Running is one of the most popular sports with hundreds of millions of participants worldwide. Good running form is the key to fast, efficient, and injury-free running. Existing kinematic analysis technologies, such as high-speed camera systems, are expensive, difficult to operate, and exclusive to sports physiology laboratories and elite athletes. Miniature MEMS-based motion sensors enable portable high-precision kinematic analysis, but suffer from high energy consumption hence short battery lifetime, especially for continued online analysis for running. This paper presents Gazelle, a wearable online analysis system for running that is compact, lightweight, accurate, and highly energy efficient; intended for runners of all levels. To enable long-term maintenance-free mobile analysis for running, Sparse Adaptive Sensing (SAS) is proposed, which selectively identifies the best sampling points to maintain high accuracy while greatly reducing sensing and analysis energy overheads. Experimental results demonstrate 97.7 percent accuracy with 76.9 to 99 percent reduced energy consumption (83.6 percent average reduction under real-world testing)-a one-order-of-magnitude improvement over existing solutions. SAS enables > 200 days of continuous high-precision operation using only a coin-cell battery. Since 2014, Gazelle has been used by over 100 elite and recreational runners during daily training and at top-level races like the Kona Ironman World Championships and New York Marathon.

Wearables are a leading category in the Internet of Things. Compared with mainstream mobile phones, wearables target one order of magnitude form factor reduction, and offer the potential of providing ubiquitous, personalized services to end users. Aggressive reduction in size imposes serious limits on battery capacity. Wearables are equipped with a range of sensors, such as accelerometers and gyroscopes. Most economical sensors were developed for mobile phones, with energy consumptions more appropriate for phones than for ultra-compact wearables. This article describes the energy challenges for wearable sensing technologies, with a primary focus on the most widely used wearable sensors: MEMS-based inertial measurement units. Using sports and fitness wearables as the pilot application, we analyze the energy characteristics of MEMS IMU data sensing, analysis, and wireless communication. We then discuss the technologies needed to solve the power and energy consumptions challenges for wearables.

Augmented reality (AR) applications have recently become popular on modern smartphones. We explore the effectiveness of this mobile AR technology in the context of grocery shopping, in particular as a means to assist shoppers in making healthier decisions as they decide which grocery products to buy. We construct an AR-assisted mobile grocery-shopping application that makes real-time, customized recommendations of healthy products to users and also highlights products to avoid for various types of health concerns, such as allergies to milk or nut products, low-sodium or low-fat diets, and general caloric intake. We have implemented a prototype of this AR-assisted mobile grocery shopping application and evaluated its effectiveness in grocery store aisles. Our application's evaluation with typical grocery shoppers demonstrates that AR overlay tagging of products reduces the search time to find healthy food items, and that coloring the tags helps to improve the user's ability to quickly and easily identify recommended products, as well as products to avoid. We have evaluated our application's functionality by analyzing the data we collected from 15 in-person actual grocery-shopping subjects and 104 online application survey participants.

Integrated circuit (IC) design automation has traditionally followed a hierarchical approach. Modern IC design flow is divided into sequentially-addressed design and optimization layers; each successively finer in design detail and data granularity while increasing in computational complexity. Eventual agreement across the design layers signals design closure. Obtaining design closure is a continual problem, as lack of awareness and interaction between layers often results in multiple design flow iterations. In this work, we propose parallel cross-layer optimization, in which the boundaries between design layers are broken, allowing for a more informed and efficient exploration of the design space. We leverage the heterogeneous parallel computational power in current and upcoming multi-core/many-core computation platforms to suite the heterogeneous characteristics of multiple design layers. Specifically, we unify the highlevel and physical synthesis design layers for parallel cross-layer IC design optimization. In addition, we introduce a massively-parallel GPU floorplanner with local and global convergence test as the proposed physical synthesis design layer. Our results show average performance gains of 11X speed-up over state-of-the-art.

Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the predominant neural network architecture has been the feedforward multilayer perceptron known as backpropagation. Adaptive resonance theory (ART) neural networks offer advantages to the classification of optical remote sensing data for vegetation and land cover mapping. A significant advantage is that it does not require prior specification of the neural net structure, creating as many internal nodes as are needed to represent the calibration (training) data. The Gaussian ARTMAP classification algorithm bases the probability that input training samples belong to specific classes on the parameters of its Gaussian distributions: the means, standard deviations, and a priori probabilities. The performance of the Gaussian ARTMAP classification algorithm in terms of classification accuracy using independent validation data indicated was over 70% accurate when applied to an annual series of monthly 1-km advanced very high resolution radiometer (AVHRR) satellite normalized difference vegetation index (NDVI) data. The accuracies were comparable to those of fuzzy ARTMAP and a univariate decision tree, and significantly higher than a Bayesian classification algorithm. Algorithm testing is based on calibration and validation data developed and applied to Central America to map the International Geosphere-Biosphere Programme (IGBP) land cover classification system.