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Aim and Objective: Lung nodule detection is critical in improving the five-year survival rate and reducing mortality for patients with lung cancer. Numerous methods based on convolutional neural networks (CNNs) have been proposed for lung nodule detection in computed tomography (CT) images. With the collaborative development of computer hardware technology, the detection accuracy and efficiency can still be improved. Materials and Methods: In this study, an automatic lung nodule detection method using CNNs with transfer learning is presented. We first compare three of the state-of-the-art convolutional neural network (CNN) models, namely, VGG16, VGG19 and ResNet50, to determine the most suitable model for lung nodule detection. We then utilize two different training strategies, namely, freezing layers and fine-tuning, to illustrate the effectiveness of transfer learning. Furthermore, the hyper-parameters of the CNN model such as optimizer, batch size and epoch are optimized. Results: Evaluated on the Lung Nodule Analysis 2016 (LUNA16) challenge, promising results with an accuracy of 96.86%, a precision of 91.10%, a sensitivity of 90.78%, a specificity of 98.13%, and an AUC of 99.37% are achieved. Conclusion: Compared with other works, state-of-the-art specificity is obtained, which demonstrates that the proposed method is effective and applicable to lung nodule detection.

In hardware-aware Differentiable Neural Architecture Search (DNAS), it is challenging to compute gradients of hardware metrics to perform architecture search. Existing works rely on linear approximations with limited support to customized hardware accelerators. In this work, we propose End-to-end Hardware-aware DNAS (EH-DNAS), a seamless integration of end-to-end hardware benchmarking, and fully automated DNAS to deliver hardware-efficient deep neural networks on various platforms, including Edge GPUs, Edge TPUs, Mobile CPUs, and customized accelerators. Given a desired hardware platform, we propose to learn a differentiable model predicting the end-to-end hardware performance of neural network architectures for DNAS. We also introduce E2E-Perf, an end-to-end hardware benchmarking tool for customized accelerators. Experiments on CIFAR10 [14] and ImageNet [22] show that EH-DNAS improves the hardware performance by an average of 1.4× on customized accelerators and 1.6× on existing hardware processors while maintaining the classification accuracy.

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Every day, a vast number of drivers are suffering from traffic jams around the world. The intelligent transport system can be used to reduce traffic jams. In this paper, we proposed a traffic jam early alert protocol (TEAP) which could propagate the alert information of traffic jam to drivers through the VANET as soon as possible. The early alert information could help the drivers avoid the jammed area. With the TEAP, the alert information including the position of the event would be transmitted immediately along the roads by unicast once a traffic jam happened while broadcast would be used when the message travelled by the crosses of roads so that all the vehicles possible drive into the spot of the accidents would get the information early. Besides, the oncoming vehicles would help disseminating the information. The simulations showed TEAP improved the packets successful delivery apart from rapid disseminating information.