Mousa, Mustafa; Claudel, Christian G.(Lecture Notes in Electrical Engineering, Springer Science + Business Media, 2014-02-24)[Conference Paper]
The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.
Atmosukarto, Indriyati; Ghanem, Bernard; Nasef Saadalla, Mohamed Magdy Mohamed; Ahuja, Narendra(Advances in Computer Vision and Pattern Recognition, Springer Science + Business Media, 2014)[Article]
Most existing software packages for sports video analysis require manual annotation of important events in the video. Despite being the most popular sport in the United States, most American football game analysis is still done manually. Line of scrimmage and offensive team formation recognition are two statistics that must be tagged by American Football coaches when watching and evaluating past play video clips, a process which takesmanyman hours per week. These two statistics are the building blocks for more high-level analysis such as play strategy inference and automatic statistic generation. In this chapter, we propose a novel framework where given an American football play clip, we automatically identify the video frame in which the offensive team lines in formation (formation frame), the line of scrimmage for that play, and the type of player formation the offensive team takes on. The proposed framework achieves 95% accuracy in detecting the formation frame, 98% accuracy in detecting the line of scrimmage, and up to 67%accuracy in classifying the offensive team’s formation. To validate our framework, we compiled a large dataset comprising more than 800 play-clips of standard and high definition resolution from real-world football games. This dataset will be made publicly available for future comparison.
Li, Fuquan; Kodzius, Rimantas; Gooneratne, Chinthaka Pasan; Foulds, Ian G.; Kosel, Jürgen(Microchimica Acta, Springer Science + Business Media, 2014-03-29)[Article]
We demonstrate a magnetic microsystem capable of detecting nucleic acids via the size difference between bare magnetic beads and bead compounds. The bead compounds are formed through linking nonmagnetic beads and magnetic beads by the target nucleic acids. The system comprises a tunnel magneto-resistive (TMR) sensor, a trapping well, and a bead-concentrator. The TMR sensor detects the stray field of magnetic beads inside the trapping well, while the sensor output depends on the number of beads. The size of the bead compounds is larger than that of bare magnetic beads, and fewer magnetic beads are required to fill the trapping well. The bead-concentrator, in turn, is capable of filling the trap in a controlled fashion and so to shorten the assay time. The bead-concentrator includes conducting loops surrounding the trapping well and a conducting line underneath. The central conducting line serves to attract magnetic beads in the trapping well and provides a magnetic field to magnetize them so to make them detectable by the TMR sensor. This system excels by its simplicity in that the DNA is incubated with magnetic and nonmagnetic beads, and the solution is then applied to the chip and analyzed in a single step. In current experiments, a signal-to-noise ratio of 40.3 dB was obtained for a solution containing 20.8 nM of DNA. The sensitivity and applicability of this method can be controlled by the size or concentration of the nonmagnetic bead, or by the dimension of the trapping well.
Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra(Advances in Computer Vision and Pattern Recognition, Springer Science + Business Media, 2014)[Article]
Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.
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