The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Background
Femoral morphological studies and parameter measurements play a crucial role in diagnosing hip joint disease, preoperative planning for total hip arthroplasty, and prosthesis design. Doctors usually perform parameter measurements manually in clinical practice, but it is time‐consuming and labor‐intensive. Moreover, the results rely heavily on the doctor's experience, and the repeatability...
Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks. There is a timely need to map the latest software DCNNs to application-specific hardware, in order to achieve orders of magnitude improvement in performance, energy efficiency and compactness. Stochastic Computing (SC),...
Deep Convolutional Neural Networks (DCNN), a branch of Deep Neural Networks which use the deep graph with multiple processing layers, enables the convolutional model to finely abstract the high-level features behind an image. Large-scale applications using DCNN mainly operate in high-performance server clusters, GPUs or FPGA clusters; it is restricted to extend the applications onto mobile/wearable...
Deep Learning, as an important branch of machine learning and neural network, is playing an increasingly important role in a number of fields like computer vision, natural language processing, etc. However, large-scale deep learning systems mainly operate in high-performance server clusters, thus restricting the application extensions to personal or mobile devices. The solution proposed in this paper...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.