Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diag...
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LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle varia...
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In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server w...
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In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server while a small lightweight MobileNet model is more suitable for the end-user device with fewer computation resources. However, the heterogeneity of the model architectures and configurations may bring a systemic problem -models may produce different outputs when given the same input. This inconsistency problem may cause severe system failure of prediction agreement inside the application. Current research has not studied the systemic design for efficiently detecting and reducing the inconsistency among models in distributed DL applications. With the increasing scale of distributed DL applications, the challenges of inconsistency mitigation should consider both algorithm and system design. To this end, we design and implement DEEPCON, an adaptive deployment system across the edge-cloud layer with over-the-air model updates. We implement ASRS sampling for efficiently sampling data to reveal the real data distribution as well as model prediction inconsistency. Then, we implement DMML-Par, an asynchronous parallel training algorithm for quickly updating the models and reducing inconsistency. implements over-the-air updates with a set of APIS to enable seamless inconsistency detection and reduction in such deep learning applications. Our experiment results on both vision and language tasks demonstrate that DMML could improve the model consistency up to 4%, 7%, and 13% at CIFAR10/100 and IMDB datasets without sacrificing the accuracy of individual models. We also show that the ASRS sampling can save 90% network bandwidth of data transmission and that DMML-Par is up to 60% faster compared to simple synchronous parallel training. 2015 IEEE.
Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature...
When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods ...
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Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper,...
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Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid qu...
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This paper systematically investigates the performance of consensus-based distributed filtering under mismatched noise covariances. First, we introduce three performance evaluation indices for such filtering problems,...
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Eye gestures have been acknowledged as a crucial real-time input channel for human-computer interaction, especially for people with disabilities who lack fine motor skills. However, existing eye-gesture-based interact...
Eye gestures have been acknowledged as a crucial real-time input channel for human-computer interaction, especially for people with disabilities who lack fine motor skills. However, existing eye-gesture-based interactions mainly rely on gaze duration, which can cause visual fatigue and is not user-friendly for those with disabilities. To address these issues, we propose acceptable interactive paths based on eye gestures in multiple contexts, such as reading and selection, to assist impaired individuals in interacting efficiently and naturally in their daily work. In the selection context, cursor movement is accomplished by fixation position, while single and double clicks of the left mouse button are executed through blink frequency. In the reading context, page scrolling is controlled by the vertical coordinates of the fixation position. In experiments, we set up reading and selection contexts to evaluate and compare the constructed interaction paths with the existing system. The experimental results demonstrate that the interaction paths proposed in this paper are efficient and natural.
As the power transmission network connects the power generation side and power consumption side, the transmission line plays a vital role in the overall stable operation of the power grid. However, because power trans...
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