Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytochemical and Microbiological studies of PetreavolubiliesL

The aim of the present study was to evaluate the phytochemical screening of Petreavolubilies L for volatile oils, polyphenols and/or tannins, sterol and/or triterpenes, flavonoid aglycones and/ glycosides, carbohydrates and /or glycosides, and iridoid glycoside It is free from anthraquinone glycosides, alkaloid, saponins, resins and oxidase enzyme. Phenylethanoidsverbascoside, eukovoside and cistanoside D were isolated and identified by co- chromatographic and specteoscopic methods. Antimicrobial properties of n-butanol fraction and pure compound were evaluated against Escherichia coli ATCC 14169, Pseudomonas aeruginosa ACCT 9027, Staphylococcusaureus ATCC 6538, Bacillus subtulis ATCC 6633, Micrococcus leutus ATCC9341, Aspergillusniger and Candida albicans ATCC 10231 by the disc diffusion method. (Gouda T. M. Dawoud and T. H. El-Morsy. Phytochemical and Microbiological studies of PetreavolubiliesL. J Am Sci 2012;8(8):202-208). (ISSN: 1545-1003). http://www.jofamericanscience. org . 31
6S-2 RNA deletion in the undomesticated B. subtilis strain NCIB 3610 causes a biofilm derepression phenotype

ABSTRACT Bacterial 6S RNA regulates transcription via binding to the active site of RNA polymerase holoenzymes. 6S RNA has been identified in the majority of bacteria, in most cases encoded by a single gene. Firmicutes including Bacillus subtilis encode two 6S RNA paralogs, 6S-1 and 6S-2 RNA. Hypothesizing that the regulatory role of 6S RNAs may be particularly important under natural, constantly changing environmental conditions, we constructed 6S RNA deletion mutants of the undomesticated B. subtilis wild-type strain NCIB 3610. We observed a strong phenotype for the ∆6S-2 RNA strain that showed increased biofilm formation on solid media and the ability to form surface-attached biofilms in liquid culture. This phenotype remained undetected in derived laboratory strains (168, PY79) that are defective in biofilm formation. Quantitative RT-PCR data revealed transcriptional upregulation of biofilm marker genes such as tasA, epsA and bslA in the ∆6S-2 RNA strain, particularly during transition from exponential to stationary growth phase. Salt stress, which blocks sporulation at a very early stage, was found to override the derepressed biofilm phenotype of the ∆6S-2 RNA strain. Furthermore, the ∆6S-2 RNA strain showed retarded swarming activity and earlier spore formation. Finally, the ∆6S-1&2 RNA double deletion strain showed a prolonged lag phase of growth under oxidative, high salt and alkaline stress conditions, suggesting that the interplay of both 6S RNAs in B. subtilis optimizes and fine-tunes transcriptomic adaptations, thereby contributing to the fitness of B. subtilis under the unsteady and temporarily harsh conditions encountered in natural habitats.
A Neuro-Fuzzy Based Approach for Resting-state Detection Using A Consumer-grade EEG

This work aims to introduce a methodology for resting-state brain activities detection by a consumer-grade EEG. From one hand, an adaptive noise reduction methodology based on non-linear Principal Component Analysis Neural Network is adopted. On the other hand, a Neuro-Fuzzy model (i.e., Fuzzy Relational Neural Network) is considered for brain activities detection since a combination of neural networks and fuzzy technology enhances the performance of control, decision-making and data analysis systems. Experiments are made on a corpus containing the activation strength of the fourteen electrodes of an EEG headset for eye state detection. We proved that by using the noised signals, the proposed methodology permits to obtain a high rate of classification accuracy.
Intelligent Energy Management Systems for Optimal Techno-Commercial Benefit in DC Micro-grids: A Review

The demand for clean, high quality and low rate Electrical Energy is on rising scale in the last decade. Both the traditional ways of generating power and delivering the same through long transmission lines are less appreciated due to the cost associated and also the detrimental effects on environment. Renewable Energy Sources (RES) may be achieved clean if smart integration of the same. Present micro-grids are supported by energy management systems (EMS) which only optimise resource management to supply continuous power to the consumers. The economic considerations such as costing profile of the sources, demand response and price discovery along with technological considerations like power quality assessment and standardisation is however, absent in these systems. Moreover, the EMS developed are location and operational condition specific that is these EMS cannot be programmed as per the requirement of the user. Hence the present EMS in use, in islanded or off-grid condition may not select the appropriate source to cater the demand requested and also is not flexible enough. Also the EMS can be developed in a way that all the information and constraints can be programmed in it. Some optimisation techniques can be used to solve the objective function. In addition to this, the intelligent EMS is proposed to improve the power quality by continuously monitoring a few standard performance indices. Though the work is based on off grid independent micro grid but the EMS will be developed with such an algorithm for both mode of operation -islanded and conventional so that which can provide benefits for off-grid operations also.
An Iteratively-Improving Internet-of-Things Honeypot Experiment

In this paper we present a prototype implementation of an iteratively improving low-interaction Internet-of-Things (IoT) honeypot, based on serving responses of real IoT devices obtained through IoT search engines, as well as devices and services under our own control. The experiment was designed to confirm if this is a viable approach to mimicking a heterogeneous group of blackbox devices. In the experiment we focused on only one of the protocols used in the IoT world, the Hypertext Transfer Protocol (HTTP), primarily due to widespread use and mature tooling. Our findings show that it is trivial to learn enough responses to induce deeper probing, and that some of the knowledge discovered in this way could not have been obtained by using any other publicly available resources.
End-to-End Speech Emotion Recognition With Gender Information

Many works have focused on speech emotion recognition algorithms. However, most rely on the proper selection of speech acoustic features. In this paper, we propose a novel emotion recognition algorithm that does not rely on any speech acoustic features and combines speaker gender information. We aim to benefit from the rich information from speech raw data, without any artificial intervention. In general, speech emotion recognition systems require manual selection of appropriate traditional acoustic features as classifier input for emotion recognition. Utilizing deep learning algorithms, and the network automatically select important information from raw speech signal for the classification layer to accomplish emotion recognition. It can prevent the omission of emotion information that cannot be direct mathematically modeled as a speech acoustic characteristic. We also add speaker gender information to the proposed algorithm to further improve recognition accuracy. The proposed algorithm combines a Residual Convolutional Neural Network (R-CNN) and a gender information block. The raw speech data is sent to these two blocks simultaneously. The R-CNN network obtains the necessary emotional information from the speech data and classifies the emotional category. The proposed algorithm is evaluated on three public databases with different language systems. Experimental results show that the proposed algorithm has 5.6%, 7.3%, and 1.5%, respectively accuracy improvements in Mandarin, English, and German compared with existing highest-accuracy algorithms. In order to verify the generalization of the proposed algorithm, we use FAU and eNTERFACE databases, in these two independent databases, the proposed algorithm can also achieve 85.8% and 71.1% accuracy, respectively.
Swarm Collective Wisdom: A Fuzzy-Based Consensus Approach for Evaluating Agents Confidence in Global States

Consensus achievement is a class of problems in which a group of agents, such as a swarm, needs to collectively reach a common decision to select one of the available options. Many consensus achievement strategies were proposed in which an agent forms its opinion and exchanges it with the other agents to reach a collective decision. To facilitate the decision making process, agents which are highly confident in their opinions are commonly given a higher chance to influence the collective’s decision making. However, the use of subjective metrics for confidence could degrade the performance of the state-of-theart algorithms in complex scenarios where agents with wrong opinions can be the most confident. To tackle this problem, we propose an objective metric for confidence by using experience to learn the mapping between the information available to an agent and the probability that the agent’s opinion is correct. To compute its confidence level, an agent feeds data from its local observations, as well as the received neighbours’ opinions, into a fuzzy inference system (FIS) that uses these inputs to estimate confidence. The proposed strategy is distributed and it requires the agents to communicate locally using messages containing only their ID and opinions. Our strategy is evaluated under scenarios with different levels of complexity. The results show that our algorithm outperforms the state-of-the-art algorithms in terms of its accuracy, task time, and ability to reach majority. The proposed approach was also shown to maintain its success, even in the most complex environments.
Pose Estimation from Electromyographical Data using Convolutional Neural Networks

This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predict the hand pose with an error rate of 4.6% for the EMG model, and 3.6% when accelerometry data is included. This shows that hand pose can be effectively estimated from EMG data, which can be enhanced with accelerometry data.
Utilizing Network Topology Cognizance Procedure in SDN-based WSN: An Experimental Study

As a means to take advantage of the full capacity of the SDN paradigm in WSN, a network topology cognizance protocol NTCP needs to be developed. The purpose of this protocol is to prepare each end device in the network to be aware of its environment in term of other nearby end devices, then by SDN other procedures, convey this awareness to the SDN Controller to be used in finding the best route for each data transmission request occurs in the network. The SDN protocols structure was evaluated using a simulation environment and an experimental one. End to end delay and the number of dying end devices over time are the two parameters that were utilized to evaluate the developed framework. The experimental setup included a TI ARM MCU with a protocol stack for wireless applications that was suitable to develop our proposed protocol stack on top of it. The results have shown promising potential for the proposed framework in terms of energy efficiency, network management, time restricts.
Area-Efficient Series-Connected Resonant Tunneling Diode Pair as Binary Neuron in Cellular Neural Network

In this letter, we propose an area-efficient series-connected resonant tunneling diode pair device (SCRTD) to utilize as a binary neuron device in cellular neural networks (CNNs). The proposed SCRTD (P-SCRTD) consists of two RTDs interconnected by a floating metal layer with a high-doped epitaxial layer. The P-SCRTD shows a 48 % reduction in area by eliminating the isolation area in the conventional SCRTD (C-SCRTD) owing to the symmetrical I-V characteristic of the RTD, without any performance penalty on the DC and RF characteristics by means of adopting the floating metal layer, compared to the C-SCRTD. CNN application of the P-SCRTD is investigated, showing inherent threshold operation and high-speed (12.5 Gbps) low-power (21 mW) performance, despite using the $2~\mu \text{m}$ technology node, for a unit cell for edge extraction.
Symbolwise MAP Estimation for Multiple-Trace Insertion/Deletion/Substitution Channels

In this paper, we work on the symbolwise maximum a posteriori probability (MAP) estimation of channel input symbol from m (≥ 2) received words (traces) having inser- tion/deletion/substitution (IDS) errors, where the errors are independent between m received words. This problem is motivated by readout process in next-generation sequencers of DNA storage. The MAP estimation is based on the belief propagation algorithm on a factor graph, which represents the joint probability of a channel input word and m pairs of channel output word and drift vector. We also propose a heuristic estimation algorithm for m ≥ 4 to reduce the computational complexity. Simulation results show the relation between channel IDS error rate and estimation error rate.
Hypothesis Testing Against Independence Under Gaussian Noise

We study a variant of the many-help one hypothesis testing against independence problem in which the source, not necessarily Gaussian, has finite differential entropy and the observation noises under the null hypothesis are Gaussian. Under the criterion that stipulates minimization of the Type II error exponent subject to a (constant) bound on the Type I error rate, we derive an upper bound on the exponent-rates function. The bound is shown to mirror a corresponding explicit lower bound, except that the lower bound involves the source power (variance) whereas the upper bound has the source entropy power. Part of the utility of the established bound is for investigating asymptotic exponent/rates and losses incurred by distributed detection as function of the number of observations.
eXP-RAN—An Emulator for Gaining Experience With Radio Access Networks, Edge Computing, and Slicing

Radio Access Networks (RAN), Edge Computing (EC), and Network Slicing are some of the critical components in 5G networks used to provide different transport services over the same infrastructure and to enable new features, e.g., low latency and context awareness. It is important to have tools for evaluating present and future applications over these components; however, there is a gap between theoretical work on resource allocation and its deployment in simulated, emulated, or real-world experiments. This paper addresses these issues through a new emulation software named eXP-RAN, which enables experimenting with network slicing in virtualized RAN nodes and EC scenarios. In this version, we have focused on the computing part of RANs. Experiments can be created manually or imported from an optimization model. We describe the software architecture and its advantages over traditional tools such as ns-3 and Mininet, as well as more recent tool targets to EC simulations such as EdgeCloudSim. We also illustrate how two use-cases, a virtual RAN (vRAN) / Multi-Access Edge Computing (MEC) orchestration and a network slicing for video service providers can be emulated and explored using eXP-RAN.
Sparse Plane-wave Decomposition for Upscaling Ambisonic Signals

Lower order ambisonics suffers from low spatial resolution, where hardware complexity is high for direct recording the higher order ambisonics (HOA). This problem can be solved by upscaling the order-l ambisonics (B-format signals). In this paper, a sparse plane-wave decomposition method using sequential matching pursuit is developed for upscaling the order of ambisonics. The proposed method maintains the same sparsity level across multiple measurements and is computationally efficient. The performance of the proposed method is evaluated based on the error in encoded signal and reconstructed sound field, and compared with the state-of-art upscaling techniques. Perceptual evaluations are also conducted, which indicates a significant improvement in spatial resolution.
Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.
Efficiency Enhancement of a Thermophotovoltaic System Integrated With a Back Surface Reflector

This work aims to investigate the various factors which may affect a thermophotovoltaic (TPV) system’s performance, with a special focus on the importance of incorporating a back surface reflector (BSR), which enables below-bandgap photons’ recycling. The possible extent to which common PV materials can be used in TPV applications is investigated by comparing them on a Planck distribution curve. The effects of varying BSR reflectivity, TPV cell’s external quantum efficiency, and emitter temperature are investigated on the TPV module’s efficiency using open-circuit voltage, empirical relations for fill factor, maximum voltage, and photogenerated current. It is shown that TPV applications require materials with smaller (e.g. 0.6 eV g ≥ 0.74 eV) bandgap energy, e.g. In0.53Ga0. 47As (0.74 eV), due to their high percentage of energy (>26%) above-bandgap without a spectral control and a small difference between peak and bandgap wavelength. It is shown that the inclusion of a BSR (reflectivity = 1) results in an increase of 15% in TPV efficiency. The results show that by the collective changes of an added BSR, high emitter temperature (>2000 K), and improved external quantum efficiency (EQE ≈ 1), the present TPV systems can attain efficiency values more than 30% which makes them a favorable prospective choice for Concentrated Solar Power.
Statistical Human Body Shape Model including Elderly People

In this study, we present a human body shape statistical model including elderly people, which is constructed using principal component analysis (PCA) on 3D body scan data of approximately 130 people. As a pre-process step, a template human body mesh model is fitted to 3D scan data using a coarse-to-fine surface registration technique based on a conformal deformation method, in order to establish correspondences between the scans of different subjects possibly in different poses. To change body style by a small set of parameters, such as "age", "weight" and "height" or the easily measurable anthropometric parameters like "shoulder width", the linear transformations between these attributes and the first 10 principal component scores are obtained. We design a simple user interface to use this deformation model to generate different body styles easily. As a result, we were able to produce and show body styles capturing the characteristics of elderly people whose shoulders fell and back bent. Finally, as an application, we used our deformation method to generate different body types, performed forward dynamics simulations in an assistive device setting and visualized the differences in contact pressure distributions due to body shape changes.
Performance Analysis of Sub-10nm Vertically Stacked Gate-All-Around FETs

Gate-All-Around Nanosheet Field Effect Transistor (GAA-NSFETs) have emerged as the solution to avoid short channel effects (SCEs) at the 10-nm technology node and beyond. In this paper, we have investigated vertically stacked NSFETs from electrical aspects as the degradation in drive current is a significant concern for GAA-NSFETs. We evaluate the impacts of geometrical variations, doping concentration, height and width of the nanosheet on the performance of triplechannel vertically-stacked NSFETs. Each design parameter has been analyzed through various figure-of-merits (FOMs) such as the threshold voltage (VTH), on-state current (ION), off-state current (IOFF), subthreshold slope (SS) and drain induced barrier lowering (DIBL). Finally, the enhancement in current drivability can be possible by minimizing the NS doping concentration and increasing both the nanosheet height and width.
Design of the convolution layer using HDL and evaluation of delay time using a camera signal

Research of the deep-learning has been applied to several problems, such as image recognition, voice recognition, and natural language processing. Those are achievement results in the world. At the same time, it is used in various applications and research fields. This technology is expected for using embedded systems of IoT devices such as smartphones and tablet-pc. This technology is expected for using the embedded system of IoT devices such as smartphones and tablet-pc. Deep-learning requires large computational resources and high power consumption. GPU is a kind of solution to computational resources. In general, most systems need to use GPU acceleration. However, it is a hard point to implement on tiny embedded devices under the required GPU. Also, in recent years, FPGAs have been applied to image processing technology fields such as defect inspection of automobiles, security systems, and industrial products. Hardware acceleration is one of the techniques to improve processing speed. This technique is often used in the field of image processing. Our research tried to design the hardware acceleration approach for the convolutional neural network and we compared the software processing approach and our hardware approach. The software processing approach is conventional. As a result, our hardware-based convolutional layer can show high-speed performance than the software-based design. These hardware-based modules were implemented on FPGA. We used HDL(Hardware Description Language) to implement on FPGA. By using this, you can create a logic circuit like programming. It is usually used when designing the FPGA circuit. In this paper, we show the detail of the architecture and the comparison result of processing time with CPU.
Automatic Fingerprint Extraction of Mobile APP Users in Network Traffic

Network operators often need a clear visibility of the mobile APPs and their user scales running in the network traffic. This is critical for network management and network security. Analysis of the network traffic using the extracted APP features and user fingerprints is helpful for effective network operations, management, and security monitoring of LANs, MANs, and WANs. In China, the number of mobile APP users continues to increase, and the proportion of Internet users using mobile APPs to access the Internet far exceeds that of computers, making this task significant and difficult. The traditional methods are mainly APP identifications or identifications of specific APP users, which cannot satisfy the requirements of globally monitoring of APPs and their user scales at the same time. Especially when many users share the same network IPs (4G, home broadband, NAT), this work becomes challenging and time-consuming. This paper proposes an automatic fingerprint extraction approach of mobile APP users in network traffic. By analyzing the plaintext of the HTTP requests initiated by APPs in training datasets, we extract the APPs’ features and the users’ fingerprints simultaneously. Both of them are the combinations of strings which are distinguishable of APPs and their users in the network traffic. The proposed method is evaluated with the top 49 popular APPs in Huawei App Store. The experimental results show that the recalls of the extractions of APPs’ features and users’ fingerprints are respectively 77.5% and 55.1% in total.