Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge

George, A. Shaji; Ravindran, Arun; Mendieta, Matías; Tabkhi, Hamed

doi:10.1109/access.2021.3055775

Cited by 55 publications

(29 citation statements)

References 27 publications

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“…By utilizing machines’ decision-making abilities, it is possible to abstract the results from a large dataset with little effort. A wide variety of machine learning applications can be found in different domains, such as the Internet of Things (IoT) [ 39 ], health care [ 40 ], machine vision [ 41 ], edge computing [ 42 ], security [ 43 , 44 ], and many others.…”

Section: Methodsmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

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Due to the tremendous expectations placed on batteries to produce a reliable and secure product, fault detection has become a critical part of the manufacturing process. Manually, it takes much labor and effort to test each battery individually for manufacturing faults including burning, welding that is too high, missing welds, shifting, welding holes, and so forth. Additionally, manual battery fault detection takes too much time and is extremely expensive. We solved this issue by using image processing and machine learning techniques to automatically detect faults in the battery manufacturing process. Our approach will reduce the need for human intervention, save time, and be easy to implement. A CMOS camera was used to collect a large number of images belonging to eight common battery manufacturing faults. The welding area of the batteries’ positive and negative terminals was captured from different distances, between 40 and 50 cm. Before deploying the learning models, first, we used the CNN for feature extraction from the image data. To over-sample the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE) since the dataset was highly imbalanced, resulting in over-fitting of the learning model. Several machine learning and deep learning models were deployed on the CNN-extracted features and over-sampled data. Random forest achieved a significant 84% accuracy with our proposed approach. Additionally, we applied K-fold cross-validation with the proposed approach to validate the significance of the approach, and the logistic regression achieved an 81.897% mean accuracy score and a +/− 0.0255 standard deviation.

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Section: Methodsmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

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“…Machine learning models can be used to solve different domain problems such as text analysis [ 28 ], computer vision application [ 29 , 30 ], IoT [ 31 , 32 ], and image processing [ 33 , 34 ], etc. In this study, we used machine learning models for WBC image classification.…”

Section: Materials and Methodsmentioning

confidence: 99%

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

et al. 2022

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White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.

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“…Although progress has been made in this area, the current state-of-art still results in significant latency, specifically when dealing with high dimensional input data (e.g., image, time series). For example, a constrained model architecture can process between 5 to 15 frames per second (fps) with an image resolution set to 1920 × 1080 [260]. However, processing 5 to 15 frames per second is relatively lower than the fps at which videos are captured (typically 24 fps higher).…”

Section: ) Low Latencymentioning

confidence: 99%

Enabling All In-Edge Deep Learning: A Literature Review

et al. 2023

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In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition, image processing, and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers (a mesh of computing devices near end devices). Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). International Electrotechnical Commission (IEC) defines EI as the concept where the data is acquired, stored, and processed utilizing edge computing with DL and advanced networking capabilities. Broadly, EI has six levels of categories based on where the training and inference of DL take place, e.g., cloud server, edge server and end devices. This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important such as in mission-critical applications (e.g., health care). Besides, 5G/6G networks are envisioned to use all in-edge. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism, data parallelism, and split learning, which facilitates DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented. INDEX TERMS Artificial intelligence, all in-edge, deep learning, distributed systems, decentralized systems, edge intelligence I. INTRODUCTION T HE global community is increasingly becoming a datadriven environment in which end devices are generating vast quantities of data outside of the traditional data centers. International Telecommunication Union anticipates that global internet traffic per month will reach 607 Exabytes (EB) in 2025 and 5016 EB in 2030 [1]. This enormous amount of data has a positive impact on artificial intelligence (AI) applications. In particular, deep learning (DL) rely on the availability of large quantities of data for its d...

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Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge

Cited by 55 publications

References 27 publications

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

Enabling All In-Edge Deep Learning: A Literature Review

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