This paper presents a novel approach for extracting foreground objects from an image. Existing methods involve separating the foreground and background mainly according to their color distributions and neighbor similarities. This paper proposes using a more discriminative strategy, structural inconsistency analysis, in which the localities of color and texture are considered. Given an indicated rectangle, the proposed system iteratively maximizes the consensus regions between the original image and predicted structures from the known background. The object contour can then be extracted according to inconsistency in the predicted background and foreground structures. The proposed method includes an efficient image completion technique for structural prediction. The results of experiments showed that the extraction accuracy of the proposed method is higher than that of related methods for structural scenes, and is also comparable to that of related methods for less structural situations.

Energy-efficient task scheduling is a fundamental issue in many application domains, such as energy conservation for mobile devices and the operation of green computing data centers. Modern processors support dynamic voltage and frequency scaling (DVFS) on a per-core basis, i.e., the CPU can adjust the voltage or frequency of each core. As a result, the core in a processor may have different computing power and energy consumption. To conserve energy in multi-core platforms, we propose task scheduling algorithms that leverage per-core DVFS and achieve a balance between performance and energy consumption. We consider two task execution modes: the batch mode, which runs jobs in batches, and the online mode in which jobs with different time constraints, arrival times, and computation workloads co-exist in the system. For tasks executed in the batch mode, we propose an algorithm that finds the optimal scheduling policy, and for the online mode, we present a heuristic algorithm that determines the execution order and processing speed of tasks in an online fashion. The heuristic ensures that the total cost is minimal for every time interval during a task's execution.

Most wireless sensor networks consist of a large number of static, low-power, short-lived, and unreliable sensors. In this paper, we considered sensor networks consisting of both static and mobile nodes. Integrating both types of devices enables new applications, such as nodes replacement, hole and partition recovery, and autonomous deployment and redeployment. We designed a smart mobile robot and implemented an application of nodes replacement to demonstrate its use, via our nodes replacement algorithm. In this algorithm, the mobile robots can navigate towards low-energy sensor nodes and replace them automatically, with new sensor nodes, having no location information. The navigation algorithm is based on received signal strength between the mobile robot and the communicating node. The experimental results confirm that the mobile robots successfully achieved their assigned tasks.