Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory and Load-aware Traffic Rerouting (MLTR) in OpenFlow-based SDN

Because of today’s increased internet traffic, traditional networking nodes are facing more challenges to manage the network with control and monitoring activities while doing data forwarding. Software Defined Network (SDN) is a modern networking architecture anticipated with a mission of reducing the burden of traditional networking nodes by proposing split architecture. Though SDN promises novel network architecture that eases network management, it also suffers from several issues like scalability, security, provisioning of QoS (Quality of Service), failure detection and recovery. Recent studies reported that in a large-scale network, failures on the data plane occur more frequently and a link can fail every 30 minutes on an average. As this leads to data loss, failure detection and recovery is considered as the key task in such networks in order to avoid data loss. This paper addresses this issue and proposes a failure recovery technique that automatically reroutes the traffic with pre-configured backup paths in case of link or node failure in the working path. Also this work is intended to reduce the TCAM space occupied by flow rules in SDN switches and also to consider the loads on the networking switches. The pre-computed alternate backup paths are to be computed by the controller accordingly in order to address these two issues. It has been shown through different emulations that the proposed MLTR results in reduced average number of flow rules thereby reducing the memory space.
Automatic Generation of Pulmonary Radiology Reports with Semantic Tags

The chest X-ray is widely used in clinical practice for diagnosis and treatment. Among all chest diseases, pulmonary disease accounts for the majority, and the description of the lungs in the radiology report is the most complicated. According to this situation, this paper proposes a pulmonary radiology report generation model (SRN+BC-LSTM) based on semantic tags of radiograph. Firstly, for the problem that the image features extracted by CNN do not contain obvious semantic information, this paper selects the high frequency words related to abnormalities in the pulmonary radiology report as semantic tags, and trains the multi-label classifier. Secondly, a binary classifier is combined to improve the BLEU of generated normal reports since the Chinese radiology reports have roughly the same description for the normal samples. The experiment results indicated that our model is 13% higher than the baseline model in BLUE4.
Automatic Accounting of Working Hours in Small and Medium Enterprises

The article describes the implementation of the ZapTimer service, which is designed to record working hours and control labor discipline in small and medium-sized enterprises with a small staff of up to 100 people. Collecting during the working day data on the location of the employee relative to the office of the organization and analyzing the working schedule set for the employee, the service promptly provides managers with information on the actual hours worked and disciplinary violations of the employee (early care or being late for the workplace). Additional features of the service are the automatic filling of the time sheet with the possibility of making adjustments and scheduling employee holidays.
Analysis of Edge Computing Model for Real-Time Self-Organizing Push of Data in Wireless LAN Environment

Wireless self-organizing networks have received extensive attention and research because they do not require fixed infrastructure support. Due to the complexity of data transmission in edge computing, the core cloud data center is very far away from the user, resulting in a very large delay in obtaining data from the mobile terminal. In view of this, this paper provides a nearby computing and processing service for user services by deploying multiple mobile edge computing servers on the base station side near the user’s mobile core network edge. Firstly, this paper establishes an accurate DCF (Distributed Coordination Function) analysis model under unsaturated conditions. An analytical model for the statistical characteristics of service time is constructed. This model distinguishes the different effects of successfully transmitted packets and discarded packets. Secondly, this paper uses the maximum centrality theory of data aggregation transmission algorithm to determine the degree of influence of nodes by degree centrality theory to generate a fixed unbalanced spanning tree. On the basis of the spanning tree, a relatively large set of parallel nodes can be obtained, thereby reducing the delay time in the entire data transmission process. Finally, this paper studies two resource joint scheduling strategies of mobile terminals and MEC (Mobile Edge Computing) servers in mobile cellular networks based on MEC system. The performance of two collaborative scheduling strategies is verified by simulation. The simulation results show that the collaborative scheduling strategy proposed in this paper can effectively reduce the service cost of the system under the condition of ensuring the user service experience.
Design of EEG Based Wheel Chair by Using Color Stimuli and Rhythm Analysis

A novel methodology of brain controlled intelligent wheelchair by using color stimuli is proposed here. A general methodology is applied to find out most effective rhythm for color classification. Primary colors RGB and secondary color yellow were chosen for left, right, forward and stop command. Alpha, Beta, Theta, Delta rhythms were analyzed for three different subjects. Using dissimilar features of time and frequency domain twelve artificial neural network were built to decide the best rhythm. Principal component analysis was made for each EEG signal to remove the background effect of color stimuli. Comparing the findings it is visualized that beta rhythm is the most efficient rhythm with minimum mean square error of 4.845×10-9 among all designed ANN for color classification.
Sliding Mode for an All-Digital Control and Readout of MEMS Gyroscopes

Mobile devices and sensor networks advances have sharply increased the demand for inertial MEMS sensors with a direct digital output. This paper reports on the first implementation of a MEMS-based gyroscope system with all-digital control and readout, using sliding mode and bitstream processing for both the driving and sensing modes. The in-plane vibrating MEMS gyroscope, sensitive to out-of-plane external angular rates, was designed in a custom 50um SOI technology. A band-pass sliding mode control (SMC), equivalent to a digital PLL control, was used for electrically driving one of the resonant modes at its resonant frequency, and track its drift (due to environment variations). Simulations indicate a fast capture time in response to step variations (high tracking speed of 11,486 rad/s2) and small continuous tracking errors, below 0.0083 %. A regular (low-pass) sliding mode control was used for the sensing mode, in order to cancel its vibration and thus improve its linearity. The feedback loop action was proven to attenuate the displacement in the sensing mode by more than 100 times. The resulting bitstream output (that generates in the SMC loop the high-frequency cancelling electrostatic forces) contains the relevant information about the Coriolis force, and a bitstream synchronous demodulation technique is used to reconstruct the input angular rate to be measured, with a reconstruction error of ±0.2 rad/s. The overall system enables an easy future implementation on a MEMS+FPGA of a high-performance angular rate sensing microsystem with digital output.
How orthogonal are we? A note on fast and accurate inner product computation in the floating-point arithmetic

The multiply-and-accumulate (MAC) belongs to the most fundamental operations in digital signal processing. It constitute the key operation for digital filters as well as object classification, to name a few. Recently we faced implementation issues related to tensor decomposition into orthogonal factors, as well as tensor projections onto the orthogonal tensor bases. Also these rely heavily on the MAC operations. However, a serious problem of the numerical accuracy vs. operation speed of these operations can be observed when implemented with the floating point arithmetic realized in a hardware or a software platform. If not carefully approached, this can lead to significant numerical errors which are frequently overlooked by inexperienced engineers critically relying on standard libraries. In this paper we remind, dust off and discuss some computational aspects of the floating point implementation of the MAC operations in big data signal processing tasks on various platforms, and in different operation modes (serial, parallel, software, hardware). Such algorithms as simple summation, the Kahan algorithm, as well as some hybrid solutions are analyzed in respect to their accuracy, simplicity in implementation, and resource consumption, as well as speed of execution.
Detection and Classification of Cervical Exfoliated Cells Based on Faster R-CNN*

The detection and classification of cervical cells via Pap smear or Liquid-based cytology (LBC) have important clinical significance for pathological diagnosis. For the limitations of pathological images and the complexity of morphological changes of cells, the accurate classification of cervical cells remains a challenging task. DNA ploidy analysis can complete the classification and recognition of individual cells well, and misclassification is common because of the factors of inevitable imaging. Unlike traditional classification methods that rely on manual features, we proposed a region detection and classification method based on multi-semantic label combined with morphological information analysis. We followed the principle that a nucleus represents a cell and divided cells into five categories. The overlapping cell regions could be divided into multiple single cells. In this paper, the data pre-processing was made up for the imbalance in classification. The weight parameters obtained from ImageNet dataset pre-training were realized the transfer learning of cervical exfoliated cells based on Faster R-CNN on LBC dataset. The result showed that the mean average precision (mAP) was 66.98% and the accuracy was 91.61% by learning depth and superficial features of cervical exfoliated cells with ResNet-101 as the backbone. To some extent, it can assist pathologists in the early diagnosis of cervical cancer.
Incident Flux Based Monte Carlo Simulation of Silicon and GaAs FETs in Quasi-Ballistic regime

An incident flux based Monte Carlo particle simulation methodology has been developed which can be used to simulate any MOSFET. Incident flux Monte Carlo is a robust and intuitive method of device simulation because it can mimic the carrier transport in a physical device. The complete implementation has been done in an open source programming language called PYTHON. The simulations results are in good agreement with the existing results in the industry. The usability and advantage of our program have been shown by estimating backscattering coefficient, ballistic ratio and channel resistance of Silicon and GaAs quasi-ballistic devices. All the results that we have obtained can be used for better modeling of the electrical behavior of a quasi-ballistic device.
A Preliminary Experimental Study on an Axial Flux Permanent Magnet Generator for Regenerative Braking

The axial flux permanent magnet generators (AFPMG) are often found in wind power plants. The AFPMG generator construction allows the installation of large quantities of permanent magnets to improve efficiency at low shaft rotation. The ratio between the power to volume or mass is relatively higher compared to similar generators with radial construction. Therefore, the axial flux permanent magnet generators can be used as a vehicle component, for example, the regenerative braking. A preliminary experimental study was carried out to manufacture and to characterize a prototype of axial flux permanent magnet generator. The generator consists of a stator and two permanent magnet rotors. The stator consists of 4 poles of the coil which are arranged in series and each rotor has 32 poles of the permanent magnet. An electric motor with a variable speed drive is used to rotate the generator shaft. A dummy load consisting of several resistive loads serves as an artificial electrical load. Generators are characterized to obtain values of voltage, current, power, and efficiency. The characterization results showed that the generator voltage at 700 rpm rotation was 13.07 Vdc (achieved at the highest rotation in the experiment and without dummy load). The highest output currents and electric powers are 0.7 A and 3.96 W, respectively (700 rpm rotor shaft rotation). The highest efficiency is 35.90% resulting in 700 rpm rotation with a dummy load of 2 W.
Visual Analysis of the Time Management of Learning Multiple Courses in Online Learning Environment

Self-paced online learning not only provides the opportunities of learning anytime but also chanllenges students’ time management, especially in the context of learning multiple courses at same time. The inappropriate scheduling of multiple courses may affect student engagement and learning performance, thus how to arrange the study time of multiple courses is a concern of both instructors and students. Existing studies related to student engagement and time management in online learning mainly focus on providing self-regulated learning strategies and evaluating learning performance. However, these methods have limited abilities to gain intuitive understanding of the time management of multi-course learning. To address this issue, we present LearnerVis to help users analyze how students schedule their multi-course learning. LearnerVis visualize the temporal features of learning process, and it enables users to customize student groups to compare the differences in student engagement and time management. A case study is conducted to demonstrate the usefulness of the system with real-word dataset.
Joint Singing Pitch Estimation and Voice Separation Based on a Neural Harmonic Structure Renderer

This paper describes a multi-task learning approach to joint extraction (fundamental frequency (F0) estimation) and separation of singing voices from music signals. While deep neural networks have been used successfully for each task, both tasks have not been dealt with simultaneously in the context of deep learning. Since vocal extraction and separation are considered to have a mutually beneficial relationship, we propose a unified network that consists of a deep convolutional neural network for vocal F0 saliency estimation and a U-Net with an encoder shared by two decoders specialized for separating vocal and accompaniment parts, respectively. Between these two networks we introduce a differentiable layer that converts an F0 saliency spectrogram into harmonic masks indicating the locations of harmonic partials of a singing voice. The physical meaning of harmonic structure is thus reflected in the network architecture. The harmonic masks are then effectively used as scaffolds for estimating fine-structured masks thanks to the excellent capability of the U-Net for domain-preserving conversion (e.g., image-to-image conversion). The whole network can be trained jointly by backpropagation. Experimental results showed that the proposed unified network outperformed the conventional independent networks for vocal extraction and separation.
A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

Recently, there are a number of particle swarm optimization algorithms (PSOs) proposed for tackling multi-objective optimization problems (MOPs). Most of multi-objective PSOs (MOPSOs) were designed to speed up their convergence, which have been validated when tackling various kinds of MOPs. However, they may face some challenges for tackling some complicated MOPs, such as the UF test problems with complicated Pareto-optimal sets, mainly due to their neglect on the diversity. To solve the above problem, a novel hybrid MOPSO (called HMOPSO-ARA) is suggested in this paper with an adaptive resource allocation strategy, which shows a superior performance over most MOPSOs. Using the decomposition approach in HMOPSO-ARA, MOPs are transferred into a set of subproblems, each of which is accordingly optimized by one particle using a novel velocity update approach with the strengthened search capability. Then, an adaptive resource allocation strategy is employed based on the relevant improvement on the aggregated function, which can reasonably assign the computational resource to the particles according to their performance, so as to accelerate the convergence speed to the true Pareto-optimal front. Moreover, a decomposition-based clonal selection strategy is further used to enhance our performance, where the cloning process is run on the external archive based on the relevant fitness improvement. The experiments validate the superiority of HMOPSO-ARA over four competitive MOPSOs (SMPSO, CMPSO, dMOPSO and AgMOPSO) and four competitive multi-objective evolutionary algorithms (MOEA/D-ARA, MOEA/D-DE MOEA/D-GRA and EF_PD) when tackling thirty-five test problems (DTLZ1-DTLZ9, WFG1-WFG9, UF1-UF10 and F1-F9), in terms of two widely used performance indicators.
Analysis and Study of the Dynamic Processes of a Permanent Magnet Synchronous Motor with a Wide Range of Parameter Variations Using the Reference Model

During the operation of electromechanical systems, engine parameters may vary depending on destabilizing factors caused by operating conditions. Therefore, the analysis of dynamic processes at variations in the parameters of controlled permanent magnet synchronous motors is an urgent task. The article studies the transient processes of the permanent magnet synchronous motors with a wide range of parameter variations based on a linear integral criterion. A block diagram of the calculation of the specified criterion using the reference model is shown. In the work, determined its values and constructed the corresponding dependencies as a function of the relative change of parameters. The obtained graphs allow us to estimate the parametric impacts on the dynamic processes of the permanent magnet synchronous motor and the quality indicators of the automated control system as a whole.
Performance Analysis of Linear Precoding Techniques over the Fading Channel for MU-MIMO

Fast and continues development Wireless communication requires enhancement of capacity of system. To improve the capacity of system multiuser multiple inputs multiple outputs (MU-MIMO) is considered. It has ability system capacity improvement without additional bandwidth. In the implementation of MU-MIMO Multi User Interference (MUI) created and this interference degrade the system performance. Thus to minimize or suppress the interference, precoding technique used before signal transmission. Due to low complexity linear precoding preferred. For the massive system dimension, an appropriate precoding algorithm designing is difficult with less computing complexity and better error rate performance simultaneously over fading channels. This paper convey sum rate performance and bit error rate (BER) performance of linear precoding techniques above fading channels with assuming that the transmitter has perfect channel state information (CSI). To valuation of the bit error rate performance, different fading surroundings namely, Rician, Nakagami, and Rayleigh fading channel distributions offered.
Network Traffic Characterization Using L-moment Ratio Diagrams

5G networks are facing to new challenges related to the growing traffic volume and service diversity. Some of the major concerns in this new scenario are the security and privacy issues required for a full technology adoption. Traffic characterization is a compound of strategies intended to define formally the behaviour and patterns in the Internet traffic.In this work, we propose the use of statistical features of network flows to characterize some of the most common attacks in the current networks through the L-moment ratio diagrams. Our work identify the parameters that can discriminate normal from malicious traffic. Moreover, our preliminary results show that this technique enables the differentiation of anomalies and can also identify several types of attack traffic.
Implementation of a Highly-Sensitive and Wide-Range Frequency Measurement Using a Si3N4 MDR-Based Optoelectronic Oscillator

Microwave photonic technologies have been introduced for achieving broadband radio-frequency signal measurement. However, few of the proposed schemes mention the low-power radio-frequency signal detection, which stringently limits their practical applications in certain areas. In this paper, we designed and demonstrated a wideband low-power radio-frequency signal measurement system with optoelectronic oscillator. Here, the unknown radio-frequency signal matched the potential oscillation mode is allowable to be detected, amplified and estimated. The key component in the tunable optoelectronic oscillator is a silicon nitride micro-disk resonator with a very high Q-factor, which is utilized to achieve frequency selection as a microwave filter. A frequency measurement system range from 1 ∼ 20 GHz with radio frequency power as low as −105 dBm, measurement errors of ±375 MHz and the maximum gain of 61.7 dB were realized experimentally
Combination of Human and Machine Intelligence to Optimize Assembly

Current research and futuristic approaches including machine learning promise the wide and thorough use of measurement data in assembly processes for analysis and optimization. However, in current assembly lines measurement data is not available in every process, e.g. not in manual assembly processes. In addition, the integration and combination of data from different sources within the assembly line will require huge efforts during the next years. Therefore, a solely data based approach is not suitable for current optimization projects that usually have to react quickly to occurring challenges. Thus, the research project “MessMo - metrologically supported assembly” uses, benchmarks and combines approaches from machine learning and methodic thinking for modelling, cause-effect-identification and optimization. Three approaches for modelling are utilized, accompanied by one approach for data and process optimization.
Remote Sensing Data: Useful Way for the Precision Agriculture

The use of the remote sensing data to scheduling irrigation and fertilization and to predict yield may contribute to a more sustainable agriculture in Mediterranean regions, where irrigation and fertilization are not optimized in terms of timing and quantity. Data of remote sensing techniques using spectral and thermal approaches have been proposed as potential indicators to allowing rapid identification of crop nitrogen status, water stress and plants diseases across large areas. Given their versatility, remote sensing techniques have become valuable tools for precision agriculture, allowing to farmers to practice a more sustainable agriculture, minimizing risks of losing the harvest by providing the resources (water irrigation and fertilizer) needed to secure yield. Here, we review some remote sensing strategies and techniques used for a smart crop management and we discuss the useful of internet and computing programs to analyze the data of these techniques for precision agriculture. Nowadays, the use of software programs is an essential tool for the process and interpretation of data derived from remote sensing technologies, permitting a rapid and accurate farmer’s decisions to improved crop production and farmers’ incomes.