Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of antisolvent treatment and of the substitution of the ion FA by the ion Cs on the properties of CsXFA1-XPbI3 films prepared by spin coating

This work reports results concerning the effect that antisolvent treatment causes on the morphology of Cs<inf>X</inf>FA<inf>1</inf>XPbI<inf>3</inf> films synthesized by spin coating as well as the impact that the substitution of the ion Formamidinium (FA) by the ion cesium (Cs) causes both on the stability and on the properties of the Cs<inf>X</inf>FA<inf>1-X</inf>PbI<inf>3</inf> films. This substitution occurs effectively when less than 25% of FA are replaced by Cs. The influence of both FA/Cs ratio and antisolvent treatment on the optical, structural and morphological properties of Cs<inf>X</inf>MA<inf>1-X</inf>PbI<inf>3</inf> films, where also studied through spectral transmittance, X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM) measurements.
Single-/Dual-Band Bandpass Filter-Integrated Single-Pole Double-Throw Switch Using Distributed Coupling Tri-Mode Resonators

This article presents a method to design a single-band (SB)/dual-band (DB) bandpass filter (BPF)-integrated single-pole double-throw (SPDT) switch by using one/two distributed coupling identical tri-mode resonator (TMR) pairs loaded with opposite polarity p-i-n diodes. Moreover, the proposed method is also directly suitable for designing the multiple-band (MB) BPF-integrated SPDT switch by simply increasing the used numbers of identical TMR pairs. As an example, a SB-BPF-integrated SPDT switch with the central frequency (CF) of 1 GHz and the ripple bandwidth (RBW) of 65 MHz is first designed by using one identical switchable TMR pair, so as to validate our design approach. Due to the inherent property of TMR and the introduced source–load coupling, high ON-state passband selectivity is achieved. Then, two switched 50- $\Omega $ matching circuits are connected to two noncommon feeding lines, and an SB-BPF-integrated SPDT switch with absorptive OFF-state property is also realized. Finally, a DB-BPF-integrated SPDT switch with CFs of 0.9 GHz/1.8 GHz and RBWs of 50 MHz/105 MHz is designed by simply employing two identical TMR pairs. When the operating states of two identical switchable TMR pairs are specifically chosen, the proposed DB-BPF-integrated SPDT switch exhibits multiple functions, i.e., the 900/1800 SB-BPF-integrated SPDT switches with absorptive OFF-state property, the 900/1800 DB-BPF-integrated SPDT switch with absorptive OFF-state property, and the 900/1800 diplexer-integrated SPDT switch. The measured results also show that these switches exhibit low ON-state passband losses, deep out-of-band rejections (OBRs) of ON-state passbands, high OFF-state suppressions (OSSs), high port-to-port isolations (PPIs), and compact circuit sizes.
The Reliability Prediction of Smart Meters Using Weibull Distribution Based on Fusion of Least Square Estimation and Maximum Likelihood Estimation

There is a big cost to using on-site inspection methods to test the performance of smart meters. However, using some algorithms and failure information to predict the reliability of smart meters is a useful way to evaluate the state of smart meters. A reliability prediction model based on Weibull distribution is proposed. And a fusion way of least square estimation and maximum likelihood estimation is used to calculate the parameters of Weibull model. Then the test results show the proposal method has a good prediction accuracy and a low RMSE.
Domain Wall Memory-Based Design of Deep Neural Network Convolutional Layers

In the hardware implementation of deep learning algorithms such as, convolutional neural networks (CNNs) and binarized neural networks (BNNs), multiple dot products and memories for storing parameters take a significant portion of area and power consumption. In this paper, we propose a domain wall memory (DWM) based design of CNN and BNN convolutional layers. In the proposed design, the resistive cell sensing mechanism is efficiently exploited to design low-cost DWM-based cell arrays for storing parameters. The unique serial access mechanism and small footprint of DWM are also used to reduce the area and energy cost of DWM-based design for filter sliding. Simulation results with 65 nm CMOS process show 45% and 43% of energy savings compared to the conventional CNN and BNN design approach, respectively.
Analysis of Map Information in Dynamic Environment

When the robot moves in the room, it’s essential to build a map to guide it. Currently, there is a method to establish the maps in real time called simultaneous localization and mapping. Due to the complexity of the surrounding environment and the influence of the accuracy of sensors on the construction of the map, the correctness of mapping will be greatly affected. This article is based on previous research. This operation uses the algorithm based on robot operating system. We design the algorithms to identify changings between the maps of indoor states and achieve good results.
An Improved Algorithm Based on Convolutional Neural Network for Smoke Detection

As an essential method of fire prevention and disaster control, smoke detection is of great significance to production and life. At present, the convolutional neural network (CNN) has achieved good results in the research of smoke detection. However, the detection accuracy is not high for some scenes. For example, the wind speed is tremendous, and the shape of the smoke changes rapidly. In order to deal with this problem better, this paper proposes an improved algorithm based on cascading classification and deep convolutional neural network. In the cascading classification part, we improve the cascading structure and make it select the appropriate parameter threshold for the smoke generated in different scenes. The convolutional neural network structure is trained to extract the variation characteristics of smoke better. Also, we optimize the parameters on the target data set. The experimental results show that the algorithm has achieved excellent results in accuracy and speed on the selected smoke detection data sets.
Forest Representation Learning with Multiscale Contour Feature Learning for Leaf Cultivar Classification

Automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. However, deep neural networks based leaf cultivar classification model can not deeply mine the contour features of leaf images. Besides, the deep neural networks are very data-hungry due to the large complexity of the models, which means that the model's performance can decrease significantly when the size of the training data decreases. In this paper, we propose the leaf cultivar classification model based on forest representation learning and multiscale contour feature learning. Firstly, we build the multiscale contour feature learning to extract the contour features of leaf images. Then, the original input features are transformed by the data augmentation method to enhance the ability of feature expression, and the autoencoder by forest performs the data of dimensionality reduction on the features. Finally, we use the improved deep forest algorithm and autoencoder by forest to build forest representation learning for classification. The experimental results show that the proposed algorithm has higher performance than the original deep forest (gcForest) algorithm and other existing start-of-art algorithms, and has higher performance and efficiency than other deep learning algorithms.
Malware Detection in Cloud Computing using an Image Visualization Technique

Malware creators generate new malicious software samples by making minor changes in previously generated samples. Here, similar features of the samples from the same malware family may be used in the detection of newly generated malicious software. In this paper, a new malware detection model based on the similarity of images is proposed. Malware detection is ensured by identifying the changes made in images. For the detection of the changes in malware images, a probabilistic framework is proposed. As a result of the experiments carried out on the two images of the program code from the same class, the proposed method accurately determines the changes made to these codes. The proposed model is tested on the Malimg dataset, and the model recognizes the changes in the software code with high accuracy.
Drug-Drug Interaction Extraction Using a Span-based Neural Network Model

Drug-drug interaction (DDI) extraction is one of important tasks in biomedical text mining. Compared with traditional pipeline extraction, joint methods enjoy lower error propagation, achieving better performances. However, biomedical texts contain a large number of overlapped relations, and existing joint extraction models fail to capture these entity relations. In this paper, we propose a novel joint model for DDI extraction. Different from the existing sequential models, our span-based neural model treats the task as a relation triplets prediction problem, and builds the entity-graph by enumerating all possible candidate spans, and simultaneously models the relation between the correlated entities. Experimental results show that the proposed method significantly outperforms the strong baselines and previous methods, achieving the state-of-the-art performances.
Knowledge Abstraction Matching for Medical Question Answering

Medical Question Answering (medical QA), which studies the problem of automatically answering patients' medical questions online, is one of the major applications of bioinformatics. Though many efforts have been made before, the medical QA system still deserves delicate algorithm optimization due to the serious application scenario and strict requirement for the answer quality. In this paper, we introduce a novel Knowledge Abstraction Matching (KAM) method for the medical QA problem. The intuition of KAM is that there are many frequent repeat text segments appearing in the answers across different questions. From this view, we propose a new method that consists of frequent segment $N$-gram mining, medical knowledge abstraction, medical segment matching and answer re-retrieval. KAM has been incorporated into Baidu's enterprise medical QA system MelodyQA deployed on the backend of Muzhi Doctor. The evaluation shows that the proposed method can generate more quality answers for MelodyQA with a significant improvement of question coverage under acceptable accuracy.
Efficiency Improvement of Planar Silicon Solar Cells Utilizing Localized Surface Plasmon Resonance of Silver Nanoparticles

In this work we develop a time- and cost-effective method of fabricating an antireflection structure on Si solar cells utilizing the localized surface plasmon resonance (LSPR) of Ag nanoparticles (NPs). Covered with an Al2O3 layer to prevent degradation, the NP decorated Si solar cell exhibits a broadband light absorption enhancement, having a low weighted average spectral reflectance (Rave) value of 9.5%, and exhibits the highest absolute gain of 19.2% in external quantum efficiency (EQE) at 700 nm and an overall 20% relative increase in power efficiency (η) compared to the reference Si solar cell without NPs.
Comparison of FEM Predicted and Measured Values of the ITER TF Coil Closure Welding Distortion

The ITER reactor relies in magnetic confinement of hot plasma. The main driver for the confinement is played by the Toroidal Field Coils. These magnets are composed by a Winding Pack, made of Nb3Sn superconductors, and a surrounding stainless steel structure or Coil Case which is closed by welding once the Winding Pack is inserted. The closure GTAW weld of the cases includes about 70m of weld ranging from 40 to 120 mm. Due to the tight tolerances that have to be respected on the final Toroidal Field Coils, a mechanical quasi-static Finite Element Model has been developed using ANSYS software by Enginsoft and SIMIC, under the work frame of a Fusion for Energy contract, to predict the welding distortion by simulating different welding scenarios and to confirm the definition of the required extra-material in the Toroidal Field Coil Cases. The FEM model was firstly benchmarked with validation coupons, then verified and fine-tuned with four 1:1 scaled Toroidal Field Coil Case cross section mock ups. Finally, a full Finite Element Model has been developed in order to predict the deformation of the cases during the welding process. The first Toroidal Field Coil has been welded during the first half of 2019. In this paper, a comparison between the deformation predicted by Finite Element Model and direct dimensional measurements of the distortions carried out during the welding process is presented.
Detection and Classification of Diseases of Banana Plant Using Local Binary Pattern and Support Vector Machine

Banana plantation is a commercial agricultural practice of huge significance especially in Asian and African countries. Banana production is affected by natural calamities and plant diseases. But plant diseases present a constant threat to the farmers affecting the quantity and quality of the banana cultivation. From the last decade, the image processing techniques and machine learning algorithms have been broadly used for identification and classification of infections in plants. In this work, texture pattern techniques for identification and classification of diseases in banana plants is introduced. The proposed methodology consists of two primary phases; (a) extraction of texture features from using local binary pattern (LBP); (b) classification of banana plant diseases and healthy banana plant. The texture features using LBP are extracted from an enhanced input image. The extracted features are fed to Support Vector Machine (SVM) and K-nearest neighbor (KNN) for final banana plant disease classification. The proposed technique is tested on the Plant Village dataset for the classification of two different experimental cases (i) Healthy-Black Sigatoka and (ii) Healthy-Cordana leaf spot. The proposed methodology attained an accuracy of 89.1 % and 90.9% for two experimental cases using SVM classifier.
Tuning PI controllers for optimal migrogrid operation based on Artificial Neural Network

The Microgrid achieve nowadays a great importance in providing energy to isolated areas. This paper demonstrates a new application of the Artificial Neural Networks (ANN) for online modification of the inverter based microgrid operation. A vector cascaded control technique is utilized as an inverter control strategy, which depends on the Proportional plus Integral (PI) controller. The suggested ANN is used to adapt the controller constants instantly. The ANN is simulated using MATLAB program. The input training samples of the ANN is obtained by carrying out different working settings simulated by PSCAD/EMTDC program. The optimal output training data fed to ANN is obtained by finding the optimal PI constants utilizing Evaporation Rate Water Cycle Algorithm (ERWCA). The utilized objective optimization problem used by the ERWCA is created by the Response Surface Methodology (RSM). The working scenarios included in this paper are 1) system conversion from grid connected mode to stand alone one, 2) system exposure to three lines to earth fault in the stand alone mode and 3) The steady state operation at grid connected mode.
Classification of Lung Images Using Deep Convolutional Neural Network

The history of medical imaging clearly portrays numerous computer aided diagnosis system (CAD) which was successfully used and implemented to assist radiologists about their patients. Medical image analysis had taken great hike over two decades using Artificial Neural Network for its task but since recent past it is being taken over by the Convolutional Neural Network and has also gained high popularity in medical imaging. CNN has mainly been developed as medical images possess high semantic features. In this paper, the tasks proposed ideology is on novel deep convolution neural network (DCNN) based method for lung normality classification. The extracted deep features from computer tomography (CT) images of the lungs are widely further used to classify the lungs abnormality i.e either as malignant or benign. Suitable modifications are performed to produce an acceptably high accuracy rate, thereby reducing the computational complexity rate. The proposed methodology involves the role of fully connected layer. While nearing to the outcome before which this layer plays a vital role in acquiring the desired classified images as per the requirement once the convolution process is finished. Therefore, this methodology is likely to be found only supportive to the system formed and thus improvising the accuracy level.
Study and Analysis of Pulsating and Nonpulsating Input and Output Current of Ultrahigh-Voltage Gain Hybrid DC–DC Converters

In this paper, eight ultrahigh-voltage gain dc–dc converters are proposed. These sets of converters are derived by the association of boost cells, coupled inductor, voltage multiplier, switched capacitors, and LC filter cells. The nonpulsating input and output converters are evaluated in terms of: principle of operation, voltage gain, voltage and current stress, estimated power losses, and efficiency. Following full analysis prototypes built in the laboratory show experimental results verifying the theoretical analysis.
Localization and classification of overlapping sound events based on spectrogram-keypoint using acoustic-sensor-network data

Automatic localization and classification of environmental sound events can provide great aid to many human-centric IoT applications. However as many papers have mentioned, environmental sound events in daily life are complicated and hard to classify especially when multiple sounds happen simultaneously. Unlike most works, which decompose and classify overlapping signals using a unified model, we first decompose overlapping sound signals with a spectrogram-keypoint based localization algorithm. These located and clustered spectrogram-keypoints are subsequently reused for sound source classification. Our major contribution is the modeling of a global cost function to synchronize the time-difference-of-arrivals (TDOA) of each small spectrogram-keypoint and further locating the sound sources by these clustered keypoints. With these clustered keypoints, 2 different classification models are used to classify the sound sources. Our experiments show that our solution is both accurate and low-cost in terms of calculation effort.
Real-time RGB-D semantic keyframe SLAM based on image segmentation learning from industrial CAD models

This paper presents methods for performing realtime semantic SLAM aimed at autonomous navigation and control of a humanoid robot in a manufacturing scenario. A novel multi-keyframe approach is proposed that simultaneously minimizes a semantic cost based on class-level features in addition to common photometric and geometric costs. The approach is shown to robustly construct a 3D map with associated class labels relevant to robotic tasks. Alternatively to existing approaches, the segmentation of these semantic classes have been learnt using RGB-D sensor data aligned with an industrial CAD manufacturing model to obtain noisy pixel-wise labels. This dataset confronts the proposed approach in a complicated real-world setting and provides insight into the practical use case scenarios. The semantic segmentation network was fine tuned for the given use case and was trained in a semi-supervised manner using noisy labels. The developed software is real-time and integrated with ROS to obtain a complete semantic reconstruction for the control and navigation of the HRP4 robot. Experiments in-situ at the Airbus manufacturing site in Saint-Nazaire validate the proposed approach.
Surface characterization of epitaxial Cu-rich CuInSe2 absorbers

We investigated the electrical properties of epitaxial Cu-rich CuInSe2 by Kelvin probe force microscopy (KPFM) under ambient and ultra-high vacuum conditions. We first measured the sample under ambient conditions before and after potassium cyanide (KCN) etching. In both cases, we do not see any substantial contrast in the surface potential data; furthermore, after the KCN etching we observed outgrowths with a height around 2nm over the sample surface. On the other hand, the KPFM measurements under ultra-high vacuum conditions show a work function dependence according to the surface orientation of the Cu-rich CuInSe2 crystal. Our results show the possibility to increase the efficiency of epitaxial Cu-rich CuInSe2 by growing the materials in the appropriated surface orientation where the variations in work function are reduced.
Improvement of Digest Based Authentication Scheme for Biometric Verification

Normally, authenticating a system in an untrusted environment is performed through mechanisms such as identification card, password checking and etc. However, by the development of technology, cracking such systems through forging password or stealing identification card may be done leading to the lack of security and hence privacy. This is why biometric based authentication systems are appropriate offer to provide security of clients. Through such systems, clients are verified and permitted to login to the system according to their physical or behavioral traits. Biometric systems offer several advantages over traditional authentication methods. Moreover, these systems, protects the privacy of clients, confidentiality and integrity of critical information for emerging technology such as smartphones and other appliances. In this paper, we improved our previous biometric authentication system and through some practical scenarios, we considered different attacks from client, server, and network sides to intrude into the privacy. We mathematically proved that our scheme is indistinguishable and prevents the attacker to threat legitimate individuals' information and privacy. Finally, we showed our computation and memory efficiency compared with related studies.
An Explicable Keystroke Recognition Algorithm for Customizable Ring-Type Keyboards

In our previous work, we developed an IMU (Inertial Measurement Unit) based smart ring that allows users to type characters without a physical keyboard and adopt well-known pattern recognition algorithms, such as Support Vector Machine (SVM), and Naive Bayes (NB), for keystroke recognition. However, these algorithms always require intensive computing resources or offer limited recognition accuracy. Moreover, they are often seen as black boxes incapable of providing readily comprehensible and visible clues for classification. This hampers the improvement of keystroke recognition accuracy and the ring-type virtual keyboard’s character layout design. Here we present a novel algorithm to recognize keystrokes in a fast and accurate manner. Firstly, the standard feature vector, including five attitude angle features and one acceleration feature, is built to express a specific stroke. Then, the feature vector of the testing keystroke is compared with the standard features. The most similar keystroke is matched and recognized after three times of voting. Based on this algorithm, we can identify the easily confused keystrokes and understand the mechanisms behind it. With this interpretability, we will be able to achieve the customized ring-type virtual keyboard application if necessary. The performance of this algorithm was evaluated by using a dataset with 1500 keystrokes of three different subjects. The results show that our algorithm is more effective in keystroke recognition than traditional algorithms for this ring-type keyboard. In addition to its application on virtual keyboards, this algorithm can also be potentially applied on other classification tasks with easy-to-understand results.