Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application

Messaoud, Seifeddine; Bouaafia, Soulef; Amna, Maraoui; Ammari, Ahmed Chiheb; Khriji, Lazhar; Machhout, Mohsen

doi:10.1016/j.compeleceng.2021.107671

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…The correct coding should be K52. 910 Chronic colitis, of which Category K52 Other non-infectious gastroenteritis and colitis belongs to Chapter 11 Digestive system diseases [6] .…”

Section: Colitis Coding Error Analysismentioning

confidence: 99%

“…900 and E11.900 should be considered as type 1 and type 2 diabetes without complications, respectively. Therefore, when E10 or E11 category is present in the discharge diagnosis of a medical record and it is not the .9 suborder code, E10.900 or E11.900 code should not exist simultaneously [6][7][8] . If you use the pinyin acronym search method to find the code for "type 2 diabetes" in Guolin 2.0, it will match to E11.900.…”

Section: Diabetes Coding Error Analysismentioning

confidence: 99%

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Error analysis of disease coding in computer software system

Ling,

Liu,

Liu

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…The correct coding should be K52. 910 Chronic colitis, of which Category K52 Other non-infectious gastroenteritis and colitis belongs to Chapter 11 Digestive system diseases [6] .…”

Section: Colitis Coding Error Analysismentioning

confidence: 99%

Section: Diabetes Coding Error Analysismentioning

confidence: 99%

Error analysis of disease coding in computer software system

Ling,

Liu,

Liu

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…With the development of computer hardware and software, mobile devices, image processing, and machine learning [5][6][7][8][9], it is feasible and meaningful to design a fast and efficient method to automatically identify and classify plant species. Plant species can be classified and identified by different organs such as leaves, flowers, fruits, or whole plants [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Yuan¹,

Liu²,

Song³

et al. 2023

Phyton

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Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.

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“…Nevertheless, while developing these devices with limited resources, the requirements of small size, weight, low power consumption, and low cost (SWaP-C) are always sought. Physical constraints would continue and be increased by the demands of recent trends as technologies around the IoT edge expand rapidly and boost their potential, namely: (i) Data transfer over the Internet to specific online services in a standardized manner is enabled by connectivity and subsequent interoperability [6][7][8]; (ii) the need for higher intelligence at the network's edge, allowing systems to make choices faster while consuming less energy [9,10]; (iii) devices developed for security, mitigating risks from a large number of massive attack surfaces present in the IoT network [11,12]; and (iv) new energy-saving techniques, allowing autonomous and durable devices [13,14].…”

Section: Introductionmentioning

confidence: 99%

Future Internet of Things: Connecting the Unconnected World and Things Based on 5/6G Networks and Embedded Technologies

Messaoud¹,

Amdouni²,

Albouchi³

et al. 2023

Internet of Things - New Trends, Challenges and Hurdles

Self Cite

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Undeniably, the Internet of Things (IoT) ecosystem keeps on advancing at a fast speed, far above all predictions for growth and ubiquity. From sensor to cloud, this massive network continues to break technical limits in a variety of ways, and wireless sensor nodes are likely to become more prevalent as the number of Internet of Things devices increases into the trillions to connect the world and unconnected objects. However, their future in the IoT ecosystem remains uncertain, as various difficulties as with device connectivity, edge artificial intelligence (AI), security and privacy concerns, increased energy demands, the right technologies to use, and continue to attract opposite forces. This chapter provides a brief, forward-looking overview of recent trends, difficulties, and cutting-edge solutions for low-end IoT devices that use reconfigurable computing technologies like FPGA SoC and next-generation 5/6G networks. Tomorrow’s IoT devices will play a critical role. At the end of this chapter, an edge FPGA SoC computing-based IoT application is proposed, to be a novel edge computing for IoT solution with low power consumption and accelerated processing capability in data exchange.

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Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application

Cited by 17 publications

References 17 publications

Error analysis of disease coding in computer software system

Error analysis of disease coding in computer software system

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Future Internet of Things: Connecting the Unconnected World and Things Based on 5/6G Networks and Embedded Technologies

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