529 research outputs found
Ultra low power MAC layer wake-up frame scheme for low cost and low traffic wireless sensor networks
Wireless sensor networks, as a key enabling technology of Ubiquitous Computing, have
been a booming research topic in the recent years. Upon designing a low-cost wireless
sensor device, power consumption is one of the most important issues, because cheap
batteries are normally the power suppliers. Since the RF transceiver is one of the biggest
power consumers in such a sensor device, enabling the RF transceiver to sleep as much as
possible is the preferred method to save power, which is normally realized by MAC layer
duty cycle scheduling.
This dissertation proposes aMAC layer wake-up-frame scheme to wake up an RF transceiver
on-demand to minimize the standby waiting time in receive mode to save power.
Analytical and simulation results show that, for a low-traffic wireless sensor network, this
scheme gives significant system battery lifetime gain compared to the traditional methods.
Furthermore, the combination of the wake-up-frame scheme and a complementary lowpower
MAC protocol is discussed. Analytical computation and simulation prove that the
combined scheme achieves a further optimized solution in the sense of power-saving, while
other important system parameters, such as response time and channel efficiency, are limited
to a reasonable range
Development of high-efficient tin selenide-based thermoelectric materials
Thermoelectric materials can realize direct energy conversion from heat to electricity based on thermoelectric effects, thus have been considered as a green and sustainable solution to the global energy dilemma by harvesting electricity from waste heat or sunlight. The conversion efficiency can be expressed as ZT=S2σT/κ, where S is the Seebeck coefficient, σ is electrical conductivity, T is the absolute temperature, and κ is the thermal conductivity. To date, two major strategies for achieving high ZT are optimizing the power factor S2σ and reducing lattice thermal conductivity κl by band and structural engineering, respectively. However, the current commercial thermoelectric materials such as bismuth telluride (Bi2Te3) have their ZT values limited t
Social interdependence theory in sport
This thesis investigates examining the effects of certain types of interdependence on motor performance in competition. In the first experiment, participants undertook a ball carrying and running task under varying levels of between-team resource interdependent condition in the individual competition. The number of balls that carried to the container decreased when between-team resource interdependence exists. In the second experiment, participants completed a basketball shooting and rebounding task under varying levels of between-team resource interdependent condition in a two-on-two team competition. Results indicated that the number of baskets made, the number of baskets attempted and the shooting accuracy was higher in resource independent competition. In the third experiment, participants undertook the same basketball shooting and rebounding task as the second experiment under varying levels of between-team resource interdependent condition and within-team reward interdependent condition. Results indicated effort-based performance was greater under resource independent condition and its interaction with low reward interdependent condition. In the final experiment, participants undertook a handgrip task in a four-on-four team competition. Compared to the no reward condition, performance was better under both high reward interdependent condition and low reward interdependent condition. Mediation analyses revealed that positive emotions, self-reported effort and pressure mediated the change of performance
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising
Degraded images commonly exist in the general sources of character images,
leading to unsatisfactory character recognition results. Existing methods have
dedicated efforts to restoring degraded character images. However, the
denoising results obtained by these methods do not appear to improve character
recognition performance. This is mainly because current methods only focus on
pixel-level information and ignore critical features of a character, such as
its glyph, resulting in character-glyph damage during the denoising process. In
this paper, we introduce a novel generic framework based on glyph fusion and
attention mechanisms, i.e., CharFormer, for precisely recovering character
images without changing their inherent glyphs. Unlike existing frameworks,
CharFormer introduces a parallel target task for capturing additional
information and injecting it into the image denoising backbone, which will
maintain the consistency of character glyphs during character image denoising.
Moreover, we utilize attention-based networks for global-local feature
interaction, which will help to deal with blind denoising and enhance denoising
performance. We compare CharFormer with state-of-the-art methods on multiple
datasets. The experimental results show the superiority of CharFormer
quantitatively and qualitatively.Comment: Accepted by ACM MM 202
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
Constructing high-quality character image datasets is challenging because
real-world images are often affected by image degradation. There are
limitations when applying current image restoration methods to such real-world
character images, since (i) the categories of noise in character images are
different from those in general images; (ii) real-world character images
usually contain more complex image degradation, e.g., mixed noise at different
noise levels. To address these problems, we propose a real-world character
restoration network (RCRN) to effectively restore degraded character images,
where character skeleton information and scale-ensemble feature extraction are
utilized to obtain better restoration performance. The proposed method consists
of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet
aims to preserve the structural consistency of the character and normalize
complex noise. Then, CiRNet reconstructs clean images from degraded character
images and their skeletons. Due to the lack of benchmarks for real-world
character image restoration, we constructed a dataset containing 1,606
character images with real-world degradation to evaluate the validity of the
proposed method. The experimental results demonstrate that RCRN outperforms
state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202
- …