Miracle-dz/UniFAR-FTUs huggingface dataset:
RFID Localization Based on Multiple Feature Fusion

As one of the enabling technologies for Internet of Things (IoTs), radio frequency identification (RFID) has been widely adopted in many applications. Among others, RFID-based localization has attracted much research attention in recent years. Existing RFID localization algorithms are usually based on single signal feature, e.g., received signal strength (RSS) or phase information. These algorithms cannot achieve high localization accuracy and low delay simultaneously: Algorithms based on RSS usually suffer from low localization accuracy, while algorithms based on phase suffer from large localization delay because they need to collect a large number of phase readings to resolve phase ambiguity. In this paper, we propose an RFID localization algorithm that can achieve both high accuracy and low delay by fusing multiple types of signal features. We first use the RSS measurements to quickly shrink the possible region of the target tag, and then use phase measurements to refine the position estimation. Experiment results demonstrate the effectiveness of this novel design.
Rail-to-rail two stage CMOS operational amplifier: A comparative analysis

This paper presents different low voltage CMOS operational amplifiers. Threshold reduction techniques are foreseen with the utilization of Bulk driven approach to reduce the supply voltage. Disadvantages identified with bulk driven approach are talked about and strategies to enhance them are proposed. To increase transconductance of the Op-Amp, cascode input stage and cross coupled output stage are utilized with no expansion in area as well as bulk driven MOSFETS can be utilized alongside partial positive feedback. These methods help in increment of dc gain. Supply voltage scaling has been the central factor because of which gain increments, efficient power utilization is there and on-chip are reduce.
Removing rain based on a cycle generative adversarial network

Single image de-raining is a difficult problem due to its inherent ill-posed nature. Many researchers have done some related work and attempt to introduce prior information to get a good rain removal result. In this paper, we propose a CycleGAN-based new deep convolutional neural network (CNN) to solve single image de-raining problem. Our idea mainly comes from cycle generative adversarial networks (GANs). CycleGAN was initially introduced for style transfer task. However, we found that the thought of dual learning used in CycleGAN is also applicable for rain streaks removal. The separation of the rain streak and the background is the most important and the most difficult part of this problem. By adding a reverse mapping, dual learning can thoroughly remove rain streaks with the background been preserved and ease the training of both generators. Inspired by the deep residual network (ResNet), we also introduce residual image learning to our generators, which directly reduce the mapping range from input to output and make the mapping process easier. Furthermore, In order to prevent the model from over fitting on the synthetic training data so that the real-world rain line cannot be effectively removed, we add the Squeeze-and-Excitation (SE) block after our generator's convolution layers, which can push the network to learn more useful features, reduce the calculation, and reinforce the network's generalization ability. Test results based on real and synthetic images show our method outperforms many recent state-of-the-art single image de-raining methods in visual performance and Quantitative aspects.
An improved spatio-temporal context tracking algorithm

Spatio-temporal context (STC) algorithm transforms the tracking process into a series of processes to find the extremum of the confidence map and fully uses the density context information around the target, which makes the algorithm rapidity and robustness. However, STC cannot deal with fast motion, motion blur and the rapid change of scale, which will cause the spatial model update error and result in the failure of the algorithm to accurately extract the target area. To deal with the problem, an improved spatio-temporal context algorithm is proposed in this paper. Firstly, the position prediction based on the target motion vector is introduced, the motion information of the target is fully taken into account to improve the accuracy of the STC algorithm in extracting the target current position. Secondly, the scale correlation filter is used to improve the STC algorithm, so that the algorithm can accurately and completely extract the target area. Finally, experiment results on public data set are provided to show the effectiveness and robustness of our proposed algorithm.
Velocity matching compliant control for a space robot during capture of a free-floating target

Space robotics provides a reliable solution to accomplish manipulation tasks in orbit without the presence of astronauts, an endeavor that is both dangerous and costly. A particular field of application is the servicing of a defective target satellite by a servicer satellite equipped with a robotic arm. Considering the phases a servicing mission may have, one of them is particularly critical: the contact phase between the robot and the target. The large forces involved as well as the short time-scales during which these forces are acting represent a challenge for the design of such missions. Therefore it is required to determine the most appropriate control strategy before, during and after the contact. This work aims to address the issue of finding the aforementioned strategy, under the condition that, after the contact, the servicer and the target satellites have the same velocity. In this way the capture of the target is guaranteed. In this paper, a novel method for tuning an impedance control scheme is presented, which ensures post-impact velocity matching between the servicer and target satellites. The method is based on the assumption that no knowledge on the position and velocity of the target is available. Lacking information on the contact duration, a passively compliant element is introduced in order to prolong the contact. The resulting dynamical system is analyzed in time domain with a simplified model. As a result, optimal parameters for the controller as a function of the target mass and the passive compliance parameters are obtained. Simulations are carried out to validate the theory which are confirmed by experiments on a one degree of freedom robot.
SonicOperator: Ultrasonic gesture recognition with deep neural network on mobiles

With the increasing popularity of smart mobile phones, gesture recognition techniques that enable always available interaction are highly demanded. In this paper, we propose SonicOperator, a system for in-air dynamic gesture recognition based on ultrasound without extra sensors in smart phones, instead of vision-based solutions with camera. Inaudible ultrasonic wave is emitted by speaker and received by microphones, then the frequency-shifted can be modeled with the Doppler effect. Each gesture consists of one or more postures sequentially in chronological order as the duration to perform a gesture is different. Furthermore, recurrent neural network is able to memorize long temporal context for time sequential gesture, to signalizes a soft target in objective function in recurrent neural network training. The experiments demonstrate that the SonicOperator prototype achieves prominent performance through gesture commands in real applications.
Distribution and Capacity for Utilization of Lower Fatty Acids of Phototrophic Purple Nonsulfur Bacteria in Wastewater Environments

Populations of phototrophic purple nonsulfur bacteria (PNSB) of up to 104-107 CFU ml-1 were found in photosynthetic sludge and activated sludge plants treating wastewater having different biological oxygen demand (BOD) levels and containing acetate as a main BOD source. There was a positive correlation between the population density of PNSB and the strength of BOD in the aeration tanks. All of the PNSB strains isolated were identified as species of the genera Rhodobacter and Rhodopseudomonas by 16S rRNA gene sequencing. The Rhodobacter species were abundant in high BOD-loaded wastewater environments, while the Rhodopseudomonas species increased at lower BOD levels. Therefore, the PNSB population structure appeared to be greatly affected by the concentration of lower fatty acids as major BOD sources, varying over time and space. The utilization of lower fatty acids with different carbon numbers (C2 to C6) by aerobically or semi-aerobically grown cells of authentic and isolated PNSB strains was evaluated by monitoring substrate-dependent oxygen uptake. The oxidation of the fatty acids by the Rhodobacter strains depended upon the number of carbons in the substrate, while the Rhodopseudomonas strains utilized all substrates equally. A low carbon number was much preferred by the Rhodobacter strains. The affinity for acetate of the Rhodobacter and Rhodopseudomonas strains ranged from 0.14 to 3.0 mM and 0.032 to 0.096 mM, respectively. These results suggest that the concentration and kind of lower fatty acids as major BOD sources are important factors affecting not only the population level but also the species composition of PNSB in wastwater environments.
Three vector complete model predictive control for three-phase grid-connected inverters with LCL filter

In the traditional model predictive control(T-MPC), only inverter side current of LCL inverter is treated as a control target. Grid side current is controlled indirectly by controlling the inverter side current directly. The control performance of the algorithm can be further improved. In this paper, a complete model predictive control algorithm(C-MPC) which take the grid side current as control target directly is proposed. The equivalent mathematical model and the equivalent circuit model are established based on active damping of capacitive current proportional feedback, then system predictive control model is established by the equivalent mathematical model and the circuit model. In order to make the output current track the directive current accurately at the next moment, the output voltage vector needed at this moment is calculated by the predictive control model. Finally, the required voltage vector is reproduced through space vector modulation(SVM). The simulation results and experimental measurements verify the developed control schemes and show high quality of grid side current (low THDi value).
RPN+ fast boosted tree: Combining deep neural network with traditional classifier for pedestrian detection

The problem of pedestrian detection receives increasing attention due to the rapid development of artificial intelligence technologies. In this paper, we propose a deep neural network based method which combines with a traditional classifier for fast and robust pedestrian detection. Specifically, region proposals generation and feature extraction are implemented using a modified RPN-VGG method. The proposed method is designed to improve system performance on small objects detection. A new classifier, Fast Boosted Tree, is trained based on RPN outputs to obtain the final results. Experiments on Caltech pedestrian dataset demonstrate that the proposed method achieves 8.77% miss rate and has the best known efficiency with state-of-the-art CNN-based detectors. When algorithm efficiency is not considered, detection quality can be further improved to 8.25% miss rate by adding global normalization and optical flow features.
Designing an accessible and engaging email application for aging in place

Supporting independent everyday functioning of older adults is a major challenge for aging in place. In particular, communication and social activities need support to prevent social isolation, cognitive and psychosocial well-being decline, and a risk of depression. This paper focuses on how technology can bring social support to isolated older-old adults (over 75 years old) and allow them to communicate with members of their social network. We present the design of an accessible and engaging email application dedicated to this population. We propose design principles based on the older adults' specificities and then use these principles to develop a tablet-based email application. We conducted a field study to evaluate our email application during 9 months. We equipped 13 community-dwelling old-older adults with a touchscreen tablet and our application at their home (compared to 13 control counterparts). This field study validates our design principles as shown by the effectiveness and efficiency gained by the participants in using our application. Moreover, we reveal the influence of health indicators in the usage behaviors and the long-term use of our application.
Automated video surveillance system based on hierarchical object identification

The structure, data models and implementation of the automated video surveillance system are proposed in this paper. The developed methods and basic information technologies for contour segmentation based on the Canny's method, and the methods of contour inflection points selection with the adjustable detailing allow us to carry out structural and statistical identification of hierarchical objects by their contour inflection points. Using the technologies proposed by the authors, the automated video surveillance system can identify objects with different levels of detailing. This allows us to increase its efficiency under the conditions of limited computing resources.
Neutralising the Authorial Signal in Delta by Penalization: Stylometric Clustering of Genre in Spanish Novels

Since John Burrows proposed it in 2002, Delta has been one of the most used and researched methods in stylometry and authorship attribution. Burrows explained it as “expression of difference, pure difference” (2002: 269) and is based on basic statistical concepts like most frequent words, z-scores and the Manhattan distance between each pair of texts.2 Burrows closes his paper with an unanswered question about why Delta works so well. Other researchers such as Hoover (2004b: 454), Argamon (2008), Plasek (2014) or Evert et al (2015: 79) have confirmed that we are still far from being able to answer this question. This lack of understanding has not stopped the stylometric community of trying to improve Delta (Hoover 2004a; Argamon 2008; Eder 2013). Smith and Aldridge (2011) have proposed Cosine Delta which gives the best results in different languages (Jannidis et al. 2015). Since Delta is sensitive to aspects or signals like genre or period (Burrows 2002), the corpora for authorship attribution tend to be homogenous in those aspects. Research has been conducted to try to separate signals (Schöch 2013 and 2014) or selecting the words that contribute to them using Recursive Feature Elimination (Büttner and Proisl 2016). Jannidis and Lauer (2014) and Hoover (2014) show how Delta can be used to distinguish genre and periods within the works of a single author. Other researchers have used other methods such as classification (Hettinger et al., 2016; Underwood 2014) or logistic regression (Jockers 2013; Riddell and Schöch 2014) to similar ends.
Digital media marketing using trend analysis on social media

Social Media has quickly gained prominence as it provides people with the opportunity to communicate and share posts and topics. Tremendous value lies in automated analysing and reasoning about such data in order to derive meaningful insights, which carries potential opportunities for businesses, users, and consumers. Many events in the world are accompanied by the Hash-Tag trends on social media. The whole idea behind this is to create such an application that would help in marketing of products and services over social media platforms. The technique is known as Social Media Marketing and is a sub-set of Digital Media Marketing. As of now there is no personalize engagement between marketers and clients. We aim to provide such data by Personal Engagement by providing a deep insight into the user's' content and thus would generate quality data resulting in better customer base, high conversion and lower bounce rates.
Deep convolutional neural networks for SOFC core-shell recognition

The quantitative analysis of the microstructure and related electrical properties of anode substrate are necessary to optimize the fabrication conditions for the best performance of Solid Oxide Fuel Cell (SOFC). In SOFC Optical Microscopic (OM) images, core-shell microstructure can observed. The core-shell will affect the electrochemical performance of the SOFC, and reduce its service life to same degree. Highly accurate recognition of core-shell images of SOFC microstructure is of great significance. In this paper, a Convolutional Neural Network (CNN) based on SOFC OM images using supervised training is proposed. In our CNN model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image recognition methods. With the utilization of a simple data augmentation method and fast convergence speed, our method can achieve the high accuracy rate and outstanding recognition capability for core-shell images.
RFID based activities of daily living recognition

In this paper, we address the issue of indoor activities of daily living recognition with a novel Activity Recognition System (ARS). This system only uses interactions between objects. Their locations is provided by a tracking system based on passive RFID tags to compute activity probabilities. Classification within the tracking system is done through a random forest that gives over 97% in accuracy. Activities are represented by the use of a custom behaviour tree, which makes it possible to compose interactions to form activities of any complexity. Interactions and activities are defined in a human readable form to allow everyone to expand them with minimal prior knowledge.
Mobile manipulation for planetary exploration

Robotic systems map unknown terrain and collect scientific relevant data of foreign planets. Currently, pilots from Earth steer these rovers on Moon and Mars surfaces via teleoperation. However, remote control suffers from a high delay of the long distance communication which leads to a reduction of the time the rover can spent gathering scientific data. We propose a system architecture for an autonomous rover for planetary exploration. The architecture is centered around a flexible, scalable world model to record and represent the environment of the robot. An autonomous task control framework and a versatile constraint motion planner use the live information from the world model to steer the rover through complex manipulation tasks. Furthermore, we present the enhancement of our Light Weight Rover Unit (LRU) with an innovative docking interface for arbitrary tool handling. We showcase the effectiveness of our approach at the moon-analogue demonstration mission of the ROBEX project on Mt. Etna, Sicily. We show in two experiments that the robot is capable of autonomously deploying scientific instruments and collecting soil samples from the volcano's surface.
A Novel Design of PI Current Controller for PMSG-Based Wind Turbine Considering Transient Performance Specifications and Control Saturation

This paper presents a novel design process of decoupled PI current controller for permanent magnet synchronous generator (PMSG)-based wind turbines feeding a grid-tied inverter through a back-to-back converter. Specifically, the design methodology consists of combining disturbance observer-based control (DOBC) with feedback linearization (FBL) technique to ensure nominal transient performance recovery under model uncertainty. By simplifying the DOBC under the feedback linearizing control, it is shown that the composite controller reduces to a decoupled PI current controller plus an additional term that has the main role of recovering the nominal transient performance of the FBL, especially under step changes in the reference. Additionally, an antiwindup compensator arises naturally into the controller when considering the control input saturation to design the DOBC. This permits removal of the effect of the saturation blocks required to limit the control input. The proposed control scheme is implemented and validated through experimentation conducted on 22-pole, 5 kW PMSG. The results revealed that the proposed technique can successfully achieve nominal performance recovery under model uncertainty as well as improved transient performances under control saturation.
Automatic Detection of Demosaicing Image Artifacts and Its Use in Tampering Detection

Even novice people can nowadays do convincing forged images. However, most forgeries alter the underlying statistics of the image, and in particular the slight artifacts caused by the demosaicing method. Demosaicing transforms the undersampled image acquired by the CFA into a three channel color image. Two problems arise: The first one is to identify the underlying CFA configuration, which classifies pixels according to whether they acquired a red, green or blue value. The second one is to detect anomalies in the regularity of the “demosaicing artifacts” that may reveal tampered image regions. We review the state of the art of detection methods, but point out that none of the proposed methods yields guaranteed detections for the CFA configuration or for the tampered areas. The methods generally yield an output that must still be evaluated visually. We therefore introduce an a contrario method that yields guaranteed detections, namely detections with a very low number of false alarms (NFA). Obtaining such an NFA is a useful complement to existing detection methods and should enable these methods to be included into automatic image evaluation processes.
Graph-based APT detection

In this paper we propose a new algorithm to detect Advanced Persistent Threats (APT's) that relies on a graph model of HTTP traffic. We also implement a complete detection system with a web interface that allows to interactively analyze the data. We perform a complete parameter study and experimental evaluation using data collected on a real network. The results show that the performance of our system is comparable to currently available antiviruses, although antiviruses use signatures to detect known malwares while our algorithm solely uses behavior analysis to detect new undocumented attacks.
Wireless Visual Sensor Network Platform for Indoor Localization and Tracking of a Patient for Rehabilitation Task

Wireless visual sensor networks (WVSNs) are commonly using information technologies of modern networking and computing platforms. Today, visual network computing applications are faced with high demand of powerful network functionalities and performances. This paper proposes the specific WVSN nodes that are able to sense surrounding signals coming from the patient in the rehabilitation room and perform as local computations wirelessly communicated within the considered WVSNs. Assuming the WVSN platform for the rehabilitation supervision of patients, this paper discusses the specifications of the concept and the development of the WVSN nodes relying on KINECT and Raspberry Pi 3 (RPi 3) boards. Several technologies, such as RPi 3, Kinect, and ID sensor, are utilized in the proposed platform to realize sensors, image-capturing units, and processing cores. This collaborative platform consists of three nodes that are designed in three processing cores using the entire workflow to support the development steps. The communication of the elements between one to another and between the components takes place via Wi-Fi. The implementation of a preprocessing stage for component integration and the development of software development kit tools are necessary. In its final part, this paper summarizes the outlines of the proposed platform in different scenarios which run on nodes and also provides evaluation results that prove the feasibility of the approach.