Boyethybo4298

Modern autonomous vehicles are required to perform various visual perception tasks for scene construction and motion decision. The multiobject tracking and instance segmentation (MOTS) are the main tasks since they directly influence the steering and braking of the car. Implementing both tasks using a multitask learning neural network presents significant challenges in performance and complexity. Current work on MOTS devotes to improve the precision of the network with a two-stage tracking by detection model, which is difficult to satisfy the real-time requirement of autonomous vehicles. In this article, a real-time multitask network named YolTrack based on one-stage instance segmentation model is proposed to perform the MOTS task, achieving an inference speed of 29.5 frames per second (fps) with slight accuracy and precision drop. The YolTrack uses ShuffleNet V2 with feature pyramid network (FPN) as a backbone, from which two decoders are extended to generate instance segments and embedding vectors. Segmentation masks are used to improve the tracking performance by performing logic AND operation with feature maps, proving that foreground segmentation plays an important role in object tracking. The different scales of multiple tasks are balanced by the optimized geometric mean loss during the training phase. Experimental results on the KITTI MOTS data set show that YolTrack outperforms other state-of-the-art MOTS architectures in real-time aspect and is appropriate for deployment in autonomous vehicles.Enabling a neural network to sequentially learn multiple tasks is of great significance for expanding the applicability of neural networks in real-world applications. However, artificial neural networks face the well-known problem of catastrophic forgetting. XMU-MP-1 What is worse, the degradation of previously learned skills becomes more severe as the task sequence increases, known as the long-term catastrophic forgetting. It is due to two facts first, as the model learns more tasks, the intersection of the low-error parameter subspace satisfying for these tasks becomes smaller or even does not exist; second, when the model learns a new task, the cumulative error keeps increasing as the model tries to protect the parameter configuration of previous tasks from interference. Inspired by the memory consolidation mechanism in mammalian brains with synaptic plasticity, we propose a confrontation mechanism in which Adversarial Neural Pruning and synaptic Consolidation (ANPyC) is used to overcome the long-term catastrophic fication and generation tasks with multiple layer perceptron, convolutional neural networks, and generative adversarial networks, and variational autoencoder. The full source code is available at https//github.com/GeoX-Lab/ANPyC.Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a growing demand for hardware accelerators that accommodate a variety of CNNs to improve their inference latency and energy efficiency, in order to enable their deployment in real-time applications. Among popular platforms, field-programmable gate arrays (FPGAs) have been widely adopted for CNN acceleration because of their capability to provide superior energy efficiency and low-latency processing, while supporting high reconfigurability, making them favorable for accelerating rapidly evolving CNN algorithms. This article introduces a highly customized streaming hardware architecture that focuses on improving the compute efficiency for streaming applications by providing full-stack acceleration of CNNs on FPGAs. The proposed accelerator maps most computational functions, that is, convolutional and deconvolutional layers into a singular unified module, and implements the residual and concatenative connections between the functions with high efficiency, to support the inference of mainstream CNNs with different topologies. This architecture is further optimized through exploiting different levels of parallelism, layer fusion, and fully leveraging digital signal processing blocks (DSPs). The proposed accelerator has been implemented on Intel's Arria 10 GX1150 hardware and evaluated with a wide range of benchmark models. The results demonstrate a high performance of over 1.3 TOP/s of throughput, up to 97% of compute [multiply-accumulate (MAC)] efficiency, which outperforms the state-of-the-art FPGA accelerators.The problem of finite-time adaptive tracking control against event-trigger error is investigated in this article for a type of uncertain nonlinear systems. By fusing the techniques of command filter backstepping technical and event-triggered control (ETC), an adaptive event-triggered design method is proposed to construct the controller, under which the effect of event-triggered error can be compensated completely. Moreover, the proposed controller can increase robustness against uncertainties and event error in the backstepping design framework. In particular, we establish the finite-time convergence condition under which the tracking error asymptotically converges to zero in finite time with the aid of a scaling function. Detailed and rigorous stability proofs are given by making use of the improved finite time stability criterion. Two simulation examples are provided to exhibit the validity of the designed adaptive ETC approach.Cardiovascular disease (CVD) threatens the lives of many and affects their productivity. Wearable sensors can enable continuous monitoring of hemodynamic parameters to improve the diagnosis and management of CVD. Bio-Impedance (Bio-Z) is an effective non-invasive sensor for arterial pulse wave monitoring based on blood volume changes in the artery due to the deep penetration of its current signal inside the tissue. However, the measured data are significantly affected by the placement of electrodes relative to the artery and the electrode configuration. In this work, we created a Bio-Z simulation platform that models the tissue, arterial pulse wave, and Bio-Z sensing configuration using a 3D circuit model based on a time-varying impedance grid. A new method is proposed to accurately simulate the different tissue types such as blood, fat, muscles, and bones in a 3D circuit model in addition to the pulsatile activity of the arteries through a variable impedance model. This circuit model is simulated in SPICE and can be used to guide design decisions (i.e. electrode placement relative to the artery and electrode configuration) to optimize the monitoring of pulse wave prior to experimentation. We present extensive simulations of the arterial pulse waveform for different sensor locations, electrode sizes, current injection frequencies, and artery depths. These simulations are validated by experimental Bio-Z measurements.Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompleteness of the network and the ignorance of a large amount of information about physical and functional interactions between biological components. Other methods that directly integrate multiple types of interactions into an aggregate network have the risks that different types of data may conflict with each other and the characteristics and topologies of each individual network are lost. Meanwhile, biomarkers used in clinical trials should have the characteristics of small quantity and strong discriminate ability. In this study, we developed a multiplex network-based dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for diagnosis, prognosis, and classification. The results showed that DualRank outperformed competing methods and could identify biomarkers with the small quantity, great prediction performance (average AUC=0.818) and biological interpretability.Precise cancer subtype and/or stage prediction is instrumental for cancer diagnosis, treatment and management. However, most of the existing methods based on genomic profiles suffer from issues such as overfitting, high computational complexity and selected features (i.e., genes) not directly related to forecast precision. These deficiencies are largely due to the nature of "high-dimensionality-small-sample (HDSS)" inherent in molecular data, and such a nature is often deemed as an obstacle to the application of deep learning to biomedical research. In this paper, we propose a DNN-based algorithm coupled with a new embedded feature selection technique, named Dropfeature-DNNs, to address these issues. We formulate Dropfeature-DNNs as an iterative AUC optimization problem when training DNNs. As such, an "optimal" feature subset that contains meaningful genes for patient stratification can be obtained when the AUC optimization converges. Since the feature subset and AUC optimizations are synchronous with the training of DNNs, model complexity and computational cost are simultaneously reduced. Rigorous feature subset convergence analysis and error bound inference provide a solid theoretical foundation for the proposed method. Extensive empirical comparisons to benchmark methods further demonstrate the efficacy of Dropfeature-DNNs in cancer subtype and/or stage prediction using HDSS gene expression data from multiple cancer types.DNA strand displacement is introduced in this study and used to construct an analog DNA strand displacement chaotic system based on six reaction modules in nanoscale size. The DNA strand displacement circuit is employed in encryption as a chaotic generator to produce chaotic sequences. In the encryption algorithm, we convert chaotic sequences to binary ones by comparing the concentration of signal DNA strand. Simulation results show that the encryption scheme is sensitive to the keys, and key space is large enough to resist the brute-force attacks, furthermore algorithm has a high capacity to resist statistic attack. Based on robustness analysis, our proposed encryption scheme is robust to the DNA strand displacement reaction rate control, noise and concentration detection to a certain extent.Customized static orthoses in rehabilitation clinics often cause side effects, such as discomfort and skin damage due to excessive local contact pressure. Currently, clinicians adjust orthoses to reduce high contact pressure based on subjective feedback from patients. However, the adjustment is inefficient and prone to variability due to the unknown contact pressure distribution as well as differences in discomfort due to pressure across patients. This paper proposed a new method to predict a threshold of contact pressure (pressure limit) associated with moderate discomfort at each critical spot under hand orthoses. A new pressure sensor skin with 13 sensing units was configured from FEA results of pressure distribution simulated with hand geometry data of six healthy participants. It was used to measure contact pressure under two types of customized orthoses for 40 patients with bone fractures. Their subjective perception of discomfort was also measured using a 6 scores discomfort scale. Based on these data, five critical spots were identified that correspond to high discomfort scores (>1) or high pressure magnitudes (>0.