Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear–based Newton–Raphson Approximation for Power Flow Solution in DC Power Grids

This paper presents a linear–based Newton method for load flow solution in DC power grids. This approximation is based on the classical Taylor’s series expansion combined to the open–circuit voltage obtained when all the constant power load points are disconnected. This solution strategy avoids the usage of an iterative process to solve the load flow problem in DC power grids reducing its processing time. Notwithstanding its simplicity, the proposed method is very accurate when is compared to classical Gauss–Seidel or Newton–Raphson methods concerning the solution quality. Simulation results are conducted via MATLAB software by using two radial test feeders composed by 10 and 33 nodes reported in the specialized literature.
Fair Resource Allocation in an MEC-Enabled Ultra-Dense IoT Network with NOMA

Ultra-dense Internet of Things (IoT) network has greatly facilitated the development of smart environments and the realization of diverse sophisticated applications. Power constrained and resource limited IoT devices often need to perform computation-intensive and delay-sensitive tasks in such a network. Mobile edge computing and non-orthogonal multiple access are two promising techniques to address the corresponding challenges. In this paper, in order to improve the fairness and resource efficiency among IoT users, resource allocation problems are formulated in ultra-dense MEC-enabled IoT networks with NOMA considered. An iterative algorithm based on successive convex approximation technique is proposed to solve those challenging non-convex problems. Simulation results show that our proposed allocation method outperforms the benchmark schemes and verifies the efficiency of the proposed algorithm.
Ultra-high speed 7mohm, 650V SiC half-bridge module with robust short circuit capability for EV inverters

Ultra-low RdsA SiC JFET technology is used to realize a 7mohm, 650V Stack Cascode switch, rated at >100A, with very fast switching, and 8us rated short circuit capability that is reported here for the first time. These devices meet all the technical requirements for EV inverters, representing the lowest RdsA and highest short circuit rated contemporary switch technology. The devices are assembled in a high performance, low inductance module to demonstrate extremely low switching losses, making the technology suitable for higher frequency DC-DC applications as well.
Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features

Early detection of esophageal abnormalities can help in preventing the progression of the disease into later stages. During esophagus examination, abnormalities are often overlooked due to the irregular shape, variable size, and the complex surrounding area which requires a significant effort and experience. In this paper, a novel deep learning model which is based on faster region-based convolutional neural network (Faster R-CNN) is presented to automatically detect abnormalities in the esophagus from endoscopic images. The proposed detection system is based on a combination of Gabor handcrafted features with the CNN features. The densely connected convolutional networks (DenseNets) architecture is embraced to extract the CNN features providing a strengthened feature propagation between the layers and alleviate the vanishing gradient problem. To address the challenges of detecting abnormal complex regions, we propose fusing extracted Gabor features with the CNN features through concatenation to enhance texture details in the detection stage. Our newly designed architecture is validated on two datasets (Kvasir and MICCAI 2015). Regarding the Kvasir, the results show an outstanding performance with a recall of 90.2% and a precision of 92.1% with a mean of average precision (mAP) of 75.9%. While for the MICCAI 2015 dataset, the model is able to surpass the state-of-the-art performance with 95% recall and 91% precision with mAP value of 84%. The experimental results demonstrate that the system is able to detect abnormalities in endoscopic images with good performance without any human intervention.
Research on Ontology-based Network Security Knowledge Map

With the rapid development of network technology, security data is becoming more and more complex. Since the multi-source heterogeneous, complex scattered data security have been expanding, traditional relational database difficult to efficiently handle complex and diverse analysis of the relationship between big data security. In view of the above problems, this paper proposes an ontology-based network security knowledge map design method to more accurately describe the complex relationship between security elements. According to the actual needs of security analysis, using the ontological idea, design a multi-level security ontology system and study the relationship between ontology; Propose a mapping mechanism between the security ontology and the security knowledge map; The security knowledge map for multi-source data is constructed by using the graph database storage mode to provide important technical support for the construction and application research of the network security knowledge base.
An Event-Driven Mechanism Coverage Algorithm Based on Sensing-Cloud-Computing in Sensor Networks

The structure of network topology in heterogeneous sensor networks changes fast. As a result, the coverage and control ability of sensor nodes will be undermined. In order that we can address this problem, we propose an event-driven mechanism coverage algorithm based on Sensing-cloud-computing in heterogeneous sensor networks (EMC-SC). First, the network coverage model is utilized to compute the boundary relationship between the dual square and the sensor nodes coverage area. Then, the Poisson distribution model is employed to analyze the coverage performance of the randomly deployed sensor nodes and further compute the probability of the coverage which is effective over the monitoring area. Next, the node state mechanism scheduling algorithm is determined according to the amount of exchanged information between the neighbor nodes and the sensor nodes as well as the remaining sensor nodes energy. Finally, the results of the simulation show that, if it is compared with other algorithms which exist, the EMC-SC algorithm which is proposed could improve the network lifetime and the network coverage rate by 12.73% and 16.22%, respectively. Therefore, the effectiveness of the proposed algorithm and the validity of the proposed algorithm are verified.
Modeling and Coverage Analysis of Downlink UAV Networks with MmWave Communications

In order to satisfy the requirement of high throughput in most unmanned aerial vehicle (UAV) applications, the potential of integrating millimeter wave (mmWave) communications into UAV networks is explored in this paper. A tractable three-dimensional (3D) spatial model is proposed for evaluating the average downlink performance of UAV networks at mmWave bands, where the locations of UAVs and users are randomly distributed with the aid of a Poisson cluster process. Moreover, a typical 3D antenna model with the uniform planar array is deployed at all UAVs to examine the impact of both azimuth and elevation angles. Based on this framework and an ordered user selection scheme, closed-form approximation equations of coverage probabilities are derived. In a noise-limited scenario, an exact expression is provided, which theoretically demonstrates that the coverage performance can be enhanced by increasing the number of antenna elements. Regarding the altitude of UAVs, there exists an optimal height for maximizing the evaluated coverage probability. Numerical results illustrate that non-line-of-sight transmissions caused by obstacles have negligible effects on the considered system.
Robust Navigation and Positioning Algorithm and Performance Evaluation in Harsh Scenarios

Global positioning system (GPS) is a satellite based tracking system that provides a user with proper real-time three dimensional position and velocity information. However, the measurements from a GPS sensor sometimes are imprecise due to multipath effect and clock offset, which need to be corrected using filtering algorithms. Kalman filter (KF) can reduce errors in a least squares sense and is widely applied in navigation and positioning. Nevertheless, traditional KF needs accurate statistical information about the mobile terminal (MT), otherwise it will result in lower precision and even divergence. In this paper, we present a robust positioning algorithm in harsh scenarios, in which we define residual covariance (RC) as an important index to evaluate the performance of the filter algorithm. We deduce the closed relationship of RC in KF under the cases of 1) inaccurate system model; 2) abrupt change of states, respectively. Then we find that RCs of KF under above two cases both have a bias compared with the desired ones, which indicates KF has poor tracking ability in some harsh environments. However, strong tracking filter (STF) still has strong tracking ability even under above two harsh scenarios because it utilizes a fading factor to adjust the gain matrix in real time so that its RC satisfies the orthogonality principle (OP), which indicates all useful information has been extracted from residual. The theoretical analysis and simulation results demonstrate the effectiveness of strong tracking filter in harsh scenarios.
Rusty: Runtime System Predictability Leveraging LSTM Neural Networks

Modern cloud scale data-centers are adopting workload co-location as an effective mechanism for improving resource utilization. However, workload co-location is stressing resource availability in unconventional and unpredictable manner. Efficient resource management requires continuous and ideally predictive runtime knowledge of system metrics, sensitive both to workload demands, e.g., CPU, memory etc., as well as interference effects induced by co-location. In this paper, we present Rusty, a framework able to address the aforementioned challenges by leveraging the power of Long Short-Term Memory networks to forecast at runtime, performance metrics of applications executed on systems under interference. We evaluate Rusty under a diverse set of interference scenarios for a plethora of cloud workloads, showing that Rusty achieves extremely high prediction accuracy, up to 0.99 in terms of $R^2$R2 value, satisfying at the same time the strict latency constraints to be usable at runtime.
Optimizing reserve prices for publishers in online ad auctions

In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method is based on the principle of Thompson sampling combined with a particle filter to approximate and sample from the posterior distribution. Our method is suitable for non-stationary environments, and we show that, when the distribution of the winning bid suffers from estimation uncertainty, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.
Failure Rate Prediction Model of Substation Equipment Based on Weibull Distribution and Time Series Analysis

The failure rate is an important indicator to assess the reliability of substation equipment, and the failure rate prediction is an effective way to master the operation status of substation equipment. According to the characteristics of failure rate data, such as failure period segmentation and stochastic variation, this paper proposes a combined prediction method by the data decomposition of failure rate time series and the segmentation theory of failure periods and then establishes a new failure rate prediction model of the substation equipment based on the Weibull distribution and time series analysis. Compared with the traditional Weibull distribution function model which cannot describe the stochastic variation of failure rate data and cannot identify the failure period automatically, the proposed model in this paper uses the minimum sum algorithm of residual squares obtained by the Weibull distribution to accurately identify the demarcation point between different failure periods and then establishes the autoregressive moving average (ARMA) model to achieve combined prediction. Finally, the effectiveness of the proposed model is verified by the engineering example and comparison with the traditional failure rate prediction methods.
Deep Learning Biopsy Marking of Early Neoplasia in Barrett’s Esophagus by Combining WLE and BLI Modalities

Esophageal cancer is the fastest rising type of cancer in the western world. Also, early neoplasia in Barrett’s esophagus (BE) is difficult to detect for endoscopists and research has shown it is similarly complicated for Computer-Aided Detection (CAD) algorithms. For these reasons, further development of CAD algorithms for BE is essential for the wellbeing of patients. In this work we propose a patch-based deep learning algorithm for early neoplasia in BE, utilizing state-of-the-art deep learning techniques on a new prospective data set. The new algorithm yields not only a high detection score but also identifies the ideal biopsy location for the first time. We define specific novel metrics such as sweet-spot flag and softspot flag, to obtain well-defined computation of the biopsy location. Furthermore, we show that combining white light and blue laser imaging improves localization results by 8%.
PIN-photodiode based active pixel in 0.35 μm high-voltage CMOS for optical coherence tomography

This paper presents an active pixel based on a PIN photodiode for application in Optical Coherence Tomography (OCT), where a high responsivity and low crosstalk is required. The proposed pixel is built on a wafer with a high-resistivity epitaxial silicon layer and optimized for high efficiency at 850 nm, low dark current, low crosstalk, and low noise operation. Advantages of this approach over conventional approaches such as the 3T active pixel in a standard CMOS process and over 4T pinned photodiode active pixel approaches for OCT applications are explained. A test chip was fabricated in $0.35 \mu \mathrm {m}$ high-voltage CMOS. Three different epitaxial layer thicknesses are investigated. Measured results of the OCT PIN-photodiode pixel are presented.
Multi-User Wideband Sparse Channel Estimation for Aerial BS with Hybrid Full-Dimensional MIMO

Due to the air-ground millimeter-wave channels exhibit inherent sparsity in both angle and delay domains, we propose a multi-user wideband sparse channel estimation scheme for aerial base station (BS) with hybrid full-dimensional (FD)-MIMO systems, where the aerial BS employs FD-MIMO array to serve multiple user equipments with a single antenna through line-of-sight (LoS) paths. Specifically, we first formulate multi-user uplink channel estimation as a parameter estimation problem. Then, by designing the hybrid combiners at the BS to visualize hybrid analog/digital array as a digital array, we estimate the horizontal/vertical AoAs and delays using the 3-dimensional (3-D) unitary ESPRIT algorithm. Moreover, the associated path gains are obtained using the least squares (LS) estimator. Finally, we reconstruct the MIMO channel based on the dominant channel parameters estimated above. According to spectrum estimation theory, the proposed scheme obtains the super-resolution estimations of the horizontal/vertical angle-of-arrivals (AoAs) and delays of sparse LoS paths with low overhead. Simulation results show that the proposed scheme can obtain a good channel estimation performance for multi-user wideband sparse scenario.
Enhancing mood metrics using encapsulation

The requirement to improve software productivity and software quality has promoted the research on software metrics. Encapsulation is a powerful mechanism in Object-Oriented programming and it is critical for building large complex software; which can be maintained and extended. Since the emergence of the Object-Oriented approach to software development, it has been recognized that the development of Object-Oriented metrics to measure the quality of software is a new challenge. Many researchers have already risen to this challenge, most notably Chidamber & Kemerer, Abreu, Lorez and Kidd. But the metrics introduced by the above proponents lack in measuring the encapsulation mechanism. This paper purposes a new metric to measure encapsulation. Encapsulation constitutes both privacy and unity and these two attributes have been taken to purpose the above mentioned metrics. A statistical analysis on EF (Encapsulation Factor) metric is also done in this study.
A Two-Stage DBN-Based Method to Exploring Functional Brain Networks in Naturalistic Paradigm FMRI

FMRI using naturalistic paradigms such as watching movies has gained increasing interest in recent neuroimaging studies. Data-driven blind source separation (BSS) methods such as independent component analysis (ICA) are widely used to extract meaningful features in fMRI data. Recent studies have shown that BSS based on deep neural networks (DNNs) such as restricted Boltzmann machine (RBM) and deep belief network (DBN) outperform ICA. Those DNN-based methods interpret spatially aggregated fMRI time series of multiple subjects in group analysis to reduce model complexity, and only brain networks with group-wise temporal consistency can be identified. However, fMRI activities to naturalistic paradigm exhibit both group-wise consistency and inter-subject difference. To address this problem, we propose a two-stage DBN-based BSS method. In the first stage, a DBN model is trained using temporally aggregated fMRI time series of multiple subjects. In the second stage, subject-specific DBN models are initialized using model parameters learned in the first stage and are trained to converge using individual fMRI data to refine brain network identification. We use an fMRI dataset acquired using a movie stimulus to validate the proposed method.
Improving Face Sketch Recognition via Adversarial Sketch-Photo Transformation

Face sketch-photo transformation has broad applications in forensics, law enforcement, and digital entertainment, particular for face recognition systems that are designed for photo-to-photo matching. While there are a number of methods for face photo-to-sketch transformation, studies on sketch-to-photo transformation remain limited. In this paper, we propose a novel conditional CycleGAN for face sketch-to-photo transformation. Specifically, we leverage the advantages of CycleGAN and conditional GANs and design a feature-level loss to assure the high quality of the generated face photos from sketches. The generated face photos are used, as a replacement of face sketches, and particularly for face identification against a gallery set of mugshot photos. Experimental results on the public-domain database CUFSF show that the proposed approach is able to generate realistic photos from sketches, and the generated photos are instrumental in improving the sketch identification accuracy against a large gallery set.
Design of a Broadband Doherty Power Amplifier Based on Hybrid Continuous Mode

Hybrid continuous mode can be utilized to design highly efficient broadband power amplifiers (PAs). In this paper, a broadband Doherty PA (DPA) with enhanced back-off efficiency is realized by using the hybrid continuous mode. Especially, the carrier PA operates under hybrid continuous mode at low-power regions, enhancing the bandwidth and back-off efficiency of the DPAs. For validating the proposed theory, a broadband DPA working over 1.5–2.6 GHz is implemented. The experimental results show that the designed DPA achieves a saturation power of 43.7–45 dBm, a drain efficiency of 41.3%–55.1% at 6-dB output back-off power level, and a drain efficiency of 57%–74.6% at peaking power level.
SlamCraft: Dense Planar RGB Monocular SLAM

Monocular Simultaneous Localization and Mapping (SLAM) approaches have progressed significantly over the last two decades. However, keypoint-based approaches only provide limited structural information in a 3D point cloud which does not fulfil the requirements of applications such as Augmented Reality (AR). SLAM systems that provide dense environment maps are either computationally intensive or require depth information from additional sensors. In this paper, we use a deep neural network that estimates planar regions from RGB input images and fuses its output iteratively with the point cloud map of a SLAM system to create an efficient monocular planar SLAM system. We present qualitative results of the created maps, as well as an evaluation of the tracking accuracy and runtime of our approach.
A new end-to-end measurement method for estimating available bandwidth

We present an original end-to-end available bandwidth measurement method, called SMART (statistics measurement for avail-bw by random train). It resolves some of the problems common for many types of existing probing methods, e.g. the long latency and large probe traffic. Contrary to traditional estimates of available bandwidth, SMART is not a methodology based on packet dispersion in packet pair or packet train, but a completely new methodology in the light of probability and statistics. The fundamental idea is to send very small packets at random moment and calculate the proportion of minimal delay ion total test samples. To reach this purpose, we redefine the available bandwidth based on probability and statistics. We have evaluated our method in controlled and reproducible environment using NS2, and the simulations show our method is accurate, efficient, quick and non-intrusive.
Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets

Sentiment analysis (SA) of Arabic tweets is a complex task due to the rich morphology of the Arabic language and the informal nature of language on Twitter. Previous research on the SA of tweets mainly focused on manually extracting features from the text. Recently, neural word embeddings have been utilized as less labor-intensive representations than manual feature engineering. Most of these word-embeddings model the syntactic information of words while ignoring the sentiment context. In this paper, we propose to learn sentiment-specific word embeddings from Arabic tweets and use them in the Arabic Twitter sentiment classification. Moreover, we propose a feature ensemble model of surface and deep features. The surface features are manually extracted features, and the deep features are generic word embeddings and sentiment-specific word embeddings. The extensive experiments are performed to test the effectiveness of the surface and deep features ensemble, pooling functions, embeddings size, and cross-dataset models. The recent language representation model BERT is also evaluated on the task of SA of Arabic tweets. The models are evaluated on three different datasets of Arabic tweets, and they outperform the previous results on all these datasets with a significant increase in the F-score. The experimental results demonstrate that: 1) the highest performing model is the ensemble of surface and deep features and 2) the approach achieves the state-of-the-art results on several benchmarking datasets.