular intervals about whether to SCAN or PATCH a particular function of the smart grid system. RL-BAGS considers functions and network architecture of the system to generate a Bayesian Network, which represents the state of the system. RL-BAGS implements two reinforcement learning algorithms, Q-Learning and SARSA learning, on the generated Bayesian Network to learn optimal policies. RL-BAGS assists system administrators performing in-depth studies of one of the functions of the smart grid system advising effective actions to scan or patch a system component.
Microbial Diversity Analysis of Sediment from Nakdong River Estuary in the Republic of Korea Using 16S rRNA Gene Amplicon Sequencing

Deterioration of sediment quality has been found in the Nakdong River Estuary after large-scale reclamations. Here, I report microbial diversity in sediments of Nakdong River Estuary in the Republic of Korea based on 16S rRNA gene sequencing by next-generation sequencing (NGS) techniques. ABSTRACT Deterioration of sediment quality has been found in the Nakdong River Estuary after large-scale reclamations. Here, I report microbial diversity in sediments of Nakdong River Estuary in the Republic of Korea based on 16S rRNA gene sequencing by next-generation sequencing (NGS) techniques. The dominant bacterial phyla were Proteobacteria, Firmicutes, Bacteroidetes, and Planctomycetes.
Draft Genome Sequences of Three Novel Staphylococcus arlettae Strains Isolated from a Disused Biological Safety Cabinet

The genome sequences of three new strains of Staphylococcus arlettae named Bari1, Bari2, and Bari3 are presented. The strains exhibited tolerance to hexavalent chromium ions. ABSTRACT The genome sequences of three new strains of Staphylococcus arlettae named Bari1, Bari2, and Bari3 are presented. The strains exhibited tolerance to hexavalent chromium ions. An sprC gene encoding a putative chromium transporter was present in each of the three draft genome sequences.