Miracle-dz/UniFAR-FTUs huggingface dataset:
Spherical Camera Localization by Color Difference Minimization Using 3D Model of the Environment

The goal of this study is to propose an occlusion-robust spherical camera localization method using 3D model of the environment. Instead of using specific features, which may cause low estimation accuracy in environment with occlusion, the color information of all pixels in images is used for the estimation. Camera pose is estimated by minimizing the color difference of the taken image and the image generated using former frame and 3D model. To reduce the influence caused by occlusion, a robust evaluation function is used in the estimation process. The effectiveness of the proposed method was confirmed by experimental tests.
Implementation of Secure Multicast Routing for Cognitive Satellite-Terrestrial Networks

The research aims to be a secured multi-casts transmission for cognitive satellite-terrestrial networks with Multiple input and Multiple Output (MIMO) antenna eavesdroppers where the satellite provides the group of legitimate users with a common confidential message and where interferences from the terrestrial base station (BS) are used to enhance the safety of the satellite link. Idea of this work is to reduce total transmitting power, subject to satellite connectivity secrecy rates and the terrestrial link data rate. The resulting problem of optimization involves the joint optimization of the non-convex and challenging information covariance matrices at the SAT and the BS. In order to turn the non-convex constraints into linear ones, we introduced a successive concave approximation method and proposed an efficient iterative algorithm. The results of the simulation show that the proposed algorithm is superior.
Ensembling Classical Machine Learning and Deep Learning Approaches for Morbidity Identification From Clinical Notes

The past decade has seen an explosion of the amount of digital information generated within the healthcare domain. Digital data exist in the form of images, video, speech, transcripts, electronic health records, clinical records, and free-text. Analysis and interpretation of healthcare data is a daunting task, and it demands a great deal of time, resources, and human effort. In this paper, we focus on the problem of co-morbidity recognition from patient’s clinical records. To this aim, we employ both classical machine learning and deep learning approaches. We use word embeddings and bag-of-words representations, coupled with feature selection techniques. The goal of our work is to develop a classification system to identify whether a certain health condition occurs for a patient by studying his/her past clinical records. In more detail, we have used pre-trained word2vec, domain-trained, GloVe, fastText, and universal sentence encoder embeddings to tackle the classification of sixteen morbidity conditions within clinical records. We have compared the outcomes of classical machine learning and deep learning approaches with the employed feature representation methods and feature selection methods. We present a comprehensive discussion of the performances and behaviour of the employed classical machine learning and deep learning approaches. Finally, we have also used ensemble learning techniques over a large number of combinations of classifiers to improve the single model performance. For our experiments, we used the n2c2 natural language processing research dataset, released by Harvard Medical School. The dataset is in the form of clinical notes that contain patient discharge summaries. Given the unbalancedness of the data and their small size, the experimental results indicate the advantage of the ensemble learning technique with respect to single classifier models. In particular, the ensemble learning technique has slightly improved the performances of single classification models but has greatly reduced the variance of predictions stabilizing the accuracies (i.e., the lower standard deviation in comparison with single classifiers). In real-life scenarios, our work can be employed to identify with high accuracy morbidity conditions of patients by feeding our tool with their current clinical notes. Moreover, other domains where classification is a common problem might benefit from our approach as well.
Quintuple Band Antenna Design Using Stacked Series Array For Millimeter Wave

Millimeter wave applications require efficient array antenna designs to fully utilize the spectrum. A quintuple band antenna is proposed for millimeter wave applications. Dual-layer substrate technology is utilized to achieve multiple resonances at higher frequencies ranging from 26GHz-40GHz. Twenty-five antenna array configurations were simulated and analyzed to develop the model. The resonances achieved correspond to the number of radiating patches. The operating frequencies of this design correspond with single band 27.64 GHz, dual band 24.18, 28.77 GHz, triple band resonances 23.18, 25.63, 35.79 GHz, quadruple band resonances 24.58, 26.77, 29.33, 34.43 GHz and quintuple band resonances 23.79, 25.37, 28.29, 31.69, 33.53 GHz. The proposed quintuple band antenna can be used for millimeter wave applications in all bands.
Current Trends And Practices In Smart Health Monitoring And Clinical Decision Support

Smart Health indicates the use of new technologies in the healthcare sector. Literally it means “intelligent health”, and the intelligence referred to is digital, guaranteed by innovative tools such as Internet of Things (IoT) devices, communication technologies, cloud computing, artificial intelligence (AI) and big data. Thanks to sensors and devices connected to patients, such as technologically advanced bracelets and watches, it is possible to collect data on the state of health of people and treat them, even remotely, anticipating critical situations before they occur. The use of IoT to support healthcare leads to suitable recommendations and set the best policies for improving the quality of patients life, assisting practitioners and healthcare providers in decision making, collecting and exchanging information, helping to prevent events, such as a heart attack or an illness. This is possible thanks to the use of AI, which process immense amounts of data to anticipate future events. In recent years, AI based on deep learning has sparked tremendous global interest and is impacting also in healthcare. Deep learning has been widely adopted in image recognition, speech recognition and natural language processing. It could be the vehicle for translating big biomedical data into improved human health. The paper presents a literature review conducted to determine the most important technologies, methodologies, algorithms and models for smart health systems. In addition, the main application areas and challenges of smart health were explored.
Cooperative Driver Pathway Discovery by Hierarchical Clustering and Link Prediction

Identifying driver pathway is a critical step to uncover the natural laws of the occurrence and progression of disease. Many studies show that multiple pathways often function cooperatively in carcinogenesis. However, how to computationally identify cooperative driver pathways of cancers is not well studied yet. Existing cooperative driver pathway identification methods either suffer from single type of genetic information source or computation difficulty. In this paper, we proposed a method (CDPLP) based on hierarchical clustering and link prediction. CDPLP firstly devises a new similarity metric to quantity the exclusivity and co-expression of two gene modules, and thus to obtain gene sets with exclusivity by hierarchical clustering. Next, it uses link prediction on the pathway-pathway interaction network to replenish the interactions between pathways. After that, CDPLP combines the gene sets and updated pathway network to discover the pathway pairs with high functional interaction and occurrence as cooperative pathways. CDPLP can make full use of multiple genetic information sources such as the mutation data, gene-gene interaction data and pathway-pathway network, and facilitate the optimization solution. We evaluated the performance of CDPLP on TCGA breast cancer (BRCA) dataset and compared it with other popular methods. The results show that cooperative driver pathways identified by CDPLP are highly associated with the target cancer, and are involved with carcinogenesis and several key biological processes.
Instrumental requirements for high resolution imaging

The resolution of modern transmission electron microscopes reaches the physical limits imposed by lens aberrations and energy width. One of the many conditions to be fulfilled, the alignment of illuminating and imaging beam onto the coma‐free objective axis, is particularly discussed here since axial coma cannot be detected by the usual resolution‐checking methods. Space consumption of specimen stages prevents the full utilization of the magnetic saturation limit only in the 100 keV range. With higher energies, this handicap is obviated, and some additional advantages can be gained which promote material investigations at atomic resolution, and which are presently utilized in instrumental research projects. High resolution with biological specimens has up to now been unsuccessful because of radiation damage. Optimum utilization of all electrons scattered at the specimen must thus be given priority over optical resolution. Important instrumental requirements are minimum exposure beam control, imaging modes with high collection efficiency, and recording devices with high detection quantum efficiency connected on‐line to image processors. A remarkable decrease in beam sensitivity of organic crystals, by more than one order, has been found by cooling the specimen down to 4 K which, by the use of superconducting lenses, can be combined with both ultra high vacuum and the stability requirements for high resolution. Yet up to now, such protection has not been achieved with He cryostates in conventional lenses, perhaps because a temperature increase even of only a few degree K is harmful.
Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.
Malware Detection Based on Feature Library and Machine Learning

In this paper, we propose a malware detection method based on feature library and machine learning. By using a combination of static and dynamic feature extraction method, we select 8 types of static features to build a feature library. In addition, for potentially unknown malwares, we use 9 groups of dynamic features to train a support vector machine model, and give interpretable detection results based on the influence of different features. To verify the performance of our method, we conducted various experiments on a total of 129,013 malware samples and compared the results with other schemes, demonstrating the effectiveness of our method.
Research on Different Current Components Compensation Techniques in Nonsinusoidal Voltage Conditions

The paper presents a research on different methods of load nonlinearity compensation by a shunt APF when the grid voltage is distorted. The basis of the research is the Physical Components of Currents Power Theory by L. S. Czarnecki. Three basic compensation techniques are analyzed: sinusoidal current, quazi-linear load and constant active power consumption. Influence of each method on separate currents' physical components is shown as well as the power losses. The results obtained are verified by computer simulation.
Fast Finite Element Modeling Method for TSV Interposer on Specially Developed SAAS Cloud

The coupled thermo-mechanical field analysis of TSV interposer is a typical multiscale simulation problem, it faces great difficulty of numerous TSVs and complex micro structures. To get balance between accuracy and efficiency, global/local finite element (GLFE) strategy often be used in the multiscale coupled field analysis. In steps of the GLFE strategy, global coarse mesh evaluation plays the key role and the mesh model of this step determines the calculation effect. Currently, finite element modeling for TSV interposer is mostly finished manually with the help of comprehensive finite element software. To improve the laboriously and time-consuming manual modeling procedures, a finite element modeling method specifically oriented for TSV interposer is proposed. The modeling method is an imitation of fabrication procedures, it consists of layer based geometry modeling, parametric meshing, automatic contact detection, automatic boundary condition building and automatic loading processes. TSV discretization methods affected the quality of the mesh model directly, 4 kinds of TSV discretization methods are proposed in this paper, namely: Fine Mesh Method, Single Point Method, Single Mesh Method and Equivalent Model Method, they reach different equilibrium points on model size and accuracy, feeding need of different simulation scenarios. As the implementation of the modeling method a SAAS cloud system is built, the system run in B/S mode and it can make the modeling procedures easier. Finally, with the help of the modeling method and the SAAS cloud system, a thermo-mechanical analysis of TSV interposer based 2.5D RF module is done. The comparison of the time consumption and results with comprehensive finite element software ANSYS Workbench indicates that the cloud system can get great efficiency with enough accuracy.
Study on the Effect of Sodium Polyacrylate and its Compounds on Artificial Warm Fog Dissipation

Sodium polyacrylate as a kind of high absorbent resin, has an excellent water absorption performance, is low in price and isn’t corrosive. Compounds of different particle sizes and components were prepared using sodium polyacrylate and sodium chloride. They were used in the fog chamber to disperse warm fog respectively then. Experiments were carried out to investigate how particle size and composition affect the warm fog dissipation. Results as follows are obtained: the time light transmittance recovers to 60% (t60%) is 85min when the fog dissipates naturally; 30g sodium polyacrylate whose average size is 30μm has a good effect on warm fog dissipation, t60% being 14min; when the sodium polyacrylate and sodium chloride are mixed by 1:2 in mass ratio, the fog elimination effect is the best, with t60% 6min. The reasons are analyzed at the same time.
Towards state estimation of Persidskii systems

A state estimation scheme for a class of Persidskii systems is introduced in this paper. Two distinct sets of conditions are formulated for robust stability and convergence of the state estimation error using the theories of input-to-output stability (IOS) and input-to-state stability (ISS). These conditions for the nonlinear error dynamics are established in the form of linear matrix inequalities. Two numerical examples are presented to illustrate the effectiveness of the proposed results.
Distributed Control of Mobile LTI and LPV Agents Using Induced L2 to L∞ Norms

This paper considers formation control for heterogeneous networks of locally controlled linear time-invariant (LTI) and linear parameter-varying (LPV) agents using a decoupled control architecture. For such networks, bounds on the agents’ peak tracking error are derived based on the induced ${{\mathcal{L}}_2}$ to ${{\mathcal{L}}_\infty }$ system norm. Furthermore, we propose linear matrix inequality (LMI) conditions to synthesize local state-feedback controllers that minimize the bound on the tracking error and additionally demonstrate that applying H∞ synthesis techniques leads to a comparable performance. Finally, the approach of this paper is illustrated for LTI and LPV agents using two examples.
A Linearly Convergent Algorithm for Multi-Agent Quasi-Nonexpansive Operators in Real Hilbert Spaces

This paper studies the fixed point finding problem for a global operator over a directed and unbalanced multi-agent network, where the global quasi-nonexpansive operator is sum separable and composed of a family of local operators. In this problem, each local operator is privately known to only each individual agent, and all local operators are assumed to be Lipschitz continuous. To deal with this problem, a distributed (or decentralized) algorithm, called Distributed quasi-averaged Operator Tracking algorithm (DOT), is proposed and rigorously analyzed, and it is shown that the algorithm can converge to a fixed point of the global operator at a linear (or exponential) rate under a bounded linear regularity condition, which is strictly weaker than the function’s strong convexity in convex optimization. To validate the proposed algorithm, a numerical example is provided finally.
Accelerated Testing of SiC Power Devices

Power metal-oxide-semiconductor field-effect transistors (MOSFETs) experience conditions of high field during normal operation. During switching conditions, unexpected transient events may occur which force devices into avalanche or short circuit conditions. Moreover, silicon carbide devices typically experience higher fields in the gate oxide and drift regions than comparable Si devices due to channel and drift property differences. A summary of 4H-SiC MOSFET reliability and ruggedness test results are reported here. Reliability tests under high field conditions: positive-bias and negative-bias temperature instability (PBTI, NBTI) to examine threshold stability; time-dependent dielectric breakdown (TDDB) for gate oxide lifetime extrapolation; high-temperature reverse bias (HTRB); and HTRB testing under high neutron flux to determine terrestrial neutron single-event burnout (SEB) rates. High-power ruggedness evaluation is presented for SiC MOSFETs under forced avalanche conditions (unclamped inductive switching (UIS)) and under short-circuit operation to bound device safe operating areas. Overall results demonstrate the intrinsic reliability of SiC MOSFETs.
Iterative H-norm Estimation Using Cyclic-Prefixed Signals

The problem of estimating the largest gain of an unknown linear and time-invariant filter is studied, known as the ${{\mathcal{H}}_\infty }$-norm estimation problem. The approach presented in this paper is iterative and corresponds to the combination of two state-of-the-art methods: Power Iterations and Weighted Thompson Sampling. The combination is done by means of a well-known technique in communications known as a cyclic prefix, in which the last points of a signal are prepended to it. This allows to take considerably more exact measurements of the frequency response of the system at a set of equispaced frequencies. The discussion is complemented with a simulation study, showing that the proposed algorithm has an increased speed of convergence to the quantity of interest.
A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering

The detection of partial discharge (PD) is a crucial method to evaluate the insulation status of transformers. The main difficulties of the current localization algorithms are the complexity of the solution and sensitivity to time delay errors. This article proposes a PD localization method in transformers based on linear conversion and density peak clustering (DPC). First, to reduce the complexity of solving the localization equations, the nonlinear localization equations are transformed into linear localization equations by eliminating the second-order terms. Then, to reduce the influence of time delay errors on localization accuracy, the initial localization values are located by multiple acoustic emission (AE) sensors. Finally, the optimal PD coordinates are determined by clustering the initial location values using density peaks clustering algorithm with automatic finding centers (AFC-DPC). The experimental results show that the proposed method can improve PD localization accuracy in transformers, and the average localization error is 5.30 cm.
Implementing Multiple-Social-Network Meta-APIs to Support OSINT Programming

Open source intelligence (OSINT) collects a set of approaches, methodologies, and tools, to make investigation about individuals on the basis of information publicly available over the Internet. For this important task, online social networks (OSN) represent nowadays the main source of information, so that large attention should be devoted to any approach that improves the capabilities of investigating over OSNs. The aim of this paper is to highlight that, despite the availability of tools and platforms for OSINT which also exploit OSNs, there is a gap in terms of software engineering perspective. Indeed, there is no a programming paradigm oriented to the abstract concept of online social network, independent of the specific instance of this concept. This resumes the multiple-social-network perspective in a software engineering key, and translates into the definition of a set of meta-APIs allowing the programmer to write generic and polymorphic code, naturally oriented to social-network investigation. This paper, born through the experience of an industrial project, shows the above claim by choosing some relevant fragments of the proposed framework.
Adaptive Dynamic Programming for Control: A Survey and Recent Advances

This article reviews the recent development of adaptive dynamic programming (ADP) with applications in control. First, its applications in optimal regulation are introduced, and some skilled and efficient algorithms are presented. Next, the use of ADP to solve game problems, mainly nonzero-sum game problems, is elaborated. It is followed by applications in large-scale systems. Note that although the functions presented in this article are based on continuous-time systems, various applications of ADP in discrete-time systems are also analyzed. Moreover, in each section, not only some existing techniques are discussed, but also possible directions for future work are pointed out. Finally, some overall prospects for the future are given, followed by conclusions of this article. Through a comprehensive and complete investigation of its applications in many existing fields, this article fully demonstrates that the ADP intelligent control method is promising in today’s artificial intelligence era. Furthermore, it also plays a significant role in promoting economic and social development.