Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Representation-Based Face Object Generative via Deep Adversarial Network

How to generate well quality faces objects of automated processes has always been the focus on researchers. Recently, due to the deep generative networks have achieved impressive successes in data generative fields, researchers have tried to introduce deep learning into the 3d objects generate field, such as text2scene, slice-based object generate. However, the generative ability in 3D object is limited by the size of the feature space, because of computational space limitations on hardware. In this paper, we address the problem by reducing amount of calculated on process of learning, and thus generative newly different objects. The problem is intractable, since first the limiting of compute space is so hard that object can't be process in deep network due to the process need to compute many matrix multiplications. To resolve the problem, we propose a sparse representation-based method of generating well-quality faces object. Our method consists of two parts: sparse reconstruction and object generative. First, we verified the possibility of using sparse representations of 3D data by reconstructing 3D object. Second, we design a network architecture of deep adversarial network of generating new sparse representation and combined with the previous reconstruction method of generating new face object. Experiments show that our method has the ability to generate very different and well quality faces objects that contain tens of thousands of points and meshes. Our findings show that sparse representation can be used in 3D object reconstruction and generate via deep generative adversarial model.
Smart Wristwatches Employing Finger-Conducted Voice Transmission System

The principal aim of this paper is to present a novel speech transmission system that conveys speech between an actuator in a wearable wristwatch and the ear bone of a user through a finger. If an individual wears a smart watch equipped with an actuator that can play speech sent via communication lines, speech vibrations propagate from the actuator to fingertips through the human tissue and bone. When an individual places his or her finger into their ear, speech conducted through the finger can be registered and heard. While listening to finger-conducted speech, sounds are muffled, significantly degrading the intelligibility of speech. To mitigate this problem, a formant enhancement filter is applied to the speech prior to being fed into the actuator. With this method, the impulse response of human tissue and bones between the fingertips and wrist on which the watch is worn is first estimated to account for speaker-dependent distortion. Based on the estimated impulse response, a gain filter is used to boost the sound spectra, especially within the formant regions, to compensate for frequency distortion prior to speech transmission. On the other hand, since the impulse responses of humans are quite different for each individual, we propose the novel idea of a personalized algorithm that guides users to select an appropriate gain filter, using the $k$-medoids clustering algorithm. Also, when an individual uses the proposed system, speech quality is degraded due to the ambient noise and acoustic echo between the microphone and actuator in the watch. Thus, to reduce background noise and acoustic echo, an integrated acoustic echo and background noise suppression algorithm is employed. Extensive simulations of the proposed system were performed by creating a novel phantom, which mimics the human hand with an aid of an ear simulator. We demonstrate that the proposed system has improved speech quality, when transmitting speech from the wearable wristwatch to a human perceptual organ through the finger.
Symmetric-Strong-Tracking-Extended-Kalman-Filter-Based Sensorless Control of Induction Motor Drives for Modeling Error Reduction

This paper proposes a real-time speed identification method by using a symmetric strong tracking extended Kalman filter (SSTEKF) for induction motor sensorless drive. In SSTEKF, the residual sequences are forced orthogonal to each other, and the gain matrix is tuned in real-time by introducing fading factors into the covariance matrix of the predicted state. The modeling error is reduced, and the mutational state is tracked rapidly based on SSTEKF. Simultaneously, the Cholesky triangular decomposition is used to change the working way of the multiple fading factor matrix in the error covariance matrix. The application of the Cholesky triangular decomposition guarantees that the error covariance matrix is symmetric in the process of iteration, and the stability of the algorithm is enhanced. Therefore, the estimation accuracy, the tracking speed, and the noise suppression of the proposed method are better than the EKF. The correctness and effectiveness of the proposed method are verified by experimental results.
Performance Analysis of a Single-phase GaN-based 3L-ANPC Inverter for Photovoltaic Applications

Nowadays, the power electronics converter design is challenged with a request of high efficiency and compactness for various applications. To tackle this, the research community and the industry have almost fully exploited the silicon technology, leading to the development of new power transistors. The Gallium-Nitride (GaN) HEMTs can be promising power devices to replace the traditional power devices. Therefore, the performances of GaN-based converters should be assessed to validate the effectiveness in terms of efficiency and power density. Moreover, among the available converter topologies, the performance of the three-level Neutral Point Clamped (NPC) family can be enhanced with the GaN HEMTs. In light of the above, in this paper, the performance of a GaN-based three-level Active NPC (3L-ANPC) converter is evaluated in terms of power losses, volume impact of passive components, and output distortions. Simulations and experiments have been performed.
Wide Design Range of Constant Output Current Using Double-Sided LC Compensation Circuits for Inductive-Power-Transfer Applications

Inductive-power-transfer (IPT) converters should desirably achieve nearly zero reactive circulating power, soft switching of power devices and load-independent constant output voltage or current with optimized transfer efficiency, and lowest component ratings. However, the load-independent output characteristic is dependent on IPT transformer parameters and their compensation. The space-constrained IPT transformer restricts the design of the low-order resonant circuit compensated IPT converter, making the IPT converter hard to optimize. This paper will analyze conditions under which any extra design freedom can be allowed for a double-sided LC compensation circuit in order to achieve load-independent output and zero reactive power input. A detailed analysis is given for the double-sided LC compensation achieving zero reactive power input and constant current output, without being constrained by the transformer parameters. Design conditions of the compensation circuit parameters for achieving these two properties are derived. A complementary LC-CC compensated IPT converter is further proposed to extend the output current amplitude limitation of the double-sided LC compensated IPT converter. Finally, the prototypes of the IPT converters are constructed to verify the design flexibility of the proposed double-sided LC compensation circuit for achieving the multiple objectives.
From Taxonomy to Requirements: A Task Space Partitioning Approach

We present a taxonomy-driven approach to requirements specification in a large-scale project setting, drawing on our work to develop visualization dashboards for improving the quality of healthcare. Our aim is to overcome some of the limitations of the qualitative methods that are typically used for requirements analysis. When applied alone, methods like interviews fall short in identifying the full set of functionalities that a visualization system should support. We present a five-stage pipeline to structure user task elicitation and analysis around well-established taxonomic dimensions, and make the following contributions: (i) criteria for selecting dimensions from the large body of task taxonomies in the literature,, (ii) use of three particular dimensions (granularity, type cardinality and target) to create materials for a requirements analysis workshop with domain experts, (iii) a method for characterizing the task space that was produced by the experts in the workshop, (iv) a decision tree that partitions that space and maps it to visualization design alternatives, and (v) validating our approach by testing the decision tree against new tasks that collected through interviews with further domain experts.
Second-order Guarantees of Gradient Algorithms over Networks

We consider distributed smooth nonconvex unconstrained optimization over networks, modeled as a connected graph. We examine the behavior of distributed gradient-based algorithms near strict saddle points. Specifically, we establish that (i) the renowned Distributed Gradient Descent (DGD) algorithm likely converges to a neighborhood of a Second-order Stationary (SoS) solution; and (ii) the more recent class of distributed algorithms, based on gradient tracking (termed SONATA), likely converges to exact SoS solutions, thus avoiding (strict) saddle points.
Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

Dynamic control of soft robotic manipulators is an open problem yet to be well explored and analyzed. Most of the current applications of soft robotic manipulators utilize static or quasi-dynamic controllers based on kinematic models or linearity in the joint space. However, such approaches are not truly exploiting the rich dynamics of a soft-bodied system. In this paper, we present a model-based policy learning algorithm for closed-loop predictive control of a soft robotic manipulator. The forward dynamic model is represented using a recurrent neural network. The closed-loop policy is derived using trajectory optimization and supervised learning. The approach is verified first on a simulated piecewise constant strain model of a cable driven under-actuated soft manipulator. Furthermore, we experimentally demonstrate on a soft pneumatically actuated manipulator how closed-loop control policies can be derived that can accommodate variable frequency control and unmodeled external loads.
PWM Frequency Noise Cancellation in Two-Segment Three-Phase Motor Using Parallel Interleaved Inverters

This paper proposed a novel method for two-segment three-phase motor to eliminate the harmonics nearby pulsewidth modulation (PWM) frequency. Due to PWM technique and switching losses considerations, ear-piercing high-frequency electromagnetic noise in drive system is common. By using magnetically coupled inductors (MCIs) and interleaved technique, the proposed novel method is able to remove the high-frequency harmonics located nearby the PWM frequency and other odd multiples in two-segment three-phase motor. With this method, the unpleasant acoustic noise could be removed when the PWM frequency is selected near 10 kHz. The size of MCIs mainly depends on the PWM frequency harmonic currents rather than the fundamental frequency currents. Thus, its size is very small compared with LC filter. Moreover, this method remains the control dynamics of two-segment three-phase motor without additional inductance for fundamental-frequency currents. The shift phase of two inverters PWM carriers is π, which is simple to implement. Finally, the effectiveness of the proposed method is verified by experimental results.
Gain Enhancement Technique for Dipole Combining with Cavity and Aperture Radiations

According to the limitation of column of the practical project, a technique of enhancing the gain and the band for the printed antenna is proposed in this paper. Here, both the cavity and the aperture radiation techniques are used to enhance the gain, and the parasitic strips above the dipole to broaden the bandwidth. This proposed antenna includes four parts, the dipole is printed on the substrate, a cavity is placed below the dipole, and another cavity with longer diameter and higher height is put outside the previous one, then the parasitic strip on the substrate is located above the dipole. The simulation results show that the resonance is affected few, but both the bandwidth and the gain are enhanced obviously. That is, the improved bandwidth is 31.8% compared with 21.7% of the conventional one, and by means of aperture radiation of the outer cavity, the gain is improved at least 1dB in the whole band, the biggest is 2.5dB comparing with the maximum of the antenna with one cavity and up to 13.6dB.
Flow Classification for Software-Defined Data Centers Using Stream Mining

Traffic management is known to be important to effectively utilize the high bandwidth provided by datacenters. Recent works have focused on identifying elephant flows and rerouting them to improve network utilization. These approaches however require either a significant monitoring overhead or hardware/end-host modifications. In this paper, we propose FlowSeer, a fast, low-overhead elephant flow detection and scheduling system using data stream mining. Our key idea is that the features from flows’ first few packets allow us to train the streaming classification models that can accurately and quickly predict the rate and duration of any initiated flow. With these predicted information, FlowSeercan adapt routing polices of elephant flows to their demands and dynamic network conditions. Another nice property of FlowSeeris its capability of enabling the controller and switches to perform cooperative prediction. Most of decisions can be made by switches locally, thereby reducing both detection latency and signaling overhead. FlowSeerrequires less than 100 flow table entries at each switch to enable cooperative prediction, and hence can be implemented on off-the-shelf switches. The evaluation via both experiments in realistic virtual networks and trace-driven simulations shows that FlowSeerimproves the throughput by multiple times over Hedera, which pulls flow statistics, and performs comparably to Mahout, which needs end-host modification.
Evaluation of the Motion-Aware Adaptive Dead Reckoning Technique under Different Network Latencies Applied in Multiplayer Games

Multiplayer games hold virtual worlds which connect dozens of players in the same session, in which these human players experience network latency in networked interactions negatively. A number of predictive techniques were developed to deliver the best experience for the players. With the growing numbers of players and the complexity of the worlds, the limitations of such techniques, like network delay, consistency, responsivity and bandwidth cost, become evident. The main technique used nowadays is called Dead Reckoning (DR) and was firstly presented decades ago. Based on it a variety of authors proposed improvements to the prediction method, culminating on Kharitonov’s proposal of the Motion-Aware Adaptive Dead Reckoning (MAADR) technique. The author’s evaluation did not consider latency, an important factor that affects the consistency (i.e. veracity) of the information and the Quality of Experience for the players. The proposal of this paper is to evaluate both the MAADR and DR prediction techniques in 4 situations ranging from simple to complex movement patterns, with 0 to 300ms of network delay. The results show that the MAADR performance is superior when compared to the classic algorithm for each situation and each different latency evaluated. It shows that the classic DR presents a large decay in precision within medium and high latencies and it is not ideal in situations with great intolerances to network delay. Because of the obtained results, it is recommended the usage of the MAADR technique when medium and high latencies are expected or when there is an intolerance for the loss of the Quality of Experience.
Analyze of non-linearity effects of 8/6 switched reluctance machine by finite elements method

This paper presents analysis of the non-linearity effects of 8/6 switched reluctance motor by the use of the finite elements method. In this kind of machine, the high nonrity may be presented in the characteristics of magnetic circuits and the variation of air gap. Hence, in order to analyze these characteristics, the modeling by finite elements method with two situations has been presented, one by considering just the non-linearity of geometric (variation of the air gap) and the second situation by considering the non-linear properties of the materials (non-linearity of magnetic circuits) with the non-linearity of geometric. The static characteristics of the SRM are calculated from two-dimensional finite elements modeling under different rotor positions with variations of the electrical current (excitation). The analysis of the results based on the comparative study for both situations. Obtained results highlight the importance of considering non-linearity in SRM.
The effect of introduction of HfO2 on the electrical characterization of the Pt/HfO2/n-GaN

In this paper is presented a theoretical study of effects of Hafnium dioxide on the electrical properties of (Pd/n-GaN) Schottky diode under a temperature of 300°K, using the forward bias I-V measurements. A simulation on the (Pd/n-GaN) and (Pd/HfO2/n-GaN) structure is presented, then simulation results are compared with experimental results. A well matching was found between simulations and experimental results. Barrier height (Φb), ideality factor (n) and series resistance (Rs) effects was also studied. In addition, (I-V), Norde, Cheung and Cheung, H(I) and G(I), Chattopadhyay and Mikhelashvili methods are used to extract different parameters. Obtained theoritical results are well matched with experimental measurements. The ATLAS module of SILVACO-TCAD software has been used in numeric simulations.
Adaptive Multi-Channel Offset Assignment for Reliable IEEE 802.15.4 TSCH Networks

More and more IoT applications require low-power operations and high reliability (close to 100%). Unfortunately, radio transmissions are unreliable by nature since they are prone to collision and external interference. The IEEE 802.15. 4–2015 TSCH standard has been recently proposed to provide high reliability through radio channel hopping and by appropriately scheduling all the transmissions. Since some of the radio channels still suffer from external interference, blacklisting techniques consist in detecting bad radio channels, and in privileging the good ones to transmit the packets. MABO-TSCH is a centralized scheduling algorithm which allocates several channel offsets to allow each radio link to apply a localized blacklist. However, such strategy is inefficient for large blacklists. In this study, we propose to allocate the channel offsets dynamically at each timeslot according to the number of parallel transmissions, while still avoiding collisions. We evaluate the performance of our solution relying on a real experimental dataset, highlighting the relevance of dynamic and per timeslot channel offset assignment for environments with high external interference, such as a smart building.
Online Distributed MPC-Based Optimal Scheduling for EV Charging Stations in Distribution Systems

The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.
An Accurate and Noise-Resilient Spread-Spectrum Clock Tracking Aid for Digitally-Controlled Clock and Data Recovery Loops

This paper presents a novel spread-spectrum clock (SSC) tracking aid for digitally-controlled clock and data recovery (CDR) loops. The proposed tracking aid accurately recovers the time instants when the slope of the incoming data frequency ramp changes its polarity, so that the CDR can pre-emptively compensate the timing errors that can otherwise be incurred when relying on feedback controls only. Compared with the existing SSC tracking aids, the proposed tracking aid’s integration-based mean-tracking loop, comprising an integrator and subsequent negative feedback loop, achieves the superior noise resilience and SSC timing accuracy. The analysis shows that the error in recovering the SSC deflection timing can be as low as 0.01% of the SSC modulation period in case of tracking a triangular SSC profile and the error does not exceed 1.5% even when the SSC profile noise is increased to the signal-to-noise-plus-distortion ratio of 20 dB. The behavioral simulation of a digitally-controlled phase-locked loop (PLL)-based CDR employing the proposed SSC tracking aid demonstrates a 0.711-UI timing margin with a BER of 10−12, which is a $1.57\times $ improvement in comparison with a PLL-based second-order CDR, when tracking a 6-Gb/s data stream modulated with a 30-kHz, 50000-ppm extended triangular-shaped SSC profile.
Research on Electromagnetic Scattering Characteristics of Kelvin Wake of Ship Based on MPI

An parallel small slope approximation acceleration algorithm based on MPI is improved to calculate the electromagnetic scattering of the ship's wake on the sea surface in this paper. The parallel algorithm calculates the backscattering coefficient at different angles by starting multiple processes, and can solve the electrical large scale electromagnetic scattering problem. The Kelvin wake model of the ship is established by the point source perturbation method. The Elfouhaily spectrum is adopted to construct the rough sea surface. Then the parallel small slope approximation method is used to calculate the electromagnetic scattering of the Kelvin wake produced by the ship. The electromagnetic scattering results were compared with those of pure sea surface. In addition, the effect of different wind speeds and different ship speeds on the electromagnetic scattering characteristics of the ship's Kelvin wake are also discussed. The acceleration ratio of different frequencies and different numbers of samples were calculated to further verify the effectiveness of the parallel algorithm.
Transition Point of the Radio Wave Propagation Generated by Antennas in Rectangular Tunnels

Radio wave propagation characteristics generated by antennas in rectangular tunnels are studied in this paper. A hybrid propagation model is used to describe the propagation characteristics, and the property of the transition points between different propagation models (regions) are analyzed by the waveguide modal method. The results indicate that the location of the transition point is significantly related to the beam width and beam direction of the transmitting antenna and the tunnel dimension. The results are valuable for layout design of the antenna system of wireless communication in confined space.