The sensor system is crucial to intelligent munitions and must function with incredibly high accuracy and in real time based on the characteristics of the munition. Additionally, it is difficult to meet the requiremen...
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Optimal energy consumption in Wireless sensornetworks (WSNs) is important. Previous research has shown that by organizing network nodes into a number of clusters, one can achieve greater energy efficiency leading to ...
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With the rapid development of new power systems and digital construction of power grids, a large number of power Internet of Things terminals need to be deployed to collect equipment status information in real time. I...
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To save the energy and extend the lifetime of the network, Cluster Routing Algorithm based on Agglomerative Hierarchical Clustering (CRAAHC) is proposed in wireless sensornetworks. WSN is divided into a certain quant...
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intelligent Transport Systems (ITS) are driving innovation in road transport by integrating advances in communication and information systems with traditional engineering practices. Core to ITS development is the coll...
intelligent Transport Systems (ITS) are driving innovation in road transport by integrating advances in communication and information systems with traditional engineering practices. Core to ITS development is the collection and analysis of traffic data. One class of traffic sensors, known as Automatic Vehicle Identification, is characterised by the ability to identify vehicles using unique identifiers. Through vehicle re-identification, such sensors can provide reliable estimates of travel time and inform route choices, both at the individual and aggregate levels, across all levels of the road hierarchy. In particular, Automatic Number Plate Recognition (ANPR) video cameras require just a visible number plate instead of specialised devices for vehicle detection. The benefits of ANPR technology for traffic monitoring have led to its adoption in cities across the world, forming complex sensornetworks with increased potential to power ITS solutions. Despite successful application in traffic forecasting, two technical barriers prevent a more widespread and diverse adoption of ANPR networks: • The lack of technical guidance on pre-processing ANPR data. We address this by developing a data pipeline which documents the various data sources and processing steps required to produce traffic data ready for analysis. In addition, we benchmark the pipeline against a real ANPR network, located in the North East of England. • The methodological gap in representing and extracting popular travel routes (corridors) from observed data. We develop a mathematical framework for corridor identification, which highlights route importance in connecting and distributing regional road traffic. The second part of this thesis focuses on two new ITS applications of ANPR networks. They demonstrate how traffic authorities can collect evidence of corridor performance and safety issues in order to prioritise transport improvements: • Bottleneck detection and impact assessment is a critical traffic
Edge intelligent applications, such as autonomous driving usually deploy multiple inference models on resource-constrained edge devices to execute a diverse range of concurrent tasks, given large amounts of input data...
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ISBN:
(纸本)9798400701184
Edge intelligent applications, such as autonomous driving usually deploy multiple inference models on resource-constrained edge devices to execute a diverse range of concurrent tasks, given large amounts of input data. One challenge is that these tasks need to produce reliable inference results simultaneously with millisecond-level latency to achieve real-time performance and high quality of service (QoS). However, most of the existing deep learning frameworks only focus on optimizing a single inference model on an edge device. To accelerate multi-model inference on a resource-constrained edge device, in this paper we propose POS, a novel operator-level scheduling framework that combines four operator scheduling strategies. The key to POS is a maximum entropy reinforcement learning-based operator scheduling algorithm MEOS, which generates an optimal schedule automatically. Extensive experiments show that POS outperforms five state-of-the-art inference frameworks: TensorFlow, PyTorch, TensorRT, TVM, and IOS, by up to 1.2x similar to 3.9x inference speedup consistently, with 40% improvement on GPU utilization. Meanwhile, MEOS reduces the scheduling overhead by 37% on average, compared to five baseline methods including sequential execution, dynamic programming, greedy scheduling, actor-critic, and coordinate descent search algorithms.
Six quantitative indicators of asymmetry of human gait, whose values can be estimated by processing data acquired using depth sensors, are considered. Procedures for the estimation of these indicators have been develo...
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Healthcare wireless sensornetworks (WSNs) have made their way into a wide range of applications and technologies with significantly different requirements and features in recent years. The integration of sensor nodes...
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Healthcare wireless sensornetworks (WSNs) have made their way into a wide range of applications and technologies with significantly different requirements and features in recent years. The integration of sensor nodes into intelligent sensing, informationprocessing and information exchange infrastructures form healthcare WSNs will have a significant impact on a variety of applications, including telemedicine, habitat monitoring, structure health monitoring, human-centric applications and medical applications, among others. Monitoring, identification of events and responding to an event which requires a continuous access to real-time information either partial or fully distributed environment is a challenging issue. In order to overcome the challenges of healthcare wireless sensornetworks (H-WSN), fuzzy-based clustering provides a cost-effective and efficient solution. Most of the problems in WSN are real time based that require fast computation, real-time optimal solution and need to be adaptive to the situation of the events and data traffic to achieve desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in sensornetworks. A real-time event detection is simulated and implemented on Crossbow mote (sensor node) using TinyOS.
The development of digital society generates mass data coming from a huge number of sensors, arising the demand for high spectrum efficiency and energy efficiency related technologies. With passive reflection, the int...
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ISBN:
(纸本)9781665450850
The development of digital society generates mass data coming from a huge number of sensors, arising the demand for high spectrum efficiency and energy efficiency related technologies. With passive reflection, the integrated sensing and communication (ISAC) can modulate the sensorinformation into the communication information and decode them in a cooperative manner at the integrated receiver, offering the potential for spectrum efficiency and energy efficiency enhancement. However, the power of sensorinformation is much lower than that of communication information, resulting in the bit rate downgrading or bit error rate (BER) increasing. In this paper, we develop the dual quadrature phase reflection modulation (DQP-RM) scheme along with its transmission framework to not only increase the sensorinformation symbol rate, but also decrease the BER of ISAC. Simulation results verify that DQP-RM theme can decrease the BER of sensorinformation compared with existing schemes and keep the BER of communication information unchanged while making the symbol rate of sensorinformation reach that of communication information.
Fire detection systems are considered an integral part of any building. However, most fire detection systems use a single passive sensor that usually faces some unavoidable problems, especially with the use of simple ...
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