This paper presents a novel CNN model called Soft Stagewise Regression Network (SSR-Net) for age estimation from a single image with a compact model size. Inspired by DEX, we address age estimation by performing multi-class classification and then turning classification results into regression by calculating the expected values. SSR-Net takes a coarse-to-fine strategy and performs multi-class classification with multiple stages. Each stage is only responsible for refining the decision of its previous stage for more accurate age estimation. Thus, each stage performs a task with few classes and requires few neurons, greatly reducing the model size. For addressing the quantization issue introduced by grouping ages into classes, SSR-Net assigns a dynamic range to each age class by allowing it to be shifted and scaled according to the input face image. Both the multi-stage strategy and the dynamic range are incorporated into the formulation of soft stagewise regression. A novel network architecture is proposed for carrying out soft stagewise regression. The resultant SSR-Net model is very compact and takes only 0.32 MB. Despite its compact size, SSR-Net’s performance approaches those of the state-of-the-art methods whose model sizes are often more than 1500× larger.
With the emergence of many-core systems, managing blocking costs effectively will soon become a critical issue in the design of real-time systems. In contrast to previous works on multicore real-time task scheduling algorithms and synchronization protocols, this paper proposes a dedicated-core framework to separate the executions of application tasks and (system) services over cores such that blocking among tasks can be better explored and managed. The rationale behind the framework is that we can exploit the characteristics of many-core systems to resolve the challenges raised by the systems themselves. We define three core minimization problems with respect to the constraints on core configurations, and present corresponding task allocation algorithms with optimal, approximate, and heuristic solutions. The results of simulations conducted to evaluate the proposed framework provide further insights into task scheduling in many-core real-time systems.
Wireless charging technology is considered as one of the promising solutions to solve the energy limitation problem for large-scale wireless sensor networks. Obviously, charger deployment is a critical issue since the number of chargers would be limited by the network construction budget, which makes the full-coverage deployment of chargers infeasible. In many of the applications targeted by large-scale wireless sensor networks, end-devices are usually equipped by the human and their movement follows some degree of regularity. Therefore in this paper, we utilize this property to deploy chargers with partial coverage, with an objective to maximize the survival rate of end-devices. We prove this problem is N P-hard, and propose an algorithm to tackle it. The simulation results show that our proposed algorithm can significantly increase the survival rate of end-devices. To our knowledge, this is one of very first works that consider charger deployment with partial coverage in wireless rechargeable sensor networks.
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