Miracle-dz/UniFAR-FTUs huggingface dataset:
Light Field Angular Super-Resolution using Convolutional Neural Network with Residual Network

Light-field images are used in many areas because of its advantage that being able to film multiple images at once, but they are limited in their actual use due to their low resolution. To overcome this drawback, Light-field super-resolution studies are actively being conducted to expand the resolution of light-field images. Light-field super-resolution are divided into two categories. The first is to increase the spatial resolution and the other one is to increase angular resolution. In this paper, we propose an angular super-resolution algorithm based convolution neural network (CNN). Experimental results show that the proposed algorithm produces higher PSNR and SSIM values than existing methods.
Kernel Density Estimation for Optimal Detection in All-Bit-Line MLC Flash Memories

NAND flash memories have recently become the main component of large-scale non-volatile storage systems. Recent studies have shown that various error sources degrade the Multi-level cell (MLC) memory performance, including intercell interference, retention error, and random telegraph noise. Accurate integration of these error sources into the analytical model to optimally derive the governing probability distributions and consequently the detection thresholds to minimize error rates lie at the heart of MLC research. Utilizing static derivations will not address the detection problem, as aforementioned error sources exhibit a strong non-stationary behavior. In this paper, a novel low-complexity implementation of a non-parametric learning mechanism, kernel density estimation, shall be used to periodically estimate the underlying probability distributions and hence approximate the optimal detection performance for time-varying all-bit-line MLC flash channel.
A Graphical Performance Tool for Design and Comparative Assessment of Predictive Torque Control Methods in Motor Drive Applications

In this paper, a graphical performance tool (GPT) is proposed to select, among several existing Predictive Torque Control (PTC) methods, an appropriate approach to drive a motor for given specifications. To achieve a fair trade-off evaluation, switching frequency, current, and torque total demand distortions have been considered depending on PTC parameters. Since, in the conventional PTC method, torque and flux weighting factors (WFs) strongly influence the behavior of the motor. Thus, to avoid an unfair comparison due to a non-optimal tuning, the proposed GPT is used to show each performance criterion when parameters and operating points change. Simulation results of three operating points show that there is only one sharing common zone where current harmonics and torque ripples are near zero. This area has been named the optimal tuning zone for PTC controllers and is used to select their parameter values. After obtaining these desired parameter values, a trade-off performance evaluation is made over a wide operating range to compare different PTC controllers with and without WF that are proposed in the literature.
Piezoelectric Pzt Thin Films: Deposition, Evaluation and Their Applications

Piezoelectric thin films have attracted attention as key materials of next generation functional micro-devices. Application products of piezoelectric thin films have been launched such as micropumps for inkjet heads, micro gyrosensors and dual stage actuator (DSA) for hard disk drive (HDD). In addition, piezoelectric vibration energy harvesters (PVEHs) are one of the promising applications of the piezoelectric thin films. In these piezoelectric microelectromechanical systems (MEMS), device performance strongly depends on the function of piezoelectric thin films, especially their transverse piezoelectric properties. Therefore, deposition of high-quality piezoelectric thin films is a crucial technology in piezoelectric MEMS. On the other hand, precise measurement of piezoelectric coefficient of thin films was indispensable for the research and development on piezoelectric thin-films devices. In this paper, we introduce fundamental technologies of piezoelectric thin films and their related micro-devices, especially focusing on sputtering deposition of PZT thin films and evaluation methods of their transverse piezoelectric properties.
Low-Latency Service Schedule Orchestration in NFV-based Networks

The Fifth Generation (5G) era is bringing tremendous new network capabilities enabling diverse services belonging to different business verticals (i.e., manufacturing, automotive, etc.) and provided with top-notch Quality of Service (QoS) (i.e., ultra-low latency, ultra-reliability, etc.). Empowered by soft-warization technologies such as Network Function Virtualization (NFV), 5G networks are envisioned to be agile, sustainable and self-organized. NFV promotes the automated provisioning of Network Services (NSs) through processing their traffic by a chain of Virtual Network Functions (VNFs). As VNFs are shared between multiple NSs, a clear approach to map and schedule the carried traffic of these services is required. Hence, in this paper, we solve the Latency-Aware Service Schedule Orchestration problem (LASSO) that jointly addresses the mapping and scheduling of services to VNFs. We formulate the problem as a Mixed Integer Linear Program (MILP) and we present ENCHAIN, a novel game-theoretic approach exploiting a scalable solution for the LASSO problem while providing each NS the freedom to decide on its own mapping and scheduling solution.
SMASH: A Malware Detection Method Based on Multi-Feature Ensemble Learning

With the increasing variants of malware, it is of great significance to detect malware and ensure system security effectively. The existing malware dynamic detection methods are vulnerable to evasion attacks. For this situation, we propose a malware dynamic detection method based on mufti-feature ensemble learning. Firstly, the method adopts the combination of software features such as API call sequence with high detection precision and low-level hardware features such as resistance to evasion the memory dump grayscale and hardware performance counters. Secondly, we improve each feature based on the original research. We select a more advanced classifier model to improve the detection precision of a single feature. Finally, an ensemble learning algorithm composed of multiple classification algorithms detects malware, the multi-features can describe malware behavior from multi-dimensions to improve detection performance. We use a large number of malware sample dataset to experiment, and the results show that our detection method can obtain good detection precision rate, and is better than other recently proposed dynamic detection methods in anti-evasion performance.
Representation of Job-Skill in Artificial Intelligence with Knowledge Graph Analysis

This study analyses the relationship of different key skills of artificial intelligence (AI) used in the job market. For this, we represent it with a knowledge graph and use a Long Short-term Memory Network to study interactions between these key skills. First, a knowledge graph is build with a rule-based method about these skills in the job markets. Then, the graph is visualized to discover knowledge relationship. Jobs in AI can be classified into two categories: general algorithm jobs and specific focus jobs. Skills of different jobs in AI are very different. Python and Linux are the most necessary key skills for all jobs in AI. Revealing all these key skills in jobs in AI is useful and provide a guideline for job-seekers, companies and universities.
Deap Based Actuator for Tactile and Microfluidics Applications

This paper presents a novel DEAP (dielectric electroactive polymer) based actuator for the generation of refreshable 3D deformation shapes. The shape formation is obtained by a controlled and multiple cooperative buckling behavior of a PDMS strip-like membrane using patterned, isolated electrodes along the strip and applying an appropriate voltage to these electrodes. The size of the deformed surface can be scaled from some hundred micrometers to some centimeters. The fabrication process of a test sample is shown and the maximum deflection height of the presented actuator is about 260 μm at an applied voltage of 3900 V.
Improved Peformance of Micro-Preconcentrator Using Silicon Nanowires as a Surface Template

In this paper, silicon nanowires (SiNWs) with high specific surface area were used as a surface template to improve the enrichment ability of micro-preconcentrator (μPC). The μPC was fabricated based on MEMS technology. The SiNWs were grown in-situ on the surface of the cavity of the μPC, and then the Tenax-TA was coated. The enrichment abilities of VOCs of the μPC with Tenax-TA and Tenax-TA using SiNWs as a surface template (SiNWs + Tenax-TA) were respectively measured. The enrichment ratio of butyl acetate of the μPC with Tenax-TA was 67.4, and the μPC with SiNWs + Tenax-TA was 193.8, which had an enhancement as high as 188%, suggesting that SiNWs as the surface template of Tenax-TA effectively enhanced the enrichment ability.
Plasmon-Assisted Selective Enhancement of Direct-Band Transitions in Multi-Layer MoS2

Multi-layer MoS2 offers numerous advantages over mono-layer MoS2 in terms of improved absorption, enhanced emission and high optical density of states, which make it a better candidate for devices. However, the selective tuning of the direct-band emissions in multi-layer MoS2 is essential for various optoelectronic applications. In this work, we have demonstrated a systematic tuning of direct-band transitions in multi-layer MoS2 using Au coating prepared by sputtering and e-beam evaporation. We observed nearly 80-fold enhancement in the ratio of direct-to indirect-peak intensity with Au coating (e-beam) to that of the direct-to indirect-peak intensity of pristine samples. Along with that, complete quenching in the emissions corresponding to I-valley has been observed with Au coating. This work provides new dimensions towards the realization of plasmonic/optoelectronic devices based on multi-layer MoS2.
Indoor Floor Change Detection Using Barometer

Indoor navigation became one of the most popular research direction recently, yet it is still being ripening. Horizontal positioning on the floor map is available by measuring radio signal power levels existed in the environment such as Wi-Fi and/or beacons. However, it is not precise enough against height differences especially when the signal source stay behind walls. Therefore, vertical positioning is mainly relied on atmospheric pressure sensors since there is a proven efficiency when it comes to relative height measurement. It has also some drawbacks since the air pressure is highly dependent on environmental conditions. In this study, it will be investigated that how the measurements from different barometric pressure sensors located at different floors in a building change over time firstly. Then these results will be assessed to extract the patterns between the measurements and floor levels.
Pneumonia Detection from Radiography Images using Convolutional Neural Networks

Pneumonia continues to be the leading cause of child mortality in children under the age of five, and 2,400 children, most of whom are babies under 2 years of age, die from pneumonia. In this study, an automated detection system is proposed for the diagnosis of pneumonia with chest radiography images. With the transfer learning technique, ResNet-152 convolutional neural network was customized to recognize pneumonia from radiography images. With this customized architecture, a recognition success of 97.4% was obtained in the detection of pneumonia disease without any preprocessing of raw data or manual feature extraction on radiography images. This model, which was proposed for the detection of pneumonia, found to be more successful when compared with the other successful studies in the literature.
Soft Spiral-Shaped Micro-Swimmer with Propulsion Force Control by Pitch Change

This paper describes a soft spiral-shaped micro-swimmer with propulsion force controlled by pitch change. Soft spiral-shaped micro-swimmers, made of stimuli-responsive hydrogel encapsulating magnetic nanoparticles, were continuously formed by using a bevel-tip capillary. We achieved the pitch change of the double-layered micro-swimmer by temperature stimulus and confirmed that the propulsion force of the swimmer was controlled by the pitch change. We believe that our micro-swimmer could pave the way to various microscale biochemical applications such as autonomous soft-robots and soft micro-probes for intricate environment.
A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting

With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.
Identification of Bluetooth-Enabled IoT Devices Through Syntactic Similarity Techniques

Moving into 2019, the number of new devices is growing exponentially, expecting more than 50 billion Bluetooth-enabled devices to be worldwide connected by 2020. As a result, emerging back-end support technologies not only have to anticipate this dramatic increase of connected devices, but also their heterogeneity. Considering this existing gap in the Internet of Things (IoT) area, this paper proposes a mechanism for discovering, connecting, and identifying heterogeneous Bluetooth-enabled IoT devices of unknown device type, in order to be integrated into different platforms. This mechanism implements three (3) stages to successfully identify an unknown device. Initially, the discovery and the connection of both known and unknown devices take place so as to retrieve explicitly required specifications of these devices. Sequentially, the measurement of the syntactic similarity of all the devices' specifications and the comparison among the derived results occurs. Based on these results, the estimation of the overall syntactic similarity takes place, identifying finally the device type of the connected unknown devices. The prototype associated with this paper provides an example of this mechanism, demonstrating in detail each stage.
Scalable Subspace Clustering via Adaptive Low-rank Model

Graph based Subspace Clustering (SC) has been a major issue in many real-world task and low-rank representation based methods have widely been used in graph construction. However, both above methods need huge computation in order to solve trace-norm minimization problem, which may not be scalable to large-scale data. In this paper, we develop an scalable and effective low-rank model for subspace clustering. Motivated by the basic idea of Robust Principal Component Analysis (RPCA), we adopt an iterative procedure to calculate the optimal solution of RPCA, where the low-rank matrix is reformulated by two factorizations. By further imposing the group sparse constraint on such factorizations, the coefficient matrix S can both achieve sparcity and capture the global structure of whole data. Benefitting from S, we then develop a new Graph based SC framework, where we have involved the adaptive low-rank model and SC into a single optimization problem. Therefore, the discriminative information learned by SC can be provided to improve the discriminative ability of low-rank graph construction, while the updated graph can further enhance the Clustering results of SC. Extensive simulations have verified the effectiveness of the proposed methods.
Seasonal Surface Urban Heat Island Analysis

Surface Urban Heat Islands (SUHIs) show large temporal variability, which introduces considerable uncertainty into studies based on a single or a few acquisitions. Besides short-term and more random influences, they exhibit considerable systematic diurnal and seasonal variation, which has been investigated in several recent studies. Here an enhanced method for seasonal SUHI analysis based on time series analysis is presented and tested in the southwest United States using MODIS day- and nighttime land surface temperature data. Despite large scatter at the daily time scale, the method is able to identify and parameterize seasonal signals in time series. The seasonal patterns vary substantially between cities. Coastal cities show the largest nighttime SUHI intensity in spring and the lowest in late summer, while continental cities have their SUHI intensity peaks in September and October. During summer, some cities show a daytime cool island. Our study underlines the need to consider temporal patterns in SUHI assessment. The method proved useful to investigate the SUHI seasonality.
Analýza a realizace kmitočtového filtru přeladitelného změnou parametru aktivního prvku

This work analyzes tuning capabilities of different fully integrated active filter topologies. Work only deals with continuous time active filters. Topologies described in this work differ in type of active element and in method of frequency tuning. Techniques of tunning are proved on second order low pass filter. Filter topologies are compared from tunning capabilities and from point of total harmonic distortion. The main building block of all filters is integrator.
Improved Visual Vocabularies for Scene Classification of High Resolution Remote Sensing Imagery in Urban Areas

The improvement of spatial resolution of remote sensing images provides more and more ground details, which makes it possible to better interpret unban land use and analyze urban functional areas. The bag-of-visual-words (BOVW) model becomes a well-known method to deal with such land-use scene classification problems. Generally, the unsupervised k-means clustering method is used to construct visual dictionaries, in which there often exist large amounts of local features to cluster in the visual vocabulary construction stage, largely affecting the computational complexity and hindering the schedule of visual dictionary generation. To solve the above mentioned problems, this paper proposes a two-phase k-means clustering based visual vocabulary construction method. The proposed method can add information of predefined scene categories from different images, which is beneficial for the selection of initial cluster centers, and, to a certain extent, alleviates the problem that the amount of samples to be clustered in a single course of clustering can be too large. Experimental results on the public UCM-21 dataset show that compared with the traditional method, the proposed one can not only reduce the dictionary construction time but also help improve the classification accuracy.
A Total Energy Attitude Control System Strategy for Rigid Spacecraft

This paper aims to design a controller based on the Total Energy Control System (TECS) method, to solve the attitude trajectory tracking problem for rigid spacecraft. TECS objective is to command the total energy rate using a proportional-integral (PI) controller. The attitude control objective fixes the total energy rate objective. The spacecraft attitude is represented directly in the manifold of the Special Orthogonal Group SO(3). Ultimate boundedness of the closed-loop trajectories is concluded using Lyapunov theory. The second objective of this paper is to test the attitude controller through real-time experiments, employing a testbed based on an underwater prototype with neutral buoyancy that can rotate unrestricted around the three axes.
An Affect Computing based Attention Estimation

Advancement in ubiquitous system has the potential to change the way we interact with the environment around us. In current smart systems, body language and visual cues form an important part of input to analyse and interpret the human perception. Understanding the emotional state of a human will help in generating a personalized response accordingly. Even-though there are multitude ways of capturing the affect of a person, common tools are found in computer vision techniques. In this paper, we present an attention measurement technique in a classroom setting using computer vision. Understanding the cognitive state of a student attending the class will help in improving the quality of education. Results indicate that the proposed system can be used to detect affective state automatically.
Semiautomatic Extraction of Belt-like Roads from High Spatial Resolution Remotely Sensed Imagery based on Self-adaptively Variable Width of Template Matching Metho

A novel semiautomatic ribbon roads extraction method for high spatial resolution remotely sensed (HSRRS) imagery based on self-adaptively variable bandwidth template matching is proposed in this letter. At first, a human operator inputs three seed points to initialize the position of the road, including the starting points, road width and road direction. Then, a rectangular template is used to estimate the best way forward point by the template matching, so the automatic extraction is triggered. According to the actual road conditions, the method can increase or decrease the width of the rectangle template self-adaptively to extract complex roads by building four small rectangle templates. To finish the whole road network, the above tracking process is repeated. UCX aerial imagery are used to validate our methods, and the results show that compared with traditional profile template matching algorithm, our method not only decreases the number of tracing errors and manual intervention times significantly, but also improves the accuracy and efficiency of the semiautomatic extraction road.
Secure Application-Oriented Network Micro-Slicing

Future networks will be expected to adapt to various requirements from industries, the Internet of Things, consumers, etc. To do so, 5G network standards have so far defined a few classes of network traffic or slices, with highly different characteristics to be supported: ultra-reliable and low latency communications (uRLLC), massive Machine Type Communications (mMTC), extreme mobile broadband (eMBB) [1]. In this demonstration, we introduce finer-grained network micro-slices that can be overlaid onto such 5G slices. Micro-slices allow to provide further context discrimination thanks to network isolation, by dynamically configuring connectivity between end-devices and applications according to requirements expressed by the applications. We illustrate the use of such micro-slices in the context of an event taking place in a stadium. The demonstration setup invites visitors to on-board their own smart-phones onto safety-oriented and/or gaming-oriented micro-slices for a tailored digital experience as spectator, civil-servant or both.