Miracle-dz/UniFAR-FTUs huggingface dataset:
An IoT based Solar Park Health Monitoring System for PID and Hotspots Effects

With solar parks being established as one of the most important renewable energy systems, there is a strong need for more efficient use of the services they provide, as well as error detection and performance issues confrontation. Internet of Things (IoT) technology, aims to fill the gap, by offering low cost and sustainable solutions towards the efficient operation of these parks. In this paper, we present an in situ monitoring and alerting system, based on WSN technologies, regarding the early detection of Potential Induced Degradation (PID) and Hotspots failures, that can cause a significant drop in solar panels’ performance. In order to do so, specific non-trivial attributes such as temperature, humidity, irradiance, current and voltage are continuously monitored at panel level, and processed in a cloud based platform to early identify these phenomena. In particular, sensor nodes send data to a centralized local sink module using a multi-hop WSN architecture, in order to establish a robust and large coverage area. Afterwards, the information is propagated to the cloud server, where deterministic diagnostic algorithms are applied. We present the reference architecture of our approach, alongside the corresponding hardware and software structural, individual components, as well as the integration process and the use case that runs over a real solar park.
Alarm text analysis based on convolution neural network and LSTM network feature fusion*

Convolutional neural networks (CNN) and recurrent neural networks (RNN) are widely used in natural language processing. However, the natural language has a certain dependence on the structure. A single CNN model leads to ignore the semantic and grammatical information of words. Traditional RNN has gradient disappearance or gradient dispersion problem. For this reason, the paper designs the CNN-BLSTM network in the way of network level combination, introduces Attention mechanism, and proposes a CNN-BLSTM +Attention model. The fusion model effectively deals with the position invariance of local features and extracted efficient local feature information. Furthermore, Attention mechanism is introduced to automatically weigh the output sequence information at each time to reduce the loss of key features when RNNs are used to model the sequence features. The feature extraction in time and space is completed. The experimental results show that the accuracy of the proposed model is 3 to 4 percentage points higher than that of other models. When dealing with alarm text, the model not only guarantees the local correlation of data, but also strengthens the effective combination ability of sequence features.
A Reliable Method for Removing Fabry–Perot Effect in Material Characterization With Terahertz Time-Domain Spectroscopy

Terahertz Time-Domain Spectroscopy (THz-TDS) has been a powerful tool for material characterization. The Fabry–Perot (FP) effect caused by the multiple reflections within the sample exists inherently, which makes the echoes contaminated. In the case of thin samples, different reflection signals may merge together and even the echoes corresponding to the main reflection disappears, making the characterization improper or impossible. To deal with this problem, a novel time-domain optimization method is proposed for removing the FP effect. By introducing a new term which describes the variation of the waveform in time-domain, not only the complex refractive index but also the thickness of material are obtained for a very thin silicon wafer (∼100 μm) whose thickness is less than 2/3 lambda at the central frequency of the THz-TDS system. The accuracy of the algorithm is quite comparable to an iterative method, which is considered to be the most accurate one at the moment, but it is more than 100 times faster, with a better convergence performance. Hence, it is promising for rapid material scenarios such as the in-line quality control and imaging-related applications.
Hexacopter Flight Performance Comparison with CCA vs. WCA Control Allocation

This paper presents an extensive discussion between two control allocation methods for hexacopters. The first one is the so-called classical control allocation (CCA) based on a pseudo-inverse matrix, and the second one is the weighted control allocation (WCA) based on a weighted pseudo-inverse matrix. Control allocation is about mapping the virtual control vector $\nu=[T,\ L,\ M,\ N]^{\top}$ made of thrust and roll, pitch and yaw torque, respectively, to the individual motors' speed. The CCA method is studied under motor saturation circumstances, in which CCA can lead to a crash. The WCA method on the other hand displays better performance than CCA for: a) motor saturation, b) vehicle's mass changes, 3) flight-controller poorly-tuned parameters, and 4) motor-fault tolerance. The performance comparison between these two control allocation techniques is made through simulations and real-flight tests.
Machine Vision On-line Detection System: Applications and Standardization Requirements

Machine vision on-line detection is the key development direction of a new generation of artificial intelligence technology to promote intelligent upgrading of manufacturing industry. It has been widely used in processing assembly, quality inspection, in-plant logistics and other aspects to enhance product quality and production efficiency. This paper sorts out the basic concepts, system framework, application cases and standardization progress of machine vision on-line detection system, and summarizes the corresponding standardization requirements.
Online Multiple Outputs Least-Squares Support Vector Regression Model of Ship Trajectory Prediction Based on Automatic Information System Data and Selection Mechanism

Existing maritime trajectory prediction models are faced with problems of low accuracy and inability to predict ship tracks in real time. To solve the above problem, an online multiple outputs Least-Squares Support Vector Regression model based on selection mechanism was proposed: (a) converting the traditional Least-Squares Support Vector Regression’s single output to multiple outputs, aiming at the problem that the single-output of the traditional Least-Squares Support Vector Regression model is difficult to apply to complex multiple features prediction scenarios, (b) reducing the high computational complexity of matrix inversion calculations using an iterative solution, in order to solve the problem of poor real-time performance, (c) determining whether to use online model based on the characteristics of different trajectories, and (d) removing initial samples least affecting the model to alleviate the impact of large increases in the number of new samples on computational complexity. The model was simulated using the automatic identification system tracks of Tianjin port in March 2015. The calculation accuracy and efficiency of this model was verified by comparing the predicted results of the proposed model with the recurrent neural network–long short-term memory, back propagation neural network, and traditional Least-Squares Support Vector Regression models. In sum, the proposed model is highly accurate in online and real-time prediction of a target ship’s trajectory when sailing at sea. In particular, it can sustain high prediction accuracy in the case of smaller data samples. The real-time predicted trajectory can assist the generation of ship collision avoidance decision-making.
Integrated report on brain tumor detection from MRI Image using Prewitt horizontal edge-emphasizing filtering technique

Researchers in image processing engineering are currently designing devices to identify tumors and process diagnostic images. Medical image segmentation is used to identify tumors which is an effective method. The goal of this project is to identify brain tumors from MRI images using the GUI framework. Using the interface, we use mathematical simulation modeling software will help us to use in several combinations of segmentation, filterations and other image processing algorithms to produce an exciting result. In this work, we utilize by the method of filtering the image using the Prewitt horizontal edge-strengthening filter. Followed with the tumor identification method known as "watershed pixels" for various developments of the images.
Stock Reversal Pattern Mining Based on Fuzzy Candlestick Lines

The stock financial time series contains information on the objective laws of the stock market, which reflects the psychological changes of investors to some extent. Candlestick lines theory is an empirical model of investment decision-making. The candlestick-line model reflects market psychology, and investors can make investment decisions based on the identified candlestick-line model. In view of the numerous and complicated parameters of the existing candlestick-line mining technology, it is difficult to be understood by the general investors. It is proposed to apply fuzzy logic to the traditional candlestick-line graph theory. Based on the definition of the stock price reversal trend, the fuzzy candlestick-line reversal feature is used to Reverse mode to classify. The empirical research on the two securities markets of SSE A shares and Shenzhen Stock Exchange A shares shows that the fuzzy logic candlestick-line reversal mode proposed in this paper exists and can be classified and identified.
Optimal Control of Variable Speed Air Conditioning System Fed by Solar PV System

Solar Air condition is one of attractive application solar energy systems; in this paper we present an approach of sizing, control and optimization of air conditioner fed by photovoltaic generator based on BLDC motor and rotary compressor, for optimization system operation MPPT (maximum power point tracking) control and speed control of BLDC motor is used, the simulation results under Matlab /Simulink show effectiveness of used method when variable speed (flow) is needed, This study make a path to improve operation of solar fed air condition system driven by electrical motor
Lig-Doctor: Real-World Clinical Prognosis using a Bi-Directional Neural Network

Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, can predict probable future conditions. Previous works have tackled the problem by using artificial neural network architectures that do not benefit from bi-directional temporal processing, or by utilizing non-generalizable inference approaches. Differently, we introduce a Deep Learning architecture whose design results from an intensive experimental process; our final architecture is based on two parallel Minimal Gated Recurrent Unit networks working in bi-directional manner, which was extensively tested with two real-world datasets. Our results demonstrate significant improvements in automated medical prognosis, as measured with metrics Precision@, Recall@, F1-Score, and AUC-ROC. We contribute with an architecture and with insights for the design of Deep Learning architectures.
Evaluating the Energy Efficiency of Software Defined Networking Controllers for Different Topologies

The increase in the number of connected devices in network deployments has resulted in network management becoming a tedious process. While the Software Defined Networking (SDN) paradigm was made primarily to deal with the tedious management issue by placing all control functionality on the controllers, this results in the controller being loaded with tasks and ends up consuming a greater amount of energy for managing the network. This leads to an increased cost for managing the network. Many works have tried to compare the performance of SDN controllers, while only few have considered the energy efficiency. However, the energy efficiency of SDN controllers when subjected to different topologies remains an open issue. This paper looked at the energy efficiency of five SDN controllers using different topologies. Mininet is used for topology emulation and Powerstat for the power consumption readings of the controllers. The results of this study show that POX and RYU controllers perform better than Beacon, Floodlight and OpenDaylight controllers in the achieved energy efficiency for all the topologies considered.
Context-aware robotic arm using fast embedded model predictive control

The growing number of collaborative robotics in unstructured environments creates highly nonconvex nonlinear shared dynamical systems. For safety and speed, path planning and collision avoidance are of the utmost importance in these situations. We present a novel nonlinear MPC solution for use on a three-dimensional four-axis robotic manipulator. The system is the first of it's kind to take into account moving obstacles. Using the OpEn framework, optimisation is done by the PANOC and ALM techniques. Experimentation demonstrates extremely fast solver times on both PC and embedded platforms.
Menin-mediated Repression of Glycolysis in Combination with Autophagy Protects Colon Cancer Against Small-molecule EGFR Inhibitors

Menin serves both tumor suppressor and promoter roles in a highly tumor-specific manner. In colorectal cancer, menin is overexpressed and plays a critical role in regulating transcription of SKP2, and combined treatment with a menin inhibitor and small-molecule EGFR inhibitor (EGFRi) leads to synergistic killing of colorectal cancer cells. However, the full spectrum of menin function in colorectal cancer remains uncertain. Herein, we demonstrate that menin inhibition increases glycolysis in colorectal cancer cells. This menin inhibitor–induced increase in glycolysis occurs in an mTOR-independent manner and enhances the sensitivity of colorectal cancer cells to EGFRis. In addition, we show that EGFRis induce autophagy in colorectal cancer cells, which is important for cell survival in the setting of combined treatment with an EGFRi and menin inhibitor. Inhibition of autophagy with chloroquine further sensitizes colorectal cancers to treatment with the combination of an EGFRi and menin inhibitor. Together, these findings uncover a novel role for menin in colorectal cancer as a repressor of glycolysis and demonstrate that menin inhibitor–induced increases in glycolysis sensitize colorectal cancer cells to EGFRis. In addition, these findings illustrate the importance of autophagy as a protective mechanism against EGFRis, especially in the presence of menin inhibition. Ultimately, these data open the possibility of using menin-mediated regulation of glycolysis to potentially improve treatment modalities for colorectal cancer.
On Adaptation of Cancelation Path Modeling Filter in Single-Channel Feedback-Type Adaptive Active Noise Control Systems

This paper addresses identification of the cancelation path (CP) during the online operation of the feedback-type active noise control (ANC) systems. In the feedback-type ANC, a reference microphone cannot be installed to pickup the reference signal for the (adaptive) ANC filter. Therefore, an appropriate reference signal must be electronically generated. This requires availability of a good estimate of the CP. Furthermore, estimation accuracy of CP affects the stability and noise reduction of the ANC systems. The method proposed in this paper develops upon a previous work where two adaptive filters were used, one as ANC filter and the other as a cancelation path modeling filter (CPMF). The proposed method essentially comprises a similar structure as that of the previous method. The key objective here is to improve upon the slow convergence of CPMF. This in turn improves the noise reduction performance as demonstrated by numerical results presented in this paper.
Masking based Spectral Feature Enhancement for Robust Automatic Speech Recognition

The accuracy of the automatic speech recognition (ASR) system will decrease under reverberant conditions. Feature enhancement as the front-end of an ASR system can improve the performance in adverse environments. In this research work, we propose modified masking based deep learning method to enhance the log Mel filterbank coefficients of reverberant speech. We eliminate the zero-value amplitude output by adding an adjustment layer to the DNN, thus improving the accuracy of spectral feature estimation. As the masking method relies on the human hearing mechanism, its feature enhancement effect is superior than the state-of-the-art spectral feature mapping method. ASR experiments with the REVERB 2014 stereo (reverberant and clean) data proved that the proposed method achieved excellent results.
An Energy-Efficient Low-Noise Complementary Parametric Amplifier Achieving 0.89 NEF

This paper presents an energy-efficient low-noise amplifier, achieving 0.89 noise efficiency factor (NEF) using discrete-time parametric amplification. A complementary parametric input stage is proposed to eliminate any DC shift between the input and output voltage levels. The power consumption is reduced by adopting a 34-phase stepwise charging technique and the sampling-induced input-referred noise is minimized by oversampling the input signal. Time- interleaving multiple parallel stages allows implementing the multiphase soft-charging scheme without causing any significant penalty in terms of area. A second current-reuse inverter-based amplification stage is designed to provide high gain, while its contribution to the total input-referred noise is attenuated by the preamplifier gain. The overall NEF is 1.76 and the total power consumption is 1.73 µW.
Piecewise Model-Based Online Prognosis of Lithium-Ion Batteries Using Particle Filters

Lithium-ion batteries are used as energy sources for energy storage systems, electric vehicles, consumer electronic devices and much more. Prediction of the remaining useful life (RUL) of such sources is vital to improve the safety and reliability of battery-powered systems. Even though several prognostic methods have been extensively explored for the RUL prediction of lithium-ion batteries, these methods are focused on adopting a single empirical / phenomenological degradation model which best describes the degradation behavior. However, certain lithium-ion battery materials exhibit two distinct degradation behaviors with an evident inflection point. In such cases, a single empirical model no longer holds good. Hence, we propose a piecewise degradation model along with a novel methodology to determine the inflection point. The proposed model is incorporated into a particle filter framework to predict the battery’s degradation trajectories. The effectiveness of the proposed model is verified by adding a 50dB noise to the measurement data. The prognostic results of the proposed piecewise model are compared with the existing single empirical model. We use prediction error and execution time as the prognostic metrics for comparison.
Application of Deep Residual Neural Network to Water Meter Reading Recognition

Aiming at water meter reading pictures taken at arbitrary angles in various complex environments, we propose a robust automatic identification method. This method is based on a deep residual neural network, uses a self-made water meter reading image data set, uses an inverse gradient stochastic descent method to optimize the weights of the RFCN model, and is trained by the network. After repeated iterations, the RFCN model for target detection of water meter reading characters is finally obtained, and then this model is used to detect and identify the characters in the water meter reading box in the water meter picture, and the output result is sorted by the central sort algorithm to achieve a water meter reading Method of identification. The experimental verification of this method shows that its recognition rate is fast and the accuracy is high, which can meet the needs of general companies.
Digestibility of Juncus acutus and its effects on ruminal cellulolytic bacteria

Abstract The objectives of this study were to estimate the digestibility of Juncus acutus and to investigate its effects on rumen cellulolytic bacteria to consider J. acutus as an alternative roughage source in ruminant nutrition. Juncus acutus samples were collected from three different stations in Kizilirmak Delta and their proximate analysis was carried out. Organic matter digestibility (OMD) and metabolisable energy (ME) values of J. acutus were estimated from gas measured by the in vitro gas production method. The effects of J. acutus on the abundance of rumen cellulolytic bacteria Fibrobacter succinogenes, Ruminococcus flavefaciens and Ruminococcus albus were determined by real-time PCR method. Crude nutrient values of J. acutus collected from three stations were analysed. Mean OMD %, MEOMD and MEGP values of J. acutus from three stations were ranged from 42.64 to 42.48%, 6.78 to 6.82 MJ/kg DM and 5.05 to 5.26 MJ/kg DM respectively. Ruminal F. succinogenes, R. flavefaciens and R. albus abundance calculated from threshold (CT) values in rumen fluids obtained from 0 to 96 h incubations showed an increases in following order F. succinogenes> R. flavefaciens> R. albus. The CP % of J. acutus was found higher from cereal straw and close to low-quality alfalfa hay and dry meadow grass. Juncus acutus increased the amount of rumen cellulolytic bacteria. In the light of obtained results, it can be concluded that J. acutus may be considered as a new alternative roughage source in ruminant nutrition.
A New Type of N-type TFET with Tri-input Terminals Using T-shaped Structure

In this paper, a new type of tunneling field effect transistor (Ti-TFET) with tri-input terminals is proposed. The ingenious T-shaped channel (T-channel) structure enhances the control ability between any two gates of all the three gates to device channel, and thus increasing its turn-on current when any two gates are biased as 1 V. The turn-on current, leakage current and the switching ratio of turn-on/turn-off current are optimized by choosing appropriate work function and body thickness. The TCAD simulation results show that “Majority-Not” logic function can be compactly implemented by using a single Ti-TFET transistor, which can greatly reduce the transistor number and stack height in a circuit.
A Study of Garbage Classification with Convolutional Neural Networks

Recycling is already a significant work for all countries. Among the work needed for recycling, garbage classification is the most fundamental step to enable cost-efficient recycling. In this paper, we attempt to identify single garbage object in images and classify it into one of the recycling categories. We study several approaches and provide comprehensive evaluation. The models we used include support vector machines (SVM) with HOG features, simple convolutional neural network (CNN), and CNN with residual blocks. According to the evaluation results, we conclude that simple CNN networks with or without residual blocks show promising performances. Thanks to deep learning techniques, the garbage classification problem for the target database can be effectively solved.
A 10-18 GHz GaN Power Amplifier Based on Asymmetric Magnetically Coupled Resonator

This paper presents a 10 GHz to 18 GHz power amplifier (PA) using 0.25-μm gallium nitride high electron mobility transistor (GaN HEMT) technology. An asymmetric magnetically coupled resonator (MCR) technique is proposed to enhance the broadband frequency response. The theory and design procedures are illustrated in the paper. This PA delivers saturated output power greater than 36 dBm with power-added efficiency (PAE) greater than 20% from 11 GHz to 17 GHz. The small signal gain varies from 18.5 dB to 21.5 dB over the frequency range of 10-18 GHz and the peak gain is 21.5 dB at 16 GHz. The chip size of this PA is 1.8 mm × 1.5 mm. The PA shows a broad fractional bandwidth and a compact chip size among Ku-band PAs in GaN technology.