Miracle-dz/UniFAR-FTUs huggingface dataset:
A Particle Detector Based on Pulse Stretching Inverter Chain

Monitoring of energetic particles responsible for the soft errors is an important requirement in the design of fault-tolerant systems for space missions. In this paper, the use of a custom-sized CMOS pulse stretching inverter chain as a particle detector is studied. The proposed detector allows for measuring the particle flux in terms of the count rate of induced Single Event Transients (SETs). In contrast to the conventional SRAM-based detectors, the proposed solution is potentially more sensitive to particle strikes, requires simpler processing logic and is more immune to multiple errors and error accumulation. The concept has been evaluated with SPICE simulations for the IHP's 130 nm CMOS technology. The target applications are the self-adaptive multi-processor systems, where the particle detector can be used to dynamically trigger the fault-tolerant mechanisms.
3D Convolutional Networks Based Automatic Diagnosis of Alzheimer's Disease Using Structural MRI

Early diagnosis of Alzheimer's disease (AD) can help take timely treatment before patients suffer from irreversible brain damage. However, current studies using manually feature extraction and 2D CNN methods cannot make the classification performance optimized. We proposed an approach using 3D convolutional neural network (CNN) to diagnose AD and mild cognitive impairment (MCI), which is the prodromal stage of AD, from structural magnetic resonance images. Subjects of 150 AD, 129 MCI, and 112 normal controls (NC) from the Alzheimer's Disease Neuroimaging Initiative dataset were used in this study. Specifically, weights of a 3-class network were trained to initialize the 2-class network and fine-tuned, which saves time and avoids overfitting. The experimental results show that our 3D-sVoxCNN model significantly improves the classification performance with the accuracy of 95.05% (AD vs MCI vs NC), 99.47% (AD vs. NC), 98.32% (MCI vs. NC), and 98.32% (AD vs. MCI).
Time Series Similarity Measure via Siamese Convolutional Neural Network

The similarity measure is a fundamental and key problem for time series analysis, which is widely used in classification, cluster, motif discovery and etc. Time series similarity calculation has been studied a lot and many methods have been proposed, such as Euclidean distance and elastic distance measures. However, all these methods are distance-based which get the similarity by accumulating the distances between optimal match pairs and ignore the intrinsic differences of time series. In order to solve the drawback, we discard this distance-based technique and design a Siamese Convolutional Neural Network(SCNN) to obtain the similarity of time series in this paper. Concretely, we first extract the essential features for similarity calculation by Convolutional Neural Network and then ontain the absolute difference between the essential features of time series. To conveniently train the proposed model, we make full use of the label information of datasets and construct a new binary dataset which each example contains two original time series and a binary label. In the proposed Siamese network, the parameters are shared between two branches and we take binary cross-entropy function to train our model. Experimental results on synthetic and real time series datasets show that our SCNN model is effective and achieves improved accuracy for time series classification task compared to other time series similarity calculation methods.
R-Peak Detection for ECG Signal Based on Local Maximums of Signal Magnitude and Correlation

Besides being used for heart disease diagnosis, ECG signal has been increasingly employed as monitoring of excise statues, emotions, stresses, and so on for healthy people. While most ECG detection algorithms were proposed by considering not only normal conditions, but also all kinds of abnormal conditions, these algorithms are usually over complex to be applied to healthy people. In this paper, we proposed a new method for the detection of R-peaks for healthy people based on the local maximums of magnitude and template correlation. A good trade-off between time complexity and detection precision is achieved by introducing a scheme to trigger the more complex template correlation detection when noise exists. In our test data of ECG signal from healthy people, the average sensitivity and precision achieved are 99.23% and 99.38%, respectively, while the computation complexity maintains low.
A Fault Diagnosis Method Based on Wavelet Packet and Manifold Learning

Aiming at nonlinear of mechanical fault signals and complexities and diversities of fault symptoms, a model of fault feature extraction was proposed based on wavelet packet and manifold learning. The improved wavelet threshold method is used to remove noise from the signal, and then the band signal reconstructed by wavelet packet decomposition is used as the feature vector to form the original feature set of bearing fault diagnosis. Using the local tangent space alignment algorithm to compress the original feature set, the new features obtained were input into a K-means classifier, and the output of the K-means classifier was clustering results. The experimental results showed that the proposed method has a better clustering performance than the traditional linear principal component analysis (PCA) method.
Exploring Token-Oriented In-Network Prioritization in Datacenter Networks

In memory computing and high-end distributed storage demand low latency, high throughput, and zero data loss simultaneously from datacenter networks. Existing reactive congestion control approaches cannot both minimize queuing latency and ensure zero data loss. A token-oriented proactive approach can achieve them together by controlling congestion even before sending data packets. However, state-of-the-art token-oriented approaches only strive to optimize network-level metrics: maximizing throughput while achieving flow-level fairness. This article answers the question of how to support objective-aware traffic scheduling in token-oriented approaches. The novelty of Token-Oriented in-network Prioritization (TOP) is that it prioritizes tokens instead of data packets. We make three contributions. Via simulations over a hypothetical TOP system, our first contribution is demonstrating the potential performance gain that can be brought by TOP. Second, we investigate the applicability of TOP. Although the overhead of enabling necessary TOP features in switches is trivial, we find that mainstream commodity datacenter switches do not support them. We hence propose a readily-deployable remedy to achieve in-network prioritization by pushing both switch and end-host hardware capacity to an extreme end. Lastly, we implement a running TOP system with Linux hosts and commodity switches, and evaluate TOP in testbeds and with large-scale simulations for various scenarios.
Drift induced rigid current shift in Ge-Rich GST Phase Change Memories in Low Resistance State

Ge-Rich GST based Phase Change Memories (PCMs) represent a valid candidate for embedded non-volatile storage due to high-temperature stability, which enables data retention compatible with automotive applications and data integrity in case of soldering reflow stress. Howeover, Ge-Rich GST based PCMs are affected by resistance drift both for cells programmed in the high-resistance state (HRS) and for cells programmed in the low-resistance state (LRS). Our experimental investigation, carried out on a population of 500k cells in a memory array, showed that the variation over time of the LRS cell current is substantially constant for all the cells in the array, regardless of the value of the cell current (and, hence, of the cell resistance) measured just after programming, which results in a rigid cell current shift. Starting from the obtained experimental results, we derived an expression to calculate the drift coefficient in LRS cells as a function of their initial resistance.
Synthesis of Ultraminiaturized Frequency-Selective Surfaces Utilizing 2.5-D Tapered Meandering Lines

An ultraminiaturized bandstop frequency-selective surface (FSS) utilizing two-dimensional (2-D) tapered meandering lines (TML) along with vertical vias is proposed. Full-wave simulations, equivalent circuit modeling, and analytical results are presented for the designed FSS. A prototype 2.5-D TML-FSS is fabricated, and its performance is measured in an anechoic chamber for various incident angles in both the transverse-electric (TE) and transverse-magnetic (TM) polarizations. Measured results are in agreement with the simulated ones, indicating the authenticity of the design process. Unit cell size of the proposed FSS is only <inline-formula><tex-math notation="LaTeX">$\text{2.9}\%$</tex-math></inline-formula> of the free-space wavelength at resonant frequency and has a fractional bandwidth of <inline-formula><tex-math notation="LaTeX">$\text{20.9}\%$</tex-math></inline-formula>. Furthermore, the proposed design is modified to convoluted 2.5-D TML-FSS and interdigitated 2.5-D TML-FSS to give different unit cell sizes in terms of wavelength with different fractional bandwidths.
Web Front-End Realtime Face Recognition Based on TFJS

Web front-end face recognition enables developers to run models on browser-side to reduce latency and server load. In this paper, we first analyzed different algorithms, standard APIs and frameworks that can be used in web front-end face recognition. Then, we designed a workflow for this and implemented it in JavaScript. In the end, we tested it on different scenarios, and the result shows that front-end real-time face recognition is possible and user acceptable.
Segmentaion of Parapapillary Atrophy in Retinal Images using HED

Parapapillary atrophy (PPA) is a kind of atrophic abnormality of eyes, which lies on the retinal fundus adjacent to optic disc (OD). Clinically, PPA is correlated with several pathologies of eyes. For example, the area of PPA has a strong association with the severity of myopia. In this work, we developed a new segmentation algorithm aimed at PPA in retinal images. The frame work was derived from Holistically-Nested Edge Detection (HED), which originally deals with edge detection. Firstly, extracting region of interest (ROI) and normalizing datasets were performed in preprocessing stage. Next, an end-to-end model began to acquire rich hierarchical features with deep supervision on side outputs. We applied a new balance parameter to loss function for realization of the area segmentation. Finally, the holistic image predictions (PPA area segmented) were performed through full convolutional neural layers. The model training on retinal images with annotation from professional ophthalmologist achieves great performance on both adults and pupils datasets. Experiential results present the average F-score of 0.7910 on adults' test data (200 images) and 0.7478 on pupils' (50 images). MIoU of the segmentation reaches 0.7237 of adults and 0.6892 of pupils.
A Data-Efficient Approach for Automated Classification of OCT Images Using Generative Adversarial Network

Deep learning algorithms can offer a reliable automated interpretation of retinal optical coherence tomography (OCT) images to assist clinicians in disease diagnosis and management. However, retinal image processing presents pertinent obstacles such as the struggle of large-scale data acquisition and high cost of annotation. To address this, we have developed a data-efficient semisupervised generative adversarial network based classifier for automated diagnosis with limited labeled data. The framework consists of a generator and a discriminator. The adversarial learning between them assists in building a generalizable classifier to predict progressive retinal diseases like age-related macular degeneration and diabetic macular edema. Experimental results on clinical-grade OCT images show an overall improvement of more than 10% in accuracy, compared to the state-of-the-art methods.
Algorithmic Level Approximate Computing for Machine Learning Classifiers

Efficiency, real-time operation and low-power consumption are the main requirements of embedded Machine Learning implementations. This paper proposes an approach for applying Algorithmic level Approximate Computing Techniques (ACTs) on two supervised machine learning algorithms. The proposed approach has been validated in two different applications: touch modality and image classification. Results demonstrate that the proposed approach improves the performance with a speedup up to 3.2× with an accuracy loss of less than 4.7%.
Towards Uniform Modeling and Holistic Testing of Hardware and Software

This paper introduces an approach to uniform modeling and testing of hardware and software systems and their faults. As an example, for hardware under consideration, designs at a behavioral level will be used, implemented in Hardware Description Language (HDL). For software, an example will be borrowed from a graphical user interface design. Both examples will be modeled by finite state machines. The mutation of these models leads to lucid hardware and software fault models, respectively. Original models and their mutants will then be used to generate test cases for positive testing and negative testing, respectively, forming a holistic test strategy. A positive test is supposed to validate the system under legal (expected, regular) circumstances, whereas a negative test checks the behavior of the system under illegal (unexpected, irregular) situations. Non-trivial examples are used to validate and analyze the approach with respect to uniform modeling and testing capability.
Enhancing the Representation of Multiple Anatomical Network for Young Adults with Self-Esteem Difference

Anatomical brain networks help explain how distributed brain area are connected, and the connectivity differences have been used to recognize neuroimaging biomarkers for mental and psychologic symptoms. However, most methods analyzing anatomical brain networks employ single-level network framework, which cannot give an overall representation on the subtle deviations of the whole brain. In this study, we put forward an enhancing representation of multiple anatomical networks for young adults with self-esteem differences. Specifically, we construct a four-layer network framework using both morphological features (i.e. gray matter volume, cortical thickness) for each region of interest (ROI) and hierarchical networks features. Two types of filter-based approaches are adopted, followed by a wrapper-based approach to select a set of distinctive features. Then, multi-kernel machine learning method is used to combine two kinds of features (ROI-based morphological features and hierarchical correlative features) into a single classifier. Our method achieves an accuracy of 97.26%, which has an outstanding performance compared with the single-network methods. This study gives a potential evidence that self-esteem is related to specific ROIs and anatomical brain connections.
Remote Sensing Image Translation Using Spatial-Frequency Consistency GAN

Synthesis aperture radar(SAR) is a powerful technique for remote sensing. The major advantage of SAR is all-weather and all-day imaging. However, SAR images are too hard for ordinary people. Motivated by the development of deep learning, it is available for the image translation from SAR images to optical images. Unfortunately, existed image translation methods ignore unique imaging of remote sensing images. Under unique imaging, remote sensing images are noisy and low resolution. Based on the observation, we propose our method, SFGAN, for remote sensing image translation using a generative adversarial network, which is trained on SEN1-2 Dataset. Our experiments compare our method with some state-of-the-art methods. Results show that the proposed method preserves more linear features under various area and has a higher PSNR.
Accuracy Evaluation of Camera-based Vehicle Localization

Cooperative maneuvers are of high interest within many V2X applications. The implementation of cooperative maneuvers require the accurate localization of the vehicles. Accurate localizations of the ego-vehicle will be provided by the next generation of connected cars using 5G. Until all cars participate in the network, unconnected cars have to be considered as well. These cars are localized via static cameras positioned next to the road. The scope of this paper is the implementation and evaluation of a system which provides the detection, tracking, and localization of vehicles for a cooperative maneuvers scenario. The application is the lane merge of vehicles where the vehicle localizations are used for the planning of trajectories. The observed vehicles are equipped with GNSS RTK units for their self-localization which is the basis for the accuracy evaluation of the localization provided by the camera system.
People Counting Based on Head Detection and Reidentification in Overlapping Cameras System

People counting is one of the key tasks in video surveillance system. Usually, one camera cannot cover all the area for a big room or plaza, so multiple cameras will be used. For overlapping cameras system, the overlapped area should be marked for people counting. To get the area, an easy way is to calibrate the cameras. But in real practical system there are hundreds of cameras, it’s impracticable to calibrate all the cameras. A practicable way is to get the overlapping area is to applying image mosaic algorithm. Using local features such as SIFT cannot to find the overlapping area due to corresponding low quality and repeated similarity (such as seats and tables) in most surveillance environment. In this paper, we are committed to finding repetitive people to improve the accuracy of the population statistics. First, a person head detector is trained to detect the human head in the frame taken by each camera video and then cut it, and the images of the head pair taken by each camera are taken as a database which belongs to this camera. Then, we select one database as Gallery, image from another database as the Probe, we use a Siamese networks to match a probe with Gallery, repeating the above process until all the iterations of the probe in the database are completed, so that we find all the recurring people between the two cameras. Finally, the median of the total number of people in all video frame image counts is calculated, and the median result is taken as the final statistical result of the scene.
Implementation and Verification of a Virtual Testing System Based on ROS and Unity for Computer Vision Algorithms

With the development of artificial intelligence technology, Computer vision algorithm is playing an increasingly important role. Computer vision algorithm testing is a vital link to ensure the safe and reliable operation of agents. However, the traditional testing methods based on real scenes provide single samples and are challenging to obtain ground truth, which makes it inefficient to test computer vision algorithms. To solve this problem, the testing method of computer vision algorithms using virtual scenes instead of real scenes has been applied. In this paper, Unity and robot operating system (ROS) are selected to build a virtual testing system named URCV for computer vision algorithms. The feasibility of the system and the influence of virtual scene elements on the testing of the monocular ORB_SLAM2 algorithm are verified, including the rendering path of Unity's RGB camera, texture accuracy, and illumination model.
Application of Metamaterials for the Design of Antenna

Metamaterials have a very important role to play in new technologies, especially in the field of telecommunications. With the availability of the negative refractive index of metamaterials, the performance of microwave devices can be improved. These artificial devices have several advantages such as their integration with the planar environment and also their low cost of manufacture. In this article, we are interested in the design of volumic antennas in SIW using longitudinal slots and CSRR resonators.The results of the design of these antennas show an improvement in their performance in terms of adaptation and bandwidth.
Improvement of Lane Keeping Assistance ADAS Function utilizing a Kalman Filter Prediction of Delayed Position States

In designing and implementing control systems, converting simulation based results to real life systems is often not straightforward and may need adaptation of the control approach to achieve similar performance levels to the simulation results. Such adaptations are usually required due to the fact that sensors and actuators have a number of imperfections such as delays, offsets and inherent noise processes. Here, such a problem in relation to the development of a lane keeping control algorithm is presented. An in-house developed lane keeping controller based on a high-fidelity simulation environment was planned to be transferred to a real demonstrator test vehicle. First tests showed significantly deteriorated and unstable performance results of the corresponding controller, which was due to sensor delays and actuator imperfections. After the diagnosis of the problem, an approach to mitigate these issues was undertaken by predicting the delayed sensor data utilizing a linear Kalman filter and an a-priori predictor. The Kalman filter and a-priori predictor design approach is based on a discrete-time version of the lane tracking model. The approach and the corresponding results were demonstrated using simulation and real vehicle implementation results that were evaluated in real driving conditions.
Localization Bar Detection by Deep Learning in Magnetic Resonance Guided Focused Ultrasound

Magnetic resonance guided focused ultrasound (MRgFUS) can focus the ultrasonic energy on a targeted tissue to achieve ablation. In this study, we propose a convolutional neural network (based on Tensorflow) based upon deep learning to detect the position bars to registry the coordinate of MR and HIFU systems, instead of the traditional detection algorithm. The results show that the model achieves the average precision (AP) of 95.6% with the precision of 99.0% and the recall of 99.0% at a score threshold of 0.5.
Using the Tassled Cap Transformation of the Fused GF-1 Multispectral Image to Detect the Quasic-Circular Vegetation Patches

The quasi-circular vegetation patches (QVPs) distributed in the Yellow River Delta, China have attracted more attentions because they are ideal objects for studying spontaneous plant colonization in order to develop an adaptive passive wetland restoration strategies which are cost-effective compared to the current implemented active restoration strategies. In this study, the approach based on the tasseled cap brightness and greenness components derived from the March Gaofen-1 image with the decision tree classifier was developed for quickly and successfully mapping the QVPs. The results showed that the precision, recall rate, and F-measure were 62.4%, 57.4%, and 59.8%, respectively. In the future, the time series tasseled cap components and better shape detection indices are needed to build more appropriate splitting thresholds to improve the detection accuracy of the QVPs.