Miracle-dz/UniFAR-FTUs huggingface dataset:
Impedance Sliding Mode Control With Adaptive Fuzzy Compensation for Robot-Environment Interacting

In the field of robot research and application, improving the interaction performance between robots and the environment is the basic requirement of robot control. Hence, the position/force control problem needs to be solved. However, in practice, the model of the robot is usually inaccurate, and the working environment is usually uncertain. To solve the position/force control problem of the robot when the model and position are uncertain, a new method of impedance sliding mode control with adaptive fuzzy compensation (ISMCAF) is proposed. The dynamics of the robot are governed to follow a target impedance model and the interaction control objective is achieved. According to Lyapulov’s theory, sliding mode control law and adaptive control law are designed to ensure the stability of the closed-loop system. The proposed method is further verified by simulation.
Machine Learning Based Malware Detection in Wireless Devices Using Power Footprints

The adoption of smart devices, smartphones and IOT (Internet of things) devices, has witnessed a tremendous growth because of the convenience that they bring to users. However, at the same time, it makes them the target of malware developers and security attacks. Therefore, in this work, we propose a machine learning based methodology to detect malicious activities in wireless devices using power consumption footprints. The methodology consists of two main steps. First, a set of effective features are extracted from power measurements using both frequency spectral estimation and principal component analysis. Second, malware detection is formulated as a binary classification problem. Three different classifier algorithms are evaluated: support vector machines (SVM), naive Bayes (NB), and decision trees (DT). The SVM classifier shows better performance in terms of classification accuracy. Six different malicious behaviours are considered in the evaluation of the methodology process. The proposed methodology has successfully detected hard to spot malicious behaviours with (96 99)% accuracy.
Multi-level Glioma Segmentation using 3D U-Net Combined Attention Mechanism with Atrous Convolution

Accurate segmentation of glioma from 3D medical images is vital to numerous clinical endpoints. While manual segmentation is subjective and time-consuming, fully automated extraction is quite imperative and challenging due to the intrinsic heterogeneity of tumor structures. In this study, we propose a multi-level glioma segmentation framework, 3D Residual-Attention-Atrous U-Net (RAAU-Net), using 3D U-Net combined attention mechanism with atrous convolution. The 3D RAAU-Net can extract contextual information by combining low- and high-resolution feature maps. The attention mechanism is embedded in each skip connection layer of 3D RAAU-Net to enhance feature representations. Meanwhile, the atrous convolution is adopted in the whole network architecture to incorporate large and rich semantic information. Furthermore, we design a new training scheme to reduce false positives and enhance generalization. Eventually, our proposed segmentation method is evaluated on the validation dataset from the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) 2018 and achieve a competitive result with average Dice score of 88% for the whole tumor, 79% for the tumor core and 73% for the enhancing tumor, respectively. Quantitative results and visual analysis have proven that these improvements in 3D RAAU-Net are effective and achieve a better segmentation accuracy compared with the baseline.
Efficient CQI Estimation for Synchronized ABS Configuration in HetNet

In Heterogeneous Networks (HetNets), User Equipments(UEs) in pico cells may suffer from significant co-channel interference from macro stations. The 3rd Generation Partnership Project (3GPP) organization has proposed the concept of Almost Blank Subframe (ABS) so as to reduce the interference of macro stations to pico UEs. Addressing the problem that the introduction of ABS makes it impossible for pico UEs to correctly obtain channel conditions on different subframes, this paper proposes an efficient scheme based on a variable parameter Lambda report and Lambda estimation to derive Channel Quality Indicator (CQI). The method is applicable to the ABS and non-Almost Blank Subframe (non-ABS). It compensates the non-restricted measured CQI with different offsets, based on ABS density and ratio of interference levels from macro cells and pico cells to obtain CQI on different subframes. Simulation and analytical results show that our proposed scheme can achieve approximately 59% gain over the 3GPP system and reduce report overhead significantly without inducing penalties on throughput.
Salient Object Detection via Quaternionic Local Ranking Binary Pattern and High-Level Priors

We proposed a novel salient object detection based on quaternionic local ranking binary pattern and high-level priors. Firstly, we employed a edge-preserving smoothing algorithm to smooth the minor variations within textures. Secondly, we adopted a local descriptor called quaternionic local ranking binary pattern (QLRBP) for smooth image, and got three feature maps. Futhermore, we presented a saliency measurement method based on Shannon entropy and L2 norm for the feature maps. Finally, we combined the two level priors with feature maps to generate three initial salieny maps, and we obtained final saliency map by the saliency measure method. The experimental results demonstrate that our method is effective and efficient.
A 0.9/1.8/2.4GHz-reconfigurable LNA with Inductor and Capacitor Tuning for IoT Application in 65nm CMOS

This paper presents a 0.9/1.8/2.4GHz-reconfigurable low noise amplifier (LNA) for IoT applications. An input transformer consisting of a primary coil and two secondary coils is proposed in the LNA to ensure impedance matching in three bands. Mutual inductance enhancement scheme is also proposed to boost the inductance Q value and improve circuit performance without power overhead. The proposed LNA is designed and simulated in TSMC 65nm CMOS with an area of 1.32mm2. The post-layout simulations show that the presented LNA achieves 14.5/16/17.5dB voltage gain and 2.8/2.5/2.0dB noise figure at 0.9/1.8/2.4GHz. The S11 and the input P1dB are −8.8/-11/-9.6dB and −11/-17/-19dBm at 0.9/1.8/2.4GHz respectively. The LNA power consumption is 4mW from a 1.2V power supply.
Investigations on the Short-Circuit Degradation and its Mechanism of 1.2-KV 19-A SiC power MOSFETs

To provide a guideline for converter design and fault protection, the failure mechanism and reliability of silicon carbide (SiC) power MOSFETs needs to be further investigated. In this paper, the failure mechanism during short-circuit (SC) of commercial 1.2-KV/19-A SiC power MOSFETs was investigated. After the SC tests, the Ids of the device decreased significantly. Moreover, it was found that the Vth and Igs increased obviously. The results demonstrated that negative charges were captured by the gate oxide and accumulated during the SC tests, which is eventually causes degradation of the gate oxide. The results of this study may be useful in the design and application of SiC power MOSFETs.
Oblique Random Forest via Regularized Multisurface Proximal Support Vector Machine

We propose a novel approach to train oblique random forests using a linear classifier where the feature axis is not invariably orthogonal to the decision hyperplanes at each internal node of the base model. At each non-leaf node, for the multiclass classification problem, we group all the training samples into two groups of classes corresponding to the underlying geometric characteristics with respect to a randomly chosen feature subspace. Subsequently, two discriminatory hyperplanes are obtained, as per each class, by employing the Regularized Multisurface Proximal Support Vector Machine (RegMPSVM). While optimizing the impurity criteria, the test hyperplane of the corresponding internal node is selected from either of the bisectors of the two hyperplanes of the two groups of classes. As the tree propagates, to tackle the small sample size issue, various regularisation approaches are employed. Using this method, the oblique random forests are grown. Proposed framework’s potency is exhibited by comparison with the state-of-the-art frameworks on 40 real-world benchmark classification datasets of miscellaneous research fields. In the expression of prediction accuracy, the ascendancy of our proposed framework is demonstrated by the classification results.
Inter-Satellite Integrated Laser Communication/Ranging Link with Feedback-Homodyne Detection and Fractional Symbol Ranging

The Integrated Laser Communication/Ranging System, which uses coded signal as the ranging information carrier, is of great importance to the next large-capacity inter-satellite information network. In this paper, the parallel FFT estimation is applied to improve the speed and range of tracking wavelength drift caused by both the trajectory estimation error and laser instability, it can handle dynamic wavelength drift up to 2.4 pm/s (300 MHz/s). Meanwhile, for clock sources with subtle dynamic frequency offset and sufficient stability, the proposed fractional symbol ranging method based on the timing offset compensation in the receiver is proven to achieve millimeter-level measurement accuracy. The Integrated Laser Communication/Ranging Link with high-sensitivity feedback-homodyne detection and fractional symbol ranging is demonstrated with real-time FPGA implementation and is shown to perform well in terms of both laser linewidth tolerance and noise resistance.
Efficient and Secure Proxy re-signature Message Authentication Scheme in Vehicular Ad Hoc Network

In order to solve privacy protection problem in the Internet of Vehicles environment, a message authentication scheme based on proxy re-signature is proposed using elliptic curves, which realizes privacy protection by transforming the vehicle's signature of the message into the roadside unit's signature of the same message through the trusted center. And through the trusted center traceability, to achieve the condition of privacy protection, and the use of batch verification technology, greatly improve the efficiency of authentication. It is proved that the scheme satisfies unforgeability in ECDLP hard problem in the random oracle model. The efficiency analysis shows that the scheme meets the security and efficiency requirements of the Internet of Vehicles and has certain practical significance.
Overview of Neural Machine Translation for English-Hindi

Artificial intelligence is getting shrewd in real time with massive amounts of computational power and high in demand in last quarter more than billion dollars. The Intensification of Neural Networks in Machine learning playing major revolution in Machine Translation (MT). Looking same aspect, we have prepared a paper on Machine Translation for Hindi using Neural Machine Translation techniques. The Basis of this paper is to translate the source language into target language with the help of sentence structure for source language (English sentence) and corresponding reordering rules for target language (Hindi sentence) using a deep neural network (DNN). When the source input that is English sentences are exactly matched with the target language database then the suitable translation for related input is directly fetched from the database. The fuzzy matching between the input sentence and the sample sentence in the database is done with cosine similarity between the sentence structure of input sentence and the sample sentence and then the reordering rule of the most alike sentence to the input sentence is used for translation.Extracting the structure of the source language sentence and extracting the reordering rule for target sentence is the core part of the translator. Reordering of words in spite of that remains one of the complex problems.It is exciting to implement Deep Neural Network and fuzzy logic to build intelligent reordering rules machine learning system.
User Acceptance of Predictive Analytics for Student Academic Performance Monitoring: Insights from a Higher Education Institution in the Philippines

Student academic performance describes how well a student meets the standards of a course or a whole of the program. Through a learning management system, these student performance data can be analyzed using predictive models integrated into information systems. This study aims to understand the user acceptance of an information system that predicts students’ academic performance using the technology acceptance model, through a survey conducted among the users in a higher education institution in the Philippines. Results show that perceived ease of use has a significant influence on perceived usefulness. Moreover, there is no significant relationship between perceived usefulness and attitude towards use of technology. Overall, the technology acceptance model explains the determinants of adoption of predictive analytics on student academic performance. Practical and research implications are discussed.
How to ID Internet of Things Solutions Without Feeling Like an IDIoT: An Application Perspective

The internet of things (IoT) and industrial IoT (IIoT) have incredible benefits and potential. They promise vast improvements in operational efficiencies and a reduction of catastrophic failures. The benefits of such technologies may be limited by incompatibilities due to manufacturer protocol and device differences. These challenges will be passed on to the user, resulting in diminished benefits and frustration if not anticipated and mitigated accordingly. This article explores available technologies and addresses guidelines for future standard and specification development while providing parallels to similar technology introductions in large-scale industrial facilities for the review of lessons learned.
Implementation of Adaptive Threshold for Peak Detection of Photoplethysmography Applied on Microcontroller

Heart rate (HR) is computed by counting how many a person's heart is beating in a minute. A photoplethysmography (PPG) that measures the changes in blood volume by implementing transmission or reflection of specific light color can be used to measure HR. HR measurement can be conducted using PPG by attaching the sensor to the wrist, earlobe or finger. The beat detection in HR measurement can be conducted by comparing the signal to the threshold for finding its peak. When the measured object has a slight movement, the signal will be shifted and it makes the peak difficult to be detected. To overcome this condition, an adaptive threshold can be applied by changing the threshold based on the shift of the signal. Several steps that are conducted in this study are sensor reading, digital filtering, thresholding, and heart rate calculation. The study shows that the adaptive threshold can change the threshold value based on the signal changes. Compared to Oximeter reading, the HR calculation can yield a good result.
Prediction of Drug Classes with a Deep Neural Network using Drug Targets and Chemical Structure Data

Drugs are classified according to their biological and chemical reactions, and the systems that they target. Thus, an accurate and efficient prediction method for drug class discovery would reveal key properties of candidate drugs, significantly conserving time and resources in drug repositioning and design. Previous approaches, based on data mining or statistics, required complicated feature construction in advance. Knowing that deep learning can identifying patterns in high-dimensional datasets without elaborate feature selection or engineering, we constructed a model for predicting drug classes using deep neural networks - with biological and chemical structure data. Our proposed model outperforms previous learning-based methods in terms of prediction accuracy.
Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks

Smart grids have become susceptible to cyber-attacks, being one of the most diversified cyber–physical systems. Measurements collected by the supervisory control and data acquisition system can be compromised by a smart hacker, who can cheat a bad-data detector during state estimation by injecting biased values into the sensor-collected measurements. This may result in false control decisions, compromising the security of the smart grid, and leading to financial losses, power network disruptions, or a combination of both. To overcome these problems, we propose a novel approach to cyber-attacks detection, based on an extremely randomized trees algorithm and kernel principal component analysis for dimensionality reduction. A performance evaluation of the proposed scheme is done by using the standard IEEE 57-bus and 118-bus systems. Numerical results show that the proposed scheme outperforms state-of-art approaches while improving the accuracy in detection of stealth cyber-attacks in smart-grid measurements.
AI-Assisted Network-Slicing Based Next-Generation Wireless Networks

The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three specific research problems under this architecture. First, we introduce a network-slicing based architecture and explain why and where artificial intelligence (AI) should be incorporated into this architecture. Second, the motivation, research challenges, existing works, and potential future directions related to applying AI-based approaches in three research problems are described in detail, i.e., flexible radio access network slicing, automated radio access technology selection, and mobile edge caching and content delivery. In summary, this paper highlights the benefits and potentials of AI-based approaches in the research of NGWNs.
Run Time Container Security Hardening Using A Proposed Model Of Security Control Map

Container technology shifted the cloud industry to a new level of operations and it comes with a cost to make it more secure! Security vulnerabilities solutions are very critical for not only public clouds but also private cloud setups where the attack originates within the cloud environments itself. Various methods are derived and being implemented in the domain of container security vulnerabilities but it is getting more complex as the cloud usage grows exponentially.This paper starts with a common understanding of container security and uses cases to illustrate security vulnerabilities in general. A detailed analysis of the vulnerabilities impact and container security is presented which will layout the controls, best practices and securing techniques. For Hardening security of runtime containers a security controls map is proposed. To give further insight the result from some experiments we have done in our lab is presented here with the control map proposed in comparison to the container deployments default.
Hybrid-Weighted Total Variation and Nonlocal Low-Rank-Based Image Compressed Sensing Reconstruction

To reconstruct natural images from compressed sensing (CS) measurements accurately and effectively, a CS image reconstruction algorithm based on hybrid-weighted total variation (HWTV) and nonlocal low-rank (NLR) is proposed. It considers the local smoothness and nonlocal self-similarity (NSS) in image, improves traditional hybrid total variation (TV) model, and constructs a new edge detection operator with mean curvature to adaptively select the TV. The HWTV combines the advantages of first-order TV and second-order TV to preserve the edges of the image and avoid the staircase effect in the smooth areas. And NLR can effectively reduce the redundant information and retain the structural information of the image. In addition, the proposed algorithm constructs prior regularization terms with improved HWTV model and NLR model, and utilizes soft threshold function and smooth but non-convex function to solve the TV and low-rank optimization problems, respectively. Finally, the alternative direction multiplier method (ADMM) iterative strategy is used to separate the target model into several sub-problems, and the most efficient methods are adopted to solve each sub-problem. Experimental results show that, compared with the state-of-the-art CS reconstruction algorithms, the proposed algorithm can achieve higher reconstruction quality, especially in the case of low sampling rates.
Towards Recognition of Pleural Effusion Images

early diagnosis of patients' diseases allows to prescribe effective treatment in a timely manner. This article presents the results of research related to the recognition of images of pleural effusions. The purpose of the research is the recognition of images characteristic of pathologies associated with oncological diseases. When recognizing images, convolutional neural networks were used. When developing software, the authors used the TenzorFlow and OpenCV libraries. Image recognition accuracy is 95%. The studies are incomplete; the authors are trying to improve the results of research by replenishing the training sample with new copies of images of pleural effusions and using combinations of pattern recognition methods.
A Linear Relation behind Outstanding Crystalline Silicon Solar Cells

In a long history of photovoltaic innovations, various architectures of crystalline silicon solar cells have been evolved to result in the highest conversion efficiency (η) of those days as described in Best Research Cell Efficiency by NREL. The cells are characterized by three parameters of the power max (Pmax), the saturation current (Jsc) and open-circuit voltage (Voc), but the fill-factor (FF) can be deduced from those three parameters. The 36 outstanding cells of those days are picked up from the published papers to be plotted on the η-FF and η-Jsc•Voc planes, in which all kinds of the architectures of the cells are included, for instance, PERC, PERL, PERT, HIT, IBC, TopCon, POLO, POLO-IBC, and Hetero-IBC(HJ-IBC). The plots of the η of those outstanding solar cells against their FF or Jsc•Voc clearly reveal a linear relation among them, even they have been independently evolved. The linear relation seems to have some effective meanings. The linear relation being assumable as a straight line, a 87% of FF on the line, which was predicted as the limit of FF by M. Reusch, can meet well with 29% to 30% in efficiency, which have been expected as the efficiency limit of crystalline silicon solar cells by W. Schockley and J. Queisser, and R. Swanson.