Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges ...
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During recent years, various hardware platforms were developed, each one suitable for use in different kind of applications. Platforms based on FPGAs, DSPs, GPUs, Single Board Computers, microcontrollers extend proces...
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ISBN:
(数字)9781665467179
ISBN:
(纸本)9781665467179
During recent years, various hardware platforms were developed, each one suitable for use in different kind of applications. Platforms based on FPGAs, DSPs, GPUs, Single Board Computers, microcontrollers extend processing capabilities and functionality in comparison with traditional personal computers based on a single CPU. Furthermore, co-design combines advantages from different types of processing units, rendering such architectures more attractive to researchers. In this paper, we achieve acceleration of image processingalgorithms using a hardware platform based on a Raspberry Pi Single Board Computer and a custom designed FPGA HAT (Hardware Attached on Top) for RPi. the FPGA HAT consists of a Cyclone 10LP device the FPGA undertakes a computationally demanding load such as robotic vision algorithms exploiting parallelism, while the RPi can apply higher level operations such as running ROS (Robot Operating System). In order to overcome bottleneck in exchanging data between RPi and FPGA, a 16-bit parallel customized protocol was developed from scratch. the achieved transfer rate was about 50 Mbytes/sec when multi threaded software was implemented for the RPi. An image edge detector was implemented in order to verify the system performance. When only the RPi was used the processing rate was 48fps for images with resolution 512x512 pixels. RPi and FPGA co-design achieved processing rate 170fps for the same resolution images, which means an acceleration of about 350%. the proposed system was also evaluated in terms of power consumption.
the article is devoted to the development of methods and architecture of optical color computing, techniques of transforming color information for textual representation and numerical calculation, including transmissi...
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this paper explores the problem of boundary data classification ambiguity that arises when machine learning techniques are applied in the field of intrusion detection. the features and attributes of the boundary data ...
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In stream processing, data arrives constantly and is often unpredictable. It can show large fluctuations in arrival frequency, size, complexity, and other factors. these fluctuations can strongly impact application la...
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ISBN:
(纸本)9781665469586
In stream processing, data arrives constantly and is often unpredictable. It can show large fluctuations in arrival frequency, size, complexity, and other factors. these fluctuations can strongly impact application latency and throughput, which are critical factors in this domain. therefore, there is a significant amount of research on self-adaptive techniques involving elasticity or micro-batching as a way to mitigate this impact. However, there is a lack of benchmarks and tools for helping researchers to investigate micro-batching and data stream frequency implications. In this paper, we extend a benchmarking framework to support dynamic micro-batching and data stream frequency management. We used it to create custom benchmarks and compare latency and throughput aspects from two different parallel libraries. We validate our solution through an extensive analysis of the impact of micro-batching and data stream frequency on stream processing applications using Intel TBB and FastFlow, which are two libraries that leverage stream parallelism on multi-core architectures. Our results demonstrated up to 33% throughput gain over latency using micro-batches. Additionally, while TBB ensures lower latency, FastFlow ensures higher throughput in the parallel applications for different data stream frequency configurations.
Multi-objective neural architecture search (NAS) algorithms aim to automatically search the neural architecture suitable for different computing power platforms by using multi-objective optimization methods. the LEMON...
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Withthe advancement of intelligent transportation systems, ship automatic identification system (AIS) data contains rich maritime traffic information. Utilizing this information effectively contributes to enhancing t...
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ISBN:
(数字)9798350376548
ISBN:
(纸本)9798350376555
Withthe advancement of intelligent transportation systems, ship automatic identification system (AIS) data contains rich maritime traffic information. Utilizing this information effectively contributes to enhancing the efficiency of ship trajectory prediction. Traditional deep learning trajectory prediction algorithms often focus on improving the accuracy of short-term prediction models while neglecting the thorough utilization of navigational features, thereby limiting the achievement of longer-term trajectory prediction. To address the current inability of ship trajectory prediction to perform long-term forecasts, this study proposes an improved QuickBundles clustering algorithm for extracting navigational features. Subsequently, this navigational information is integrated into a parallel LSTM-GRU prediction model to achieve longer-term ship trajectory prediction. Experiments are conducted using data from the Houston-Galveston Bay area obtained from open sources (https://***), and the results demonstrate that the PLG-IQB algorithm performs well in long-term trajectory prediction.
High fidelity simulations of unsteady fluid flow are now possible with advancements in high performance computing hardware and software frameworks. Since computational fluid dynamics (CFD) computations are dominated b...
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Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex s...
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this paper introduces an optimized approach that harnesses the power of transfer learning, specifically the ResNet34 architecture, to transcend the limitations of existing self-developed model in recognizing Chinese l...
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ISBN:
(纸本)9798400718137
this paper introduces an optimized approach that harnesses the power of transfer learning, specifically the ResNet34 architecture, to transcend the limitations of existing self-developed model in recognizing Chinese license plates. By integrating advanced digital image processing (DIP) techniques withthe robustness of the ResNet34 model, that not only refines the accuracy and efficiency of the automatic license plate recognition (ALPR) system but also underscores the pivotal role of DIP in enhancing feature extraction and image quality enhancement. this method leverages a suite of sophisticated, open-source DIP tools, namely the OpenCV framework and Python Imaging Library (PIL), complemented by the versatility of artificial neural networks (ANN). this synergy enables the concurrent generation of training, validation, and test datasets, thereby streamlining the model development pipeline. Innovating further, our deep learning training paradigm operates in parallel, managing data generation, processing, and training simultaneously. this approach not only refines the recognition capabilities of the ALPR system but also optimizes the utilization of computational resources. Fine-tuning the established parameters of the ResNet34 model alongside specific training adjustments has led to a remarkable improvement in accuracy, ranging from 3.12% to 12.50% across the seven characters of the license plate, outperforming our self-developed CNN baseline model. Despite an increase in the model’s parameter count from 9,023,463 to 22,919,879, the implementation efficiency has impressively increased by over 80%. the significance of this research lies in its potential to evolve traffic management, security monitoring, and parking systems by providing a more accurate, efficient, and robust solution for Chinese license plate recognition. the integration of DIP with deep learning models, as demonstrated in this study, sets a new resolution for future research and applications in the ALPR
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