2,483 research outputs found
Graphic-Card Cluster for Astrophysics (GraCCA) -- Performance Tests
In this paper, we describe the architecture and performance of the GraCCA
system, a Graphic-Card Cluster for Astrophysics simulations. It consists of 16
nodes, with each node equipped with 2 modern graphic cards, the NVIDIA GeForce
8800 GTX. This computing cluster provides a theoretical performance of 16.2
TFLOPS. To demonstrate its performance in astrophysics computation, we have
implemented a parallel direct N-body simulation program with shared time-step
algorithm in this system. Our system achieves a measured performance of 7.1
TFLOPS and a parallel efficiency of 90% for simulating a globular cluster of
1024K particles. In comparing with the GRAPE-6A cluster at RIT (Rochester
Institute of Technology), the GraCCA system achieves a more than twice higher
measured speed and an even higher performance-per-dollar ratio. Moreover, our
system can handle up to 320M particles and can serve as a general-purpose
computing cluster for a wide range of astrophysics problems.Comment: Accepted for publication in New Astronom
On the Patent Claim Eligibility Prediction Using Text Mining Techniques
With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Of the many patentability requirements, patentable subject matter serves as a gatekeeping function to prevent a patent from preempting future innovation. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a clear definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants. In this research, in order to solve the software patent eligibility issues, we propose an effective model to determine patent claim eligibility by text-mining and machine learning techniques. Drawing upon USPTO issued guidelines, we identify 66 patent cases to design domain knowledge features, including abstractness features and distinguishable word features, as well as other textual features, to develop the claim eligibility prediction model. The experiment results show our proposed model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model
TCN AA: A Wi Fi based Temporal Convolution Network for Human to Human Interaction Recognition with Augmentation and Attention
The utilization of Wi-Fi-based human activity recognition (HAR) has gained
considerable interest in recent times, primarily owing to its applications in
various domains such as healthcare for monitoring breath and heart rate,
security, elderly care, and others. These Wi-Fi-based methods exhibit several
advantages over conventional state-of-the-art techniques that rely on cameras
and sensors, including lower costs and ease of deployment. However, a
significant challenge associated with Wi-Fi-based HAR is the significant
decline in performance when the scene or subject changes. To mitigate this
issue, it is imperative to train the model using an extensive dataset. In
recent studies, the utilization of CNN-based models or sequence-to-sequence
models such as LSTM, GRU, or Transformer has become prevalent. While
sequence-to-sequence models can be more precise, they are also more
computationally intensive and require a larger amount of training data. To
tackle these limitations, we propose a novel approach that leverages a temporal
convolution network with augmentations and attention, referred to as TCN-AA.
Our proposed method is computationally efficient and exhibits improved accuracy
even when the data size is increased threefold through our augmentation
techniques. Our experiments on a publicly available dataset indicate that our
approach outperforms existing state-of-the-art methods, with a final accuracy
of 99.42%.Comment: Published to IEEE Internet of things Journal but haven't been
accepted yet (under review
Low-rank matrix recovery with structural incoherence for robust face recognition
We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1
Hybrid OFDM/OOK Modulations in OCDMA Scheme for Free Space Optics
The study proposes hybrid analog/digital transmission format scheme which integrated optical code-division multiple-access (OCDMA) and polarization multiplexing technique in free space optics (FSO) transmission. Orthogonal frequency division multiplexing (OFDM) transmits as the analog format and (on-off keying) OOK transmits as digital format in the study, respectively. In the proposed hybrid OCDMA system, it has high-speed transmission, signal security and low cost…etc. The multiple access interference (MAI) can be efficiently eliminated by using the balanced detection scheme at the receive end
EVALUATION OF RECEIVING ABILITY OF TEENAGE MALE TABLE TENNIS PLAYERS IN TAIWAN
The purpose of this study was to evaluate the forehand receiving ability of teenage male table tennis players. Thirty-nine male players consist of skill levels from junior to senior high school students and national squads were selected. This assessment involves three tests: basic control, judgment, and match-like condition simulation. We found under the basic control test, the junior high school players performed poorer in downspin and left-side downspin in the aspect of accuracy (
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