Miracle-dz/UniFAR-FTUs huggingface dataset:
Demystifying Power and Performance Bottlenecks in Autonomous Driving Systems

Autonomous Vehicles (AVs) have the potential to radically change the automotive industry. However, computing solutions for AVs have to meet severe performance and power constraints to guarantee a safe driving experience. Current solutions either exhibit high cost and power dissipation or fail to meet the stringent latency constraints. Therefore, the popularization of AVs requires a low-cost yet effective computing system. Understanding the sources of latency and energy consumption is key in order to improve autonomous driving systems. In this paper, we present a detailed characterization of Autoware, a modern self-driving car system. We analyze the performance and power of the different components and leverage hardware counters to identify the main bottlenecks. Our approach to AV characterization avoids pitfalls of previous works: profiling individual components in isolation and neglecting LiDAR-related components. We base our characterization on a rigorous methodology that considers the entire software stack. Profiling the end-to-end system accounts for interference and contention among different components that run in parallel, also including memory transfers to communicate data. We show that all these factors have a high impact on latency and cannot be measured by profiling isolated modules. Our characterization provides novel insights, some of the interesting findings are the following. First, contention among different modules drastically impacts latency and performance predictability. Second, LiDAR-related components are important contributors to the latency of the system. Finally, a modern platform with a high-end CPU and GPU cannot achieve real-time performance when considering the entire end-to-end system.
Anatomical Landmarks and DAG Network Learning for Alzheimer’s Disease Diagnosis

The accurate diagnosis and prediction for individuals is crucial in computer-aided diagnosis of Alzheimer’s disease (AD). The existing structural magnetic resonance imaging based classification methods of AD diagnosis mainly focus on the voxel level, region level and patch level morphological pattern analysis. However, most of these methods extract features with high dimension which may lead to overfitting problem. Besides, the interaction of different patches is not considered in the classifier ensemble. In this article, we propose a novel anatomical landmarks and directed acyclic graph (DAG) network feature learning based classification algorithm for the diagnosis of AD individuals. First, the anatomical feature patches of gray matter image are identified by the morphological and statistical analysis. Second, a simple and efficient DAG convolutional neural network is proposed to extract the discriminative deep features of image representation. Especially, the deep features are obtained by fusing feature maps of different network levels which contain semantic high-level and high-resolution low-level features. Finally, support vector machine and deep features are utilized to construct the classification model and predict the individual of AD. Experiments on three public datasets including ADNI-1, ADNI-2 and MIRIAD demonstrate that the proposed method can effectively improve the classification performance compared with the state-of-the-art methods for AD diagnosis.
Creating Computer Models of Radio Engineering Devices for a Virtual Laboratory

The paper is aimed at modeling various radio engineering devices to incorporate them into a virtual laboratory. The development of a microwave transistor amplifier and its integration into a virtual laboratory are considered as well as connecting its model to a model of a vector network analyzer with a completely realistic interface. The created laboratory work allows us to study the configuration and operation of the transistor amplifier, as well as the design technique and tuning input and output amplifier matching circuits.
Recognition method of key nodes in SN-UASN based on TOPSIS

In Underwater Acoustic Sensor Networks (UASN), certain key nodes in the network will cause network partition, communication failure, packet loss once they failed to work. Also, because the key nodes are key positions of the UASN, once the malicious behavior of the key nodes occurs, the network performance will drop sharply. This paper first puts forward the key node recognition method for UASN. Also, we propose node usage and hop distance factors to evaluate node’s importance. Five factors at the topological level are combined, and using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to evaluate the importance of each node in the UASN. In the experiment, the key nodes are replaced by attack nodes to verify the performance of the algorithm. Experiment results show that the algorithm has a good and effective value.
Artificial Neural Network Based Improved Modulation Strategy for GaN–based Inverter in EV

Wide–bandgap (WBG) device based high–frequency inverters using Gallium Nitride (GaN) switches are gaining significant research attention in the field of electric vehicles (EVs) due to their potential to operate at higher switching frequencies with improved efficiency as compared to the available power devices. However, the computation time of the control algorithm plays a significant role in the effective control and operation of such high–frequency converters. This paper presents an advanced artificial neural network (ANN) based improved space vector pulse width modulation (SVPWM) control for Gallium Nitride based inverter in EV application. The proposed neural–network (NN) based control technique has two core objectives: the first objective is to overcome the processing speed of the complex algorithm during high switching frequency operation and hence reduce the computation time which is the major challenge in WBG device–based inverter control. The second objective is to minimize the GaN inverter switching losses and to improve the overall performance of the inverter. The NN based SVPWM is trained using the reference voltage to get the modulated signal for pulse generation, thereby reducing the computation time and improving the performance of the inverter. The proposed ANN–based improved switching strategy has been validated experimentally using a GaN inverter and a comparative performance analysis with a conventional SVPWM technique is presented in this paper.
Near-duplicated Loss for Accurate Object Localization

Multi-class object detection always involves the tasks of accurate target localization which is mainly related to bounding box regression. Smooth L1 loss is the most popular bounding box regression loss used in the current state-of-the-art object detection systems. However, such loss for regressing the parameters of a bounding box can’t accurately and consistently regress the bounding box to the associated ground truth well. We instead propose the near-duplicated loss, a loss that better evaluate the disparity between the bounding box and the ground truth consistently. We present an approximate algorithm associated with a kernel function that not only considers the absolute distance but also involves the relative overlap area between the two bounding boxes. The new loss doesn’t need additional supervision and is easy to embed into existing networks. Our final result, by incorporating the near-duplicated loss into the state-of-the-art object detection detectors (Faster RCNN, RetinaNet), shows consistent and significant improvements on popular object detection benchmarks (MS COCO and Pascal VOC).
A Contrast Pattern-Based Scientometric Study of the QS World University Ranking

Despite the shortcomings and criticisms of world university rankings, such metrics are widely used by students and parents to select institutions and by educational institutions to attract talented students and researchers, as well as funding. This article introduces the first contrast pattern-based scientometric study of world university rankings. Specifically, this study collects a database containing 34 features, which describe the essential research indicators for the top 200 universities in the Quacquarelli Symonds (QS) ranking. The use of 18 state-of-the-art classifiers in this database shows that the top 100 universities in the QS World University Rankings are separable from the remaining compared universities, achieving an average accuracy of 71%. Additionally, using a contrast pattern mining algorithm, a set of patterns describing the top 100 universities is extracted based on scientometric features. Additionally, this study proposes an approach for visualizing the extracted patterns to facilitate the decision-makers, such as senior university managers, in formulating and evaluating their research (ranking) strategies.
Food Recognition with ResNet-50

Object recognition has spurred much attention in recent years. The fact that computers are now able to detect and recognize objects has made Artificial Intelligence field, especially machine learning grow very rapidly. The proposed framework uses Deep Convolutional Neural Network (DCNN) that is based on ResNet 50 architecture. Due to the limited computational resources to train the whole model, the ResNet model is imitated and the pre-trained weights are imported. Thereafter, the last few layers of the model are trained on three datasets that have been acquired online. This process is called fine-tuning a pre-trained model. It is one of the most common approaches in building a DCNN architecture. The dataset that was used to evaluate the performance of the model are ETHZ-FOOD101, UECFOOD100 and UECFOOD256. The parameter setting and results of the proposed method are also presented in this paper.
Explainable Deep Learning for Referable Diabetic Retinopathy

Diabetic Retinopathy (DR) is a retinal lesion due to diabetes. Through blood leaks and excess glucose in the blood vessels, pathological lesions including hemorrhages, exudates and microaneurysms (HM, EX, MA) develop in the eye, which may lead to blindness if not timely treated. In this paper, we propose a deep Convolutional Neural Network (CNN) architecture trained to identify Referable Diabetic Retinopathy (RDR) lesions from retinal fundus images. The model uses a pre-trained network with fine-tuned layers, cosine learning rate decay, and warm up. The efficiency of the proposed architecture has been evaluated on eight public datasets. The results show that the proposed architecture obtains state-of-the-art performance using only publicly available datasets. An explainability algorithm was also developed to show the efficiency of the model in detecting RDR signs.
Performance Evaluation of Low-cost PurpleAir Sensors in Ambient Air

Attention has been paid to low-cost, light-scattering-based particulate matter (PM) sensors, which provide PM measurements in order to supplement a small number of expensive air quality monitoring stations. However, low-cost PM sensors produce measurement data of questionable quality. In this paper, we evaluate the performance of low-cost PM sensors, specifically PurpleAir PA-II units. To evaluate the PurpleAir PA-II units, we use accurate air quality data measured from monitoring stations within close proximity to the PA-II units as reference. By means of linear regression, we compare PurpleAir PA-II units with air quality monitors. From the result, PurpleAir PA-II units have high correlations (R2 ≥ 0.84) with nephelometers, which are based on the principle of light-scattering, and thus the PurpleAir PA-II is a sufficient substitute for nephelometers. PurpleAir PA-II units have a good agreement (R2 = 0.72 and 0.89) with MetOne BAM 1020 monitors based on beta ray attenuation but show a non-linear behavior. Furthermore, there is an essential observation that the PurpleAir PA-II unit needs to have significantly high precision in order to have a high correlation with an expensive reference monitor and thus be used supplementally. The considered PurpleAir PA-II units overestimate PM2.5 concentrations compared with air quality reference monitors. Therefore, a calibration algorithm for PurpleAir PA-II units should be considered to give a correct air quality index at sites considered in this paper. However, our results show the PurpleAir PA-II to be a promising low-cost air quality sensor for supplementing a conventional air quality sensing network with expensive monitors.
Reinforcement Learning Based Test Case Prioritization for Enhancing the Security of Software

In order to enhance the security of software, each system update needs to perform regression test. Regression testing in a continuous integration environment requires test cases to meet the needs of rapid feedback. Therefore, it is necessary to enable test cases to be effectively sorted within a certain time range so that more failure data could be discovered and the fault detection rate of testing could be improved. Reinforcement learning algorithms interact with the environment, so it is viable to optimize the sorting problem of test case in the process of continuous integration through a reward mechanism. In the development environment of continuous integration, it has been experimentally proven that the execution history of test cases in the last four cycles has a greater impact on the sorting of test cases in the current cycle. Therefore, a new RHE reward function was put forward by using part of weighted information obtained from historical execution result for enhancing the security of the system. Taking the influence of execution time into account, the multi-target sequencing technology for test case is employed with a view to improving the efficiency of defect discovery. It has been found by applying this sorting method to three industrial testing research that: (1) compared with weighted reward function based on the entire historical execution information, the function based on the four historical execution information had a higher capability of detecting faults; (2) The reward function obtained from the weighted historical results could effectively improve the fault detection rate and reduce the time consumed. (3) The multi-objective sorting methods taking execution time into consideration was able to maximize the number of testing cases that had already discovered faults within the available time.
High Resolution Localization and Vertical Angle Scattering Spectrum Reconstruction of Targets in Shallow Water Waveguide

In complex ocean environment, reconstructing low-frequency scattering pattern (frequency-angle spectrum) of underwater targets can provide further powerful support for target classification. This paper describes the reconstruction of target scattering pattern with unknown position, from received data on a vertical line array (VLA) in shallow water waveguide. The point spread function (PSF) in conventional matching field processing (CMFP) does not have global strict shift invariance. This paper proves that with the same effective mode number, the PSF retains the shift invariant property and can thus be deconvolved from CMFP by Richardson-Lucy (R-L) algorithm. The proposed deconvolved matched field processing (DMFP) can achieve high resolution along both range and depth direction and low side lobes. By using the estimated target range and depth, and the estimated mode amplitude at each frequency by mode decomposition, the broadband target scattering spectrum with respect to vertical angle is accurately reconstructed with moderate number of modes.
Management Emulation of Advanced Network Backbones in Africa: 2019 Topology

AFRICACONNECT is composed of three advanced networks UBUNTUNET, WACREN and ASREN, which connect the national research and education networks in 29 countries in Africa. Each backbone infrastructure has evolved over time and has been updated, with bandwidth and backbone router capability being added. IPv6 connectivity and management assessment emulation were developed using the backbone topology of AFRICACONNECT from 2019. The results demonstrate the capabilities of the GNS3 emulator when using high-performance backbone networks and offer a top-down view that can support strategic decisions on the evolution of this kind of network, which can be useful to Internet Service Provider companies.
A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation

Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN into making false predictions. The existing methods of generating adversarial examples include pixel-level perturbation or spatial transformation of images, which cannot consider concurrently with the semantic quality of adversarial examples or success rate of attack. These methods are computationally bulky and slow to generate the adversarial examples. To solve this kind of issue, a two-stage generative adversarial networks (TSGAN) with semantic content constraints is proposed in this paper. The first-stage uses the original example dataset to train generator <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>, which can help the generator learn the distribution of real examples. Then, the example semantic quality constraint loss function, the adversarial loss function and the distance loss function are adopted in the second-stage, so that the generator <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> can continue to learn to search the distribution of the adversarial examples, and train the new generator <inline-formula> <tex-math notation="LaTeX">$G_{adv} $ </tex-math></inline-formula>. The adversarial examples generated by generator <inline-formula> <tex-math notation="LaTeX">$G_{adv} $ </tex-math></inline-formula> are better fit the distribution of real examples, and have targeted black-box attack capability. The experiments show that the adversarial examples generated by TSGAN can achieve the success rate of attack at 98.40% in target model, 29.40% success rate in defense-oriented model. And 77.58% success rate is obtained in the transfer test attack. The results show that the adversarial examples generated by the proposed model, which has a highly attack success rate and more difficult to defense. Meanwhile, the improved adversarial examples have stronger transfer ability than the existing models. The proposed model can effectively reduce the expression of target category features of the adversarial examples, and the generated adversarial examples have better semantic quality than others.
Research on Maritime Rescue UAV Based on Beidou CNSS and Extended Square Search Algorithm

With the development of multi-intelligence related technologies and distributed artificial intelligence, people have gradually applied drones to maritime search and rescue work. Maritime search and rescue requires high efficiency and short search time, which is difficult, and how to use UAVs for efficient and rapid maritime search and rescue has become a major concern. In this paper, based on the extended square search algorithm, the original algorithm is improved, and the probability function of target discovery is introduced to make the algorithm closer to the actual search and rescue situation. Finally, based on the MATLAB platform, this paper simulates the algorithm by writing a program, and studies the relationship between flight height, search pitch and search width overlap ratio and search time. The simulation results show that the flying height of the drone is inversely proportional to the search time, and the overlap ratio is proportional to the time. In the aspect of emergency data backhaul and communication, based on the original agreement of Beidou, the necessary transmission agreement keywords are added to facilitate the subsequent processing of the data.
Improved Adaptive Dynamic Surface Control for a Class of Uncertain Nonlinear Systems

An improved dynamic surface control (IDSC) approach is presented for a class of strict-feedback nonlinear systems with unknown functions. The proposed method makes the state errors get rid of the influence of first-order filters, which simplifies the design of control. By employing neural networks to account for system uncertainties, the virtual control signal of the IDSC is directly used to construct the state error instead of the signal generated by the first-order filter in the dynamic surface control (DSC) method. The stability of the method is proved by Lyapunov stability theory, and the semi-global uniform ultimate boundedness of all signals in the closed-loop system is guaranteed. Simulation results demonstrate the IDSC method has better tracking performance and stability than traditional DSC method.
Prediction of NMF-based Wiener Filter for Speech Enhancement Using Deep Neural Networks

In this paper, a novel approach is presented to predict a training target called NMF-based Wiener filter using deep neural networks (DNN) in the nonnegative matrix factorization (NMF) based speech enhancement. The NMF-based Wiener filter, as a masking-based target, is easier than the encoding vectors used in previous algorithms for parameter estimation. The intermediate error of the NMF-based speech enhancement process was reduced due to direct prediction of the NMF-based Wiener filter. The encoding vectors of noisy speech were extracted with the NMF algorithm and normalized to obtain more discriminative input features. The DNN was trained to learn a nonlinear mapping from the encoding vector of noisy speech to the NMF-based Wiener filter. At test stage, the predicted NMF-based Wiener filter was used to enhance noisy speech. The objective evaluations demonstrated that the proposed algorithm outperforms some existing NMF-based and DNN-based methods at various input signal-to-noise ratio (SNR) levels.
Features of Building a Robust Control Channel for the Aircraft Roll Angle

The article discusses the methods of constructing a control channel for the roll angle of an aircraft with a solid fuel engine. For each of the considered schemes, we calculated robust controllers and investigated the achieved control quality. The discovered advantages of the schemes, which differ from the traditional one, are the increased speed response and robustness. The article contains weight functions and numerical values of controller parameters.
FPGA-Based Evaluation and Implementation of an Automotive RADAR Signal Processing System using High-Level Synthesis

HLS enables the design of optimized hardware from behavioral specifications using HLL such as C, C++, and SystemC. Hardware designs were traditionally developed using HDLs such as Verilog, VHDL etc. at the Register Transfer Level. Recently HLS has been gaining popularity due to increasingly better QoR, high productivity and lower development times. HLS gives software developers the ability to implement their designs on FPGAs without requiring detailed knowledge of RTL technologies and HDL. A high-level model for HLS of an automotive RADAR signal processing system has been investigated for the purpose of comparison between hardware design using HLS model and an existing HDL model. A synthesized design of an automotive RADAR signal processing system using Xilinx Vivado HLS-based design methodology is presented in this paper which can be depicted as a mid to high complexity, real world application. Various HLS techniques have been used to optimize the design for both speed and resource utilization while providing a much shorter development time. The FPGA resource utilization increased but it was well under 5% of the total resources available on the FPGA chip, achieving a speed up of 2x when compared to the RTL-based design for the RADAR system while at the same time reducing the development time by 60%.