Recent advances in mobile devices have made profound changes in people's daily lives. In particular, the impact of easy access of information by the smartphone has been tremendous. However, the impact of mobile devices on healthcare has been limited. Diagnosis and treatment of diseases are still initiated by occurrences of symptoms, and technologies and devices that emphasize on disease prevention and early detection outside hospitals are under-developed. Besides healthcare, mobile devices have not yet been designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such blindness. In this paper, an overview of our research on a new wearable computer called eButton is presented. The concepts of its design and electronic implementation are described. Several applications of the eButton are described, including evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suffering from dementia.

IEEE 802.16m resource scheduling remains a challenging issue for video multicast. Given the OFDMA frame structure, the IEEE 802.16m scheduling task is comprised of: (1) intraframe scheduling; and (2) interframe scheduling. In the literature, while many studies concentrated on the development of intraframe scheduling mechanisms, few studies looked at the potential of interframe scheduling mechanisms. This paper is the first attempt to investigate the energy efficiency potential of interframe scheduling algorithms to support scalable-video multicast services over IEEE 802.16m networks. Under the premise that the bandwidth requirements of scalable-video subscribers must be satisfied, we first prove that the interframe scheduling problem of minimizing video-subscriber energy consumption is NP-hard. To tackle the NP-hard problem, we propose a multiple bin-packing algorithm, MBPA, for energy-efficient scheduling. The proposed MBPA has full compatibility with the existing intraframe scheduling mechanisms and with the IEEE 802.16m sleep-mode operations. By applying the divide-and-conquer strategy, MBPA effectively eliminates unnecessary wake-up periods and unnecessary state transitions (between wake-up and sleep states), and thus achieves high energy-efficiency. Through theoretical analysis, we show that MBPA is a p-approximation algorithm, where p is a finite value no less than one. Finally, the simulation results show the effectiveness of the proposed MBPA in energy efficiency, user satisfaction, and computational complexity.

In this paper, we propose a constraint graph-based macro placement algorithm that removes macro overlaps and optimizes macro positions for modern mixed-size circuit designs. Improving over the constraint graph by working only on its essential edges without loss of the solution quality, our algorithm can search for high-quality macro placement solutions effectively and efficiently. Instead of packing macros along chip boundaries like most recent previous work, our placer can determine a non-compacted macro placement by linear programming and placement region cost evaluation and handle various placement constraints/objectives. Compared with various leading academic macro placers, our algorithm can consistently and significantly reduce the wirelengths for designs with different utilization rates, implying that our macro placer is robust and has very high quality.

In addition to wirelength, modern placers need to consider various constraints such as preplaced blocks and density. We propose a high-quality analytical placement algorithm considering wirelength, preplaced blocks, and density based on the log-sum-exp wirelength model proposed by Naylor and the multilevel framework. To handle preplaced blocks, we use a two-stage smoothing technique, i.e., Gaussian smoothing followed by level smoothing, to facilitate block spreading during global placement (GP). The density is controlled by white-space reallocation using partitioning and cut-line shifting during GP and cell sliding during detailed placement. We further use the conjugate gradient method with dynamic step-size control to speed up the GP and macro shifting to find better macro positions. Experimental results show that our placer obtains very high-quality results.

In addition to wirelength, modern placers need to consider various constraints such as preplaced blocks and density. We propose a high-quality analytical placement algorithm considering wirelength, preplaced blocks, and density based on the log-sum-exp wirelength model proposed by Naylor et al. [20] and the multilevel framework. To handle preplaced blocks, we use a two-stage smoothing technique, Gaussian smoothing followed by level smoothing, to facilitate block spreading during global placement. The density is controlled by white-space re-allocation using partitioning and cut-line shifting during global placement and cell sliding during detailed placement. We further use the conjugate gradient method with dynamic step-size control to speed up the global placement and macro shifting to and better macro positions. Experimental results show that our placer obtains the best published results.

In this paper, we present a hybrid placer, called NTUplace2, which integrates both the partitioning and the analytical (quadratic programming) placement techniques for large-scale mixed-size designs. Unlike most existing placers that minimize wirelength alone, we also control the cell density to optimize routability while minimizing the total wirelength. NTUplace2 consists of three major stages: multilevel global placement, legalization, and detailed placement. To handle mixed-size designs, in particular, we present a linear programming based legalization algorithm to remove overlaps between macros during global placement. Various other techniques are integrated to improve the solution quality at every stage.