Research on Efficacy Evaluation of Large-Scale Networked Intelligent Perception System

Under the smart engineering system (SES), there is a huge demand for evaluating the efficacy of a large-scale networked intelligent perception system (IPS). Considering the large-scale, distributed, and networked system characteristics and perception task demands, this paper proposes a conceptual system for IPS efficacy evaluation and, on this basis, designs the architecture of the efficacy evaluation system. A networked IPS model is constructed based on domain ontology, an index system is quickly established for efficacy evaluation, the evaluation methods are assembled automatically, and adaptive real-time organization strategies are generated for networked perception based on efficacy estimate. After exploring these key technologies, a prototype system is created for the service-oriented integrated efficacy evaluation platform and used to verify and integrate research results. The research provides support for the efficacy evaluation theories and methods of large-scale networked IPS.

Active Binaural Auditory Perceptual System for a Socially Interactive Humanoid Robot

Sound Source Localization (SSL) and gaze shift to the sound source behavior is an integral part of a socially interactive humanoid robot perception system. In noisy and reverberant environments, it is non-trivial to estimate the location of a sound source and accurately shift gaze in its direction. Previous SSL algorithms are deficient in the optimum approximation of distance to audio sources and to accurately detect, interpret, and differentiate the actual sound from comparable sound sources due to challenging acoustic environments. In this article, a learning-based model is presented to achieve noiseless and reverberation-resistant sound source localization in the real-world scenarios. The proposed system utilizes a multi-layered Gaussian Cross-Correlation with Phase Transform (GCC-PHAT) signal processing technique as a baseline for a Generalized Cross Correlation Convolution Neural Network (GCC-CNN) model. The proposed model is integrated with an efficient rotation algorithm to predict and orient toward the sound source. The performance of the proposed method is compared with the state-of-art deep network-based sound source localization methods. The findings of the proposed method outperform the existing neural network-based approaches by achieving the highest accuracy of 96.21% for an active binaural auditory perceptual system.

Evaluation and of University Building Design Effect Based on Multisensor Perception and Data Security

The development of the smart cities with new and integrated information and communication technologies has changed the traditional industries’ processes. One of the domains is construction industry which plays an important supporting role for the economic development of a country, but at the same time, the construction industry is also an industry with high energy consumption and high pollution. Therefore, in order to alleviate the contradiction between economic development and resources and the environment, the construction industry must achieve sustainable development and take the road of green construction. In order to carry out the evaluation of the design effect of colleges and universities, this paper introduces the multisensor perception and fuzzy comprehensive evaluation methods. First, through the design and analysis of the sensor perception system used in the building environment, the collection, acquisition, analysis, and processing of complex information of heterogeneous multiterminals are obtained. Secondly, cluster analysis and genetic algorithms are used in the processing and analysis process of building multiterminal sensor data. The security aspect is also taking into account to design the methods. The system test verifies the performance of the university building design effect evaluation model, which can provide a reference for the sustainable development of the construction industry.

Light-induced perovskite dynamic transformation enabling photodetector mimicking neuromorphic vision sensing system

As an artificial perception system, neuromorphic vision sensing system can imitate the complex image sensing and processing functions of the human visual neural network. In order to stimulate the nervous...

Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

Dissecting the membrane-microtubule sensor in grapevine defence

Specific populations of plant microtubules cooperate with the plasma membrane to sense and process abiotic stress signals, such as cold stress. The current study derived from the question, to what extent this perception system is active in biotic stress signalling. The experimental system consisted of grapevine cell lines, where microtubules or actin filaments are visualised by GFP, such that their response became visible in vivo. We used the bacterial elicitors harpin (inducing cell-death related defence), or flg22 (inducing basal immunity) in combination with modulators of membrane fluidity, or microtubules. We show that DMSO, a membrane rigidifier, can cause microtubule bundling and trigger defence responses, including activation of phytoalexin transcripts. However, DMSO inhibited the gene expression in response to harpin, while promoting the gene expression in response to flg22. Treatment with DMSO also rendered microtubules more persistent to harpin. Paradoxically, Benzylalcohol (BA), a membrane fluidiser, acted in the same way as DMSO. Neither GdCl3, nor diphenylene iodonium were able to block the inhibitory effect of membrane rigidification on harpin-induced gene expression. Treatment with taxol stabilised microtubule against harpin but amplified the response of PAL transcripts. Therefore, the data support implications of a model that deploys specific responses to pathogen-derived signals.

Survey on Deep Learning-Based Marine Object Detection

We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

Visual Robotic Perception System with Incremental Learning for Child–Robot Interaction Scenarios

This paper proposes a novel lightweight visual perception system with Incremental Learning (IL), tailored to child–robot interaction scenarios. Specifically, this encompasses both an action and emotion recognition module, with the former wrapped around an IL system, allowing novel actions to be easily added. This IL system enables the tutor aspiring to use robotic agents in interaction scenarios to further customize the system according to children’s needs. We perform extensive evaluations of the developed modules, achieving state-of-the-art results on both the children’s action BabyRobot dataset and the children’s emotion EmoReact dataset. Finally, we demonstrate the robustness and effectiveness of the IL system for action recognition by conducting a thorough experimental analysis for various conditions and parameters.

Research on Comparison of LiDAR and Camera in Autonomous Driving

With the improvement of vehicles automation, autonomous vehicles become one of the research hotspots. Key technologies of autonomous vehicles mainly include perception, decision-making, and control. Among them, the environmental perception system, which can convert the physical world’s information collection into digital signals, is the basis of the hardware architecture of autonomous vehicles. At present, there are two major schools in the field of environmental perception: camera which is dominated by computer vision and LiDAR. This paper analyzes and compares the two majors schools in the field of environmental perception and concludes that multi-sensor fusion is the solution for future autonomous driving.