Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying hand gesture recognition and joint tracking to a TV controller using CNN and Convolutional Pose Machine

This paper introduces a novel TV control simulation system that recognizes hand gestures and track hand joints based on Convolutional Neural Networks (CNN) and Convolutional Pose Machines (CPM). The system provides users with an intuitive means of controlling television functions through hand gestures. Moreover, based on relative position and angle of fingers, users can manipulate an onscreen cursor and continuously modify volume & channels at varying speed. We achieved 95.8 percent testing accuracy in 19 gestures with 4 subjects, and average 11 & 45 fps while conducting CPM and CNN respectively.
Color Recognition Method based on Circle Extraction and Similarity

To drive and control autonomous guided vehicle by the autonomous system, the landmark can be attached and reported the driving information. This landmark can notify the information by its color. Vision-AGV captured the landmark by the image and then they analyze the meaning of the landmark. To implement this concept, the system should be designed for detecting and recognizing the landmark. Therefore, this paper proposes the method to detect and recognize the color of the landmark. To detect the landmark, circular Hough transform is applied. This algorithm can detect the shape of the circle. However, it detects the circle which is not the landmark. To sort out the landmark, peak signal-to-noise ratio is calculated. This value represents the similarity between two images. So, we compare the circle and the reference for determining whether it is a landmark or not and recognizing the color of the landmark. The circle is extracted by the circular Hough transform, and the reference represents each color such as green, red, yellow. To compare two things by peak signal-to-noise ratio, we determine the color of landmark. As shown in experimental results, the proposed method can detect and recognize the color of landmark in case of the traffic light.
Which Content in a Booklet is he/she Reading? Reading Content Estimation using an Indoor Surveillance Camera

In this paper, we propose a method for estimating reading content in a booklet using an image captured by an indoor surveillance camera. Here, we assume that a reading content can be specified by estimating followings; what booklet, which page of the booklet, and which region in the page. We propose a reading booklet/page estimation method based on image search, and a reading region estimation method focusing on the body pose of the reader. We evaluated the method as a 44 classes classification problem, which consists of eleven pages of booklets and four regions in each pages. We achieved 25.6% in accuracy of the reading content estimation.
LAS: Language Agnostic System for Question Answering

In this article we present a deep learning question answering (QA) setting that can work for any natural language. We recognize the problem of low-resource languages, i.e. most languages other than English, which lack appropriately sized datasets or cutting-edge NLP tools. To address this problem, we have designed and implemented a QA dataset and system that are independent from language use; specifically, we test our solution on the Polish language which is both low-resource and grammatically complex. Both these features make the task of QA significantly harder. To the best of our knowledge, this is the first attempt to train a deep learning QA system in a language-agnostic setting.
Demystifying Myths of MEC: Rethinking and Exploring Benefits of Multi-Access/Mobile Edge Computing

The benefits of Mobile/Multi-access Edge Computing (MEC) are often discussed for reducing the latency and offloading intense computation for data processing of emerging applications on the user equipment (UE), such as Aug-mented/Virtual Reality (AR/VR), connected car, content delivery, etc., onto edge cloud. On the other hand, the performance of UE is rapidly improving with abundant resources for enabling local data processing. In this paper, we rethink the benefits of MEC by first arguing these benefits are myths, that is, the advancement of UE may defeat the often-assumed benefits of MEC, i.e., offloading computation and reducing latency, and then demystify the myths by carefully summarizing the latency and computational requirements of the existing applications possibly benefiting from MEC by surveying several tens of publications while examining the recent UEs performance improvement. We also attempt to undercover new benefits of MEC. In the light of these observations, we conclude that MEC is not necessarily just required for low-latency and computationally intensive applications, but also brings benefits from the four additional perspectives: (1) Data Scalability, (2) Application Scalability, (3) Intent-driven Networking, (4) Partial offloading of the network functions.
Simulation of UWB Radar-Based Positioning Performance for a UAV in an Urban Area

In an urban canyon environment, the positioning accuracy of global navigation satellite systems (GNSSs) decreases. In this paper, we consider an ultra-wideband (UWB) radar-based positioning method for unmanned aerial vehicles (UAVs) as a complement to GNSS in an urban canyon environment. The positioning accuracy of the UWB radar-based system is simulated based on the error statistics from actual range measurements. For a realistic simulation, we created a 3D map of an actual urban canyon environment in Seoul, deployed UWB radars on the map, and evaluated the positioning accuracy of a UAV at a single location.
A Dual Layered Loop Array Antenna for Base Stations With Enhanced Cross-Polarization Discrimination

This paper presents a novel dual-loop array antenna targeted at current and future base station applications. The antenna has four rectangular loops and four trapezoidal loops printed on the front and back sides, respectively, of a substrate placed above a flat square reflector. All eight loop radiators are excited simultaneously with properly designed feed networks to achieve its ±45° polarization states. The trapezoidal loops act like folded (electric) dipoles; the rectangular loops act primarily as magnetic dipoles. The combination of these two loop arrays leads to a type of magnetoelectric loop antenna that has stable directivity patterns with high cross-polarization discrimination (XPD) values across a 45.5% operational fractional bandwidth from 1.7 to 2.7 GHz. A fabricated and measured prototype confirms the simulation results and demonstrates that the half-power beamwidths in the horizontal plane vary between 63° and 70°, the XPD values are >20 dB in the boresight direction, and are >10 dB within the entire cellular coverage angular range: $-60^\circ \leq \theta \leq 60^\circ $ .
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning

Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance. We propose a unifying probabilistic paradigm, where deep neural network architectures are used to infer output posterior distributions. These probabilities are in turn processed to yield discrete point estimates. Designing such architectures often involves two important and complementary aspects that we investigate and discuss. First, we study how recent advances in deep architectures may be exploited for the task of speaker count estimation. In particular, we show that convolutional recurrent neural networks outperform recurrent networks used in a previous study when adequate input features are used. Even for short segments of speech mixtures, we can estimate up to five speakers, with a significantly lower error than other methods. Second, through comprehensive evaluation, we compare the best-performing method to several baselines, as well as the influence of gain variations, different data sets, and reverberation. The output of our proposed method is compared to human performance. Finally, we give insights into the strategy used by our proposed method.
Application of Porous a-Si:H Passivation Layer Deposited Using Si2H6 Precursor in Silicon Heterojunction Solar Cells

The improved silicon heterojunction solar cell efficiency by employing porous a-Si:H passivation layer deposited using Si<inf>2</inf>H<inf>6</inf> precursor is presented. By introducing Si<inf>2</inf>H<inf>6</inf> precursor into the commonly-used SiH<inf>4</inf> plasma, it is demonstrated that the porosity of a-Si:H passivation layer can be increased significantly. A stack of the porous a-Si:H passivation layer with overlying dense a-Si:H capping layer preserves excellent interface passivation even after doped layer deposition. In the finished solar cell, improvements in V<inf>OC</inf> from 0.691 to 0.714 V and efficiency from 20.0 to 21.0% were obtained as a consequence of using porous a-Si:H passivation layer deposited under high Si<inf>2</inf>H<inf>6</inf> concentration.
Event Trigger Fuzzy Adaptive Compensation Control of Uncertain Stochastic Nonlinear Systems With Actuator Failures

In this paper, event trigger fuzzy adaptive compensation control for the uncertain stochastic nonlinear system with actuator failures is considered. Although great strides have been made in the accommodating nonlinear system recently, it remains a challenge to establish an event-triggered-based controller for the uncertain stochastic nonlinear system with actuator failures, especially to ensure asymptotic tracking performance. In this paper, we propose a new relative event trigger adaptive fuzzy controller established by utilizing the techniques of robust approach to address the actuator failures and stochastic interference. It is proven that the proposed scheme guarantees the bounding of all closed-loop signals and the asymptotic convergence performance of tracking error. Simulation results demonstrate that our proposed control scheme for compensating the stochastic nonlinear system guarantees effectiveness.
Unsupervised Learning of Fingerprint Rotations

The alignment of fingerprint samples is a preprocessing step in fingerprint recognition. It allows an improved biometric feature extraction and a more accurate biometric comparison. We propose to use Convolutional Neural Networks for estimation of the rotational part. The main contribution is an unsupervised training strategy similar to Siamese Networks for estimation of rotations. The approach does not need any labelled data for training. It is trained to estimate orientation differences for pairs of samples. Our approach achieves an alignment accuracy with a mean absolute deviation 2.1° on data similar to the training data, which supports the alignment task. For other datasets accuracies down to 6.2° mean absolute deviation are achieved.
Equivalent Circuit Modelling of Ferrite Inductors Losses

The modelling of the magnetic losses in the core of ferrite inductors for power conversion circuits has been considered in this paper. On the basis of the Revised Generalized Steinmetz Model (RGSE) an equivalent circuital modelling has been obtained. The losses are modelled as an equivalent resistance connected in parallel to the inductor. The model has been tested under periodic triangular current waveforms.
Energy Efficient Real Time Scheduling on Multi-core Processor with Voltage Islands

Majority of the modern embedded systems are making use of high performance cluster based multi-core processors. The cores belonging to each cluster is equipped with a separate clock and supply voltage. These clusters are named as Voltage and Frequency Islands. The cores belonging to a cluster operate at the common clock frequency and supply voltage. This paper addresses the issue of increasing energy consumption of voltage island based multi-core processors. We propose an energy efficient real time scheduling algorithm for optimizing static and dynamic energy consumption of such processors. The proposed algorithm optimizes the overall energy consumed by each core in the system, takes care of hard deadline of periodic tasks and achieves acceptable response time of aperiodic tasks. It also improves the overall usage of each processor core that remains unused after static partitioning of tasks. The simulation results show that the proposed algorithm saves significant energy and increases processor utilization in Voltage Island based multi-core platform.
A Circularly-polarized Patch Antenna using Pin-loaded Technique with PSO

A circularly polarized (CP) microstrip patch antenna in the X-Band frequency is designed using Particle Swarm Optimization (PSO). Circular polarization is achieved in the proposed design by using the pin-loaded technique. First, two shorting pins are introduced in the single-fed patch to reduce S11. Then, the position of the inductive pins in the initial design, obtained by numerical approaches, is optimized using the PSO algorithm to obtain an axial ratio <3-dB in the required frequency range. The converged result from the PSO algorithm satisfied the objective and design specifications of reflection coefficient of S11 <-10 dB, axial ratio <3 dB, an omnidirectional radiation pattern and satisfactory gain.
Multi-Modal Biometric Recognition Using Human Iris and Dynamic Pressure Variation of Handwritten Signatures

Physiological traits containing information extracted from human organs such as fingers, ears, eyes, palm prints, and knuckles can be used for biometric recognition. In addition, behavioural biometric traits such as speech, gait, handwritten signature, and keyboard dynamics while typing a text can provide reliable biometric features. In the current research, texture information extracted from iris data, and dynamic pressure variation data extracted from online signatures was combined to form a reliable biometric system. In this view, this paper is proposing a multi-modal biometric recognition architecture, which utilises a new feature vector extraction mechanism based on the Webber Local Descriptor and the k-NN machine learning classifier. The proposed architecture was evaluated using multimodal data obtained from 64 users. The TAR-TRR results when using iris recognition alone reached 88.39%. When using dynamic pressure variation data extracted from online signatures TAR-TRR reached 75.89%. When combining the data of the two modalities the recognition TAR-TRR increased to 90.18%. The proposed architecture revealed that biometric systems which perform recognition using two different data channels are more reliable that when using single channel data.
Laughter Synthesis using Mass-spring Model and Excitation Source Characteristics

With growing interest, an amalgamation of the field of Human-Computer Interaction and Speech Synthesis is redefining the way humans interact with technology by exploring several facets of interaction and thus incorporating it in synthesized speech. One such facet is laughter – an important form of interaction that expresses emotion like humor, joy, amusement as well as sarcasm. The most significant excitation of a laughter signal occurs at the instant of glottal closure, and determination of these instants, known as epochs, along with instantaneous fundamental frequency is used for deriving the excitation source information – a method known as Zero Frequency Filtering. In the existing literature, modification of the characteristics of the excitation source information along with the LP residuals and prosody that involves changing the pitch and duration of the signal, form the rudimentary step for synthesizing laughter calls. In order to emulate the naturalness, these modifications are carried out by first examining the features of natural laughter. This paper proposes a method to generate laughter signal by concatenation of laughter calls using mass-spring model, as laughter calls make up a laughter signal. Using the mass-spring model, a fair CMOS is achieved making the synthesized laughter sound more natural. The synthesized laughter can be used to improve the perceivability of a synthesized speech in Human-Computer Interaction
A New Super-Resolution Technique for Direction (and Time) of Arrival Detection

A new technique for resolving different directions (or times) of arrival beyond the Rayleigh diffraction limit is presented. It is based on the Euclidean Algorithm for polynomial factorization and is implemented as a simple polynomial division (deconvolution) followed by a polynomial-root search. Similar to other super-resolution techniques, it reflects the exchangeability between bandwidth and signal-to-noise ratio governed by Shannon information-capacity concept. The core idea of the technique is borrowed from the error correction analysis of the Generalized Reed-Solomon codes used in channel coding. Both noiseless and noisy synthetic measurements of the output signals of a uniform linear antenna array are used for verifying the technique.
Three-Dimensional Manipulation of a Spherical Object Using Ultrasound Plane Waves

In this letter, we demonstrate a new robotic framework where a lightweight balloon with no onboard actuators or batteries acts as a mid-air robot that travels three-dimensionally within a workspace. The workspace contains five sparsely arranged ultrasound transducer arrays emitting steerable plane waves that cause acoustic radiation forces on the balloon and actuate it. The fabricated system performs a dynamic PID control of the movement of the balloon based on visual feedback. For practical operation of the system, we experimentally modeled the acoustic radiation force applied on a sphere. Based on the model, we developed the system dynamics model and a control law. We constructed a prototype system and demonstrated meter-scale three-dimensional manipulation of a 200 mm-diameter spherical object. Our proposed methodology is beneficial for long-term mid-air robotic applications, such as 24-h indoor surveillance system with no blind spots caused by battery limitation.
MEMS Flow Sensor Using Suspended Graphene Diaphragm With Microhole Arrays

This letter reports a miniature flow sensor using a suspended composite membrane of graphene and silicon nitride containing an array of microscale through-holes. As a fluid flow passes through these microholes, the graphene layer of the composite membrane is stressed to change its electrical resistance due to the piezoresistive effect of graphene. The creation of the microholes in graphene allows for a wide dynamic range of the sensor from 5 to 56 m/s of air flow velocity. The sensor also exhibits an air flow rate sensitivity of <inline-formula> <tex-math notation="LaTeX">$\sim 7\times 10^{ {-8}}$ </tex-math></inline-formula> (m/s)<sup>−1</sup>. [2018-0133]
Local Subclass Constraint for Facial Expression Recognition in the Wild

The Automated Facial Expression Recognition (FER) in the wild is still a challenge problem. Currently, most of Deep Convolutional Neural Networks(DCNNs) based FER methods adopt softmax cross-entropy loss to encourage the separability of inter-class features. Many deep embedding approaches (e.g. contrastive loss, triplet loss, center loss) have been extended to the field of FER to enhance the discriminative ability of deep expression features and obtain the predictive effect. In this work, we present a novel deep embedding approach explicitly designed to respect the huge intra-class variation of expression features while learning discriminative expression features. We aim at forming a locally compact representation space structure through minimizing the distance between samples and their nearest subclass center. We demonstrate the effectiveness of this idea on RAF(Real-world Affective Faces) database. The experiment results show that our approaches can not only improve the classification performance but also adaptively learn a locally compact and expression intensity-aware feature space structure. We further extend our models to Static Facial Expressions in the Wild (SFEW) dataset and the results show the generalized ability of our approaches.
RotateConv: Making Asymmetric Convolutional Kernels Rotatable

In deep Convolutional Neural Networks(CNN), the design of kernel shapes influences a lot on the model size and performance. In this work, our proposed method, RotateConv, applies a novel kernel shape to massively reduce the number of parameters while maintaining considerable performance. The new shape is extremely simple as a line segment one, and we equip it with the rotatable ability which aims to learn diverse features with respect to different angles. The kernel weights and angles are learned simultaneously during end-to-end training via the standard back-propagation algorithm. There are two variants of RotateConv that only have 2 and 4 parameters respectively depending on whether using weight sharing, which are much compressed than the normal $3\times 3$ kernel with 9 parameters. In experiments, we validate our RotateConv with two classical models, ResNet and DenseNet, on four image classification benchmark datasets, namely MNIST, CIFAR10, CIFAR100 and SVHN.
Analysis of Thrust Performance of a Dual-Mover Linear Vernier Machine With Horizontal-Magnetized PM Arrays

Recently, a novel dual-mover linear vernier machine with horizontal-magnetized permanent magnet arrays has already been proposed to improve the thrust and power factor (PF). According to published papers, the thrust and the PF of the novel LVM can be increased by more than 30% and can be improved up to 0.7, respectively, compared to a conventional LVM. However, by now, the analysis of this kind of machine is merely based on the finite element method (FEM). Consequently, this paper will focus on the analytical analysis of thrust performance. Average force, force ripple, slot cogging force, as well as end cogging force are taken into consideration. The effectiveness of the analytical calculations is validated by FEM. By using the derived expressions, it is possible to predict the thrust performance and optimize the machines efficiently.
Discovering $k$-Trusses in Large-Scale Networks

A k-truss is a subgraph where every edge belongs to at least k-2 triangles in the subgraph. The truss decomposition assigns each edge the maximum k for which the edge belongs to a k-truss, and the trussness of a graph is the maximum among its edges. Discovery algorithms for k-trusses and truss decomposition provide useful insight for graph analytics (such as community detection). Even though they take polynomial time, on massive networks they suffer from handling a potentially cubic number of wedges: algorithms either need a long time to recompute triangles several times, have high memory usage, or rely on the large number of cores on graphic units. In this paper we describe EXTRUSS, a highly optimized algorithm for truss decomposition which outperforms existing algorithms. We then introduce a faster algorithm, HYBTRUSS, which finds the trussness of a graph using less time and space than EXTRUSS. Our algorithms take the best of existing approaches having good performance, low memory usage, and no need for sophisticated hardware systems. We compare our algorithms with the state-of-the-art on a set of real-world and synthetic networks. EXTRUSS processes graphs with over a billion edges, which seems difficult for the competitors, and our HYBTRUSS is the first algorithm able to find the trussness of a graph with over 25 billion edges.