Miracle-dz/UniFAR-FTUs huggingface dataset:
A Texture Generation Approach for Detection of Novel Surface Defects

Surface defect detection is challenging due to varying defect types and their novelties. Because of this, it is hard for algorithms to implement across datasets. Moreover, current automated optical inspection (AOI) machines cannot handle this novelty effectively. In this work, we develop a new method for surface defect detection based on generative models, which can detect novelty according to learned distributions. Experimental results on real industrial datasets show that the proposed method can successfully construct the surface texture pattern generator. By transforming the image through the generator to the corresponding latent space, the defects can be separated effectively without a tedious effort of annotation in a large amount of training data.
Download Elastic Traffic Rate Optimization via NOMA Protocols

Non-orthogonal multiple access (NOMA) is a promising scheme for the fifth generation (5G) of mobile communication systems. In this scheme, transmission to multiple users is performed on the same subchannel using superposition coding and successive interference cancellation. In this paper, we focus on a multi-cell network with two users’ data traffic models, namely elastic and streaming. We exploit the NOMA scheme in order to maximize the download elastic traffic rate at cells, without degrading the download streaming traffic rates. Since elastic traffic rates at different cells are interactive, we maximize the total elastic traffic rates assuming either perfect or partial channel state information available at each base station subject to a target rate for streaming users. Because of the interference due to NOMA as well as the interference among different cells, subchannel assignment and power allocation affect the system performance significantly. For this reason, we propose an iterative algorithm to jointly solve the subchannel assignment and the power allocation problem via the Hungarian algorithm and successive convex approximation, respectively. Finally, our simulation results show the efficiency of the proposed algorithm.
Image Registration via Geometrically Constrained Total Variation Optical Flow

In this paper, we present a method for registration of image pairs. Our method relates both images to one another for registration purposes making use of optical flow. We formulate the problem in a variational setting making use of an L1-norm fidelity term, a total variation (TV) criterion, and a geometric constraint. This treatment leads to a cost function, in which, both the total variation and the homographic constraints are enforced via regularisation. Further, to compute the flow we employ a multiscale pyramid, whereby the total variation is minimized at each layer and the geometric constraint is enforced between layers. In practice, this is carried out by using a Rudin-Osher-Fatemi (ROF) denoising model within each layer and a gated function for the homography computation between layers. We also illustrate the utility of our method for image registration and flow computation and compare our approach to a mainstream non-geometrically constrained variational alternative elsewhere in the literature.
Study of Dynamic Behavior of Magnetostrictively Patterned Flexible Micro-Wings

For the design of magnetostrictive MEMS devices, finite-element modeling (FEM), the determination of the magnetostrictive coefficient, and application examples were examined in this paper. The magnetostrictive coefficients are usually calculated by measuring the bending deflection of the cantilever on which magnetostrictive materials are deposited. In most of the research on the magnetostrictive deposited substrate for the derivation of structural formulas, it is commonly assumed that the flexural rigidities of magnetostrictive materials are negligible because magnetostrictive films are much thinner than substrate materials. In this paper, the effect of magnetostrictive thin film’s elastic energy on the magnetostrictive coefficient is investigated. A self-coded and developed finite-element program for magnetostrictive actuators is formulated, where magnetic properties are considered as nonlinear, and elastic properties are assumed as linear. As a result, it is potentially shown that magnetostrictive thin-film micropatterned actuators would be accommodated for targeted actuating behavior through an FEM capability for evaluation and selective micropatterning technique for fabrication.
DR DRAM: Accelerating Memory-Read-Intensive Applications

Today, many data analytic workloads such as graph processing and neural network desire efficient memory read operation. The need for preprocessing various raw data also demands enhanced memory read bandwidth. Unfortunately, due to the necessity of dynamic refresh, modern DRAM system has to stall memory access during each refresh cycle. As DRAM device density continues to grow, the refresh time also needs to extend to cover more memory rows. Consequently, DRAM refresh operation can be a crucial throughput bottleneck for memory read intensive (MRI) data processing tasks. To fully unleash the performance of these applications, we revisit conventional DRAM architecture and refresh mechanism. We propose DR DRAM, an application-specific memory design approach that makes a novel tradeoff between read and write performance. Simply put, DR has two layers of meaning: device refresh and data recovery. It aims at eliminating stall by enabling read and refresh operations to be done simultaneously. Unlike traditional schemes, DR explores device refresh that only refreshes a specific device at a time. Meanwhile, DR increases read efficiency by recovering the inaccessible data that resides on a device under refreshing. Our design can be implemented on existing redundant data storage area on DRAM. In this paper we detail DR's architecture and protocol design. We evaluate it on a cycle accurate simulator. Our results show that DR can nearly eliminate refresh overhead for memory read operation and brings up to 12% extra maximum read bandwidth and 50~60% latency improvement on present DRR4 device.
Generative Adversarial Network (GAN) Based Data Augmentation for Palmprint Recognition

Palmprint recognition is a very important field of biometrics, and has been intensively researched on both feature extraction and classification methods. Recently, deep learning techniques such as convolutional neural networks have demonstrated clear advantages over traditional learning algorithms for various image classification tasks such as object recognition and detection. However, a large amount of data is needed to train deep networks, which limits its application to some tasks such as palmprint recognition where it lacks of sufficient training samples for each class (i.e., each individual). In this paper, we propose a Generative Adversarial Net (GAN) based solution to augment training data for improved performance of palmprint recognition. An improved Deep Convolutional Generative Adversarial Net (DCGAN) is first devised to generate high quality plamprint images by replacing convolutional transpose layer with linear upsampling and introducing Structure Similarity (SSIM) index into loss function. As a result, the generated images have discriminative features, increased smoothness and consistency, and less variance compared to those generated by the baseline DCGAN. Then, a mixing training strategy via a combination of GAN-based and classical data augmentation techniques is adopted to further improve recognition performance. The experimental results on two publicly available datasets demonstrate the effectiveness of our proposed GAN based data augmentation method in palmprint recognition. Our method is able to achieve 1.52% and 0.37% Equal Error Rates (EER) on IIT Delhi and CASIA palmprint datasets, respectively, which outperforms other existing methods.
Extracting Visual Encodings from Map Chart Images with Color-Encoded Scalar Values

Map charts are used in diverse domains to show geographic data (e.g., climate research, oceanography, business analysis, etc.). These charts can be found in news articles, scientific papers, and on the Web. However, many map charts are available only as bitmap images, hindering machine interpretation of the visualized data for indexing and reuse. We propose a pipeline to recover both the visual encodings and underlying data from bitmap images of geographic maps with color-encoded scalar values. We evaluate our results using map images from scientific documents, achieving high accuracy along each step of our proposal. In addition, we present two applications: data extraction and map reprojection to enable improved visual representations of map charts.
Scene Understanding with Support Relation Inference for Exoskeleton Robot

The exoskeleton robot in the rapid development requires physical relation interpretation about scene understanding to fulfill effective interaction. Although traditional scene understanding provides good performance of object recognition and semantic segmentation, it tells us little about physical relation interpretation. Meanwhile, there are few methods to demonstrate the contextual relations among objects in the scene. While some of them artificially define prior constrains and solve the equation by minimizing the objective function, others aim at perceiving the stability of 3D blocks to classify the support relation to several classes. In this paper, a novel approach is proposed which can automatically get the prior knowledge based on deep learning and Conditional Random Fields(CRF) to infer support relationship. In addition, unlike all of previous methods applying the post-processing step after segmenting or recognizing objects, our method is based on end-to-end deep network which could be trained by back-propagation algorithm. The support relationship can be directly obtained as the output from a RGB picture input through our approach, where no other intermediate steps are needed. We show the effectiveness of the proposed method on the NYU-Depth V2 dataset in which we manually re-annotate 1449 images.
Utilizing Narrative Text from Electronic Health Records for Early Warning Model of Chronic Disease

Chronic diseases are associated with high morbidity and mortality, and they cannot be quickly determined and identified during their early stages. Therefore, early identification and warning of chronic diseases are important. This study proposed an early warning model (EWM) based on electronic health records (EHRs). The model included comprehensive methods that identify whether the patient is expected to suffer from chronic disease and provide early warning based on the undiagnosed narrative text of EHRs. A professional medical terminology library called Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) was utilized to improve the accuracy of early warning. The model utilized semantic relationships in the SNOMED CT to expand the effects of related terms of renal cancer. We utilized 1,300 medical records and more than 1,400 progress notes in the EHRs in our experiments. The proposed EWM of renal cancer achieved a precision of 90%, recall of 91%, and F-measure of 91%.
Leaderless Consensus for Multiple Euler-Lagrange Systems with Event-Triggered Communication

In this paper, the problem of consensus for multiagent systems, modeling by Euler-Lagrange equations, is studied with event-triggered communication network. By utilizing a proportional plus damping control scheme, a triggering mechanism is developed so that agents can achieve consensus without continuously exchange their signals. The networked system is demonstrated to be stable with guaranteed consensus performance if the communication topology between agents are undirected and connected. Moreover, a triggering gain is introduced to tune the triggering sensitivity and frequency. The lower bounds of inter-event time for agents are presented so that the Zeno behavior is avoided in the event-triggered control system. The proposed system can reduce network-access-frequency, and stop exchanging output signals when the multi-agent system achieve consensus. Simulations with four Euler-Lagrange agents are illustrated to show the performance and efficiency of the proposed system.
Using Visible+NIR information for CNN face recognition

Basically every digital camera can acquire information that extends the visible spectrum since the sensor is sensitive to the near-infrared spectrum - and this sometimes requires only minor modifications to the device. So, using a conventional digital camera and stripping off the internal ICF (Infrared Cut-off Filter) filter, we use the captured Visible + NIR images (also called full-spectrum or VNIR images) for the classical face recognition problem. The camera stores the image as 3 channels RGB files, and training and evaluating a CNN with these full-spectrum images lead to surprisingly good results. On the contrary, doing the same with RGB+NIR (4 channels) does not perform as well. The paper shows that the contribution of the blue channel to this task is weak, and the recognition rate raises significantly when NIR is added to the channels, adding information and increasing signal to noise ratio especially in the blue channel.
Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network

Fault detection of nonlinear processes using Kernel Principal Component Analysis(KPCA) method has recently prompt a lot of interest due to its industrial practical importance. However, this method cannot be applied for data sets with a large amount of samples. To overcome this deficiency, this paper proposes a reduced KPCA method based on K-means clustering. This method aims to find a reduced data set among the training data in the input space and uses this reduced data set to built the reduced KPCA model in the feature space. The relevance of the proposed method is illustrated on an air quality monitoring network. The simulation results demonstrate the effectiveness of the new method when compared to the classical KPCA technique.
Design of a 1-MHz High-Efficiency High-Power-Density Bidirectional GaN-Based CLLC Converter for Electric Vehicles

In this paper, a bidirectional CLLC converter operating at MHz-level switching frequency is proposed to achieve high power density and high efficiency. An optimized stacked printed circuit board winding based planar transformer with adjustable leakage inductance is modeled and designed for the proposed converter. It is enabled using gallium nitride devices to achieve fast switching with low switching losses. A detailed power loss model is established for the efficient thermal design of the converter. A proof-of-concept hardware of 3.3 kW, 400–450 VDC input, 250–420 VDC output CLLC converter with 1 MHz operating frequency is built and tested over a wide range of loading conditions. The power density of the system is 9.22 W/cm3 (151.1 W/in3) and the peak efficiency achieves over 97%.
Design and Experiment of Wideband Circularly-Polarized Millimeter-Wave Transmitarrays

In this paper, the design, modeling, and experiment of wideband circularly-polarized transmitarrays operating in the Q-band are presented. The transmitarray is composed of anisotropic unit cells with spatially-dependent rotation angles, which possess a response resembles that of a half-waveplate. Based on the scattering properties of the unit cells, a vector field analysis method is used to model the electrically large transmitarrays that are not able to be simulated by a full-wave solver. To validate the proposed design and modeling method, a single-beam Q-band circularly-polarized transmitarray is tested, showing a strong agreement between measured and calculated results. It achieves a peak gain of more than 30 dBi and a 2-dB gain bandwidth of more than 15%. In addition, by using the intersection approach, the design of a transmitarray that can emit multiple shaped beams is also reported.
A Prediction-Based Charging Policy and Interference Mitigation Approach in the Wireless Powered Internet of Things

The Internet of Things (IoT) technology has recently drawn more attention due to its ability to achieve the interconnections of massive physic devices. However, how to provide a reliable power supply to energy-constrained devices and improve the energy efficiency in the wireless powered IoT (WP-IoT) is a twofold challenge. In this paper, we develop a novel wireless power transmission (WPT) system, where an unmanned aerial vehicle (UAV) equipped with radio frequency energy transmitter charges the IoT devices. A machine learning framework of echo state networks together with an improved ${k}$ -means clustering algorithm is used to predict the energy consumption and cluster all the sensor nodes at the next period, thus automatically determining the charging strategy. The energy obtained from the UAV by WPT supports the IoT devices to communicate with each other. In order to improve the energy efficiency of the WP-IoT system, the interference mitigation problem is modeled as a mean field game, where an optimal power control policy is presented to adapt and analyze the large number of sensor nodes randomly deployed in WP-IoT. The numerical results verify that our proposed dynamic charging policy effectively reduces the data packet loss rate, and that the optimal power control policy greatly mitigates the interference, and improve the energy efficiency of the whole network.
High-Selectivity Balanced-to-Unbalanced Filtering Power Divider

A new type of wide-band differential-mode -to-single-ended filtering power divider is presented in this paper. It makes use of half-wavelength ring resonators and coupled-line sections to realize high common-mode suppression within a broad frequency interval. A proof-of-concept microstrip prototype for the proposed circuit that operates in the 1.7-2.7-GHz 4/5-Generation (4/5G) Long-Term-Evolution (LTE) band is manufactured and measured. It features state-of-the-art performance metrics for this RF component in a reduced circuit area of about $0.5\lambda_{\text{g}}\times 0.5\lambda_{\text{g}}$. They include a broad balanced-to-single-ended upper stopband and high common-mode rejection up to four times their center frequency.
On the Design of Penalty Structures for Minimum-Violation LTL Motion Planning

This paper studies the problem of penalizing rule violation in the context of logic-based motion planning. Translating a given Linear Temporal Logic (LTL) rule into a penalty structure requires a design decision, since the discrete automata obtained from the rule do not provide a straightforward method to penalize rule violation. We propose a design method that explicitly specifies violation to allow for more flexibility in parametrization of desired behaviors and differentiation of penalty semantics. Case study results are shown in the context of an autonomous driving scenario.
Cloud Computing Fuzzy Adaptive Predictive Control for Mobile Robots

The purpose of this paper is on the use of cloud computing for efficiently planning autonomous real-time prespecified trajectory tracking and obstacle avoidance control for an omnidirectional wheeled robot using fuzzy adaptive predictive control algorithm. The autonomous trajectory tracking control includes dynamic simulation, omnidirectional wheeled robot control, and the feedback signal mainly provided by the sensor object surface and depth measurement. The robot is equipped with three independent-driven omnidirectional wheels and six ultrasonic sensors. The Jacobian between Cartesian space corresponding to the joint space of the robot is setup for ellipse motion planning so that it can autonomously follow the prespecified trajectory tracking, obstacle avoidance, and other sports. An architecture is setup to split computation between the remote cloud and the robot so that a robot can interact with a computing cloud. Given this robot/cloud architecture, the stability of the closed-loop control system from the Lyapunov theorem for the fuzzy adaptive predictive control algorithm and trajectory planning is guaranteed with satisfactory performance on the cloud during a periodically updated preprocessing phase efficiently, and manipulation queries on the robots given changes in the workspace can achieve real-time trajectory tracking and obstacle avoidance with ellipse motion planning control. Finally, tradeoffs arising between path quality and computational efficiency are evaluated through simulation, and experiments are given for analyzing the control performance.
On-Chip Self-Interference Canceller with Variable Inductor Based Electrical Balance Network for Full Duplex Systems

An on-chip broadband self-interference canceller based on the electrical balance of hybrid transformers is proposed in this work. A variable inductor based on-chip balanced impedance network is utilized to match the actual antenna impedance varying with frequency. In order to achieve broadband electrical balance, an auxiliary quadrature digital transmitter is used to compensate variations of the antenna impedance with frequency. The proposed technique is demonstrated in a standard 130 nm CMOS 1P8M process and the chip occupies 4.84mm2. The simulation results show that the demonstration achieves isolation of more than 60 dB over a 25 MHz bandwidth with 23 dBm transmitter output power at 2 GHz.
Conditions for rate stability in constrained RFID networks

RFID networks are subject to constraints derived from the interference caused by concurrent readers’ operation. In this paper we discuss a general approach to decide whether a given system load produces stable operation, that is, if queue backlogs do not grow indefinitely. We assume that the RFID network operates in a time-slotted fashion, where traffic arrives as tag batches to RFID cells. The service rate (necessary to determine stability regions) of a RFID cell, given the batch size distribution and any arbitrary FSA policy is computed by simulation. This framework is illustrated with several examples.
Multimodal Emotion Recognition Using Classifier Reliability-Based Aggregation

This paper addresses emotion recognition by first individually processing and then aggregating different modes of human communication through a classification and aggregation framework. Specifically, the proposed framework processes speech acoustics, facial expressions, and body language using unimodal emotion classifiers. The speech emotion is classified using a deep neural network (DNN) while facial and body language emotion classifiers are implemented using classifiers implemented through supervised fuzzy adaptive resonance theory. The speech emotion classifier uses acoustic features, the facial emotion classifier uses features based on facial animation parameters (FAP), and body language emotion classifier uses head and hands features. The unimodal evaluations are then aggregated this paper also proposes classifier reliability-based aggregation preferences for the unimodal evaluations. The reliability-based preferences are extracted from the accuracies of the unimodal classifiers for each emotion. The results show that the proposed framework outperforms the existing techniques. Furthermore, because of late fusion, the functionality of the proposed approach is robust to unavailability of all but one of the modes of communication.
Design of SIW Frequency Agile Antenna Based on a Varactor Loaded on U-Slot

This work describes a new kind of frequency reconfigurable antenna loaded by two varactor diodes. The proposed structure is designed on hexagonal SIW cavity radiating through a U-slot. , integrated in the lower face of the SIW. The antenna is fed by a grounded coplanar transmission line attached to a 50 ! l microstrip line. The simulation results show that the resonance frequency is varied from 10.4 GHz to 10.76 GHz when the two BBY31 diodes are controlling. The radiation pattern is not affected during the variation of the resonance frequency. This is a suitable propriety for several wireless applications.