71 research outputs found
Deep Residual Shrinkage Networks for EMG-based Gesture Identification
This work introduces a method for high-accuracy EMG based gesture
identification. A newly developed deep learning method, namely, deep residual
shrinkage network is applied to perform gesture identification. Based on the
feature of EMG signal resulting from gestures, optimizations are made to
improve the identification accuracy. Finally, three different algorithms are
applied to compare the accuracy of EMG signal recognition with that of DRSN.
The result shows that DRSN excel traditional neural networks in terms of EMG
recognition accuracy. This paper provides a reliable way to classify EMG
signals, as well as exploring possible applications of DRSN
Promoting cold-start items in recommender systems
As one of major challenges, cold-start problem plagues nearly all recommender
systems. In particular, new items will be overlooked, impeding the development
of new products online. Given limited resources, how to utilize the knowledge
of recommender systems and design efficient marketing strategy for new items is
extremely important. In this paper, we convert this ticklish issue into a clear
mathematical problem based on a bipartite network representation. Under the
most widely used algorithm in real e-commerce recommender systems, so-called
the item-based collaborative filtering, we show that to simply push new items
to active users is not a good strategy. To our surprise, experiments on real
recommender systems indicate that to connect new items with some less active
users will statistically yield better performance, namely these new items will
have more chance to appear in other users' recommendation lists. Further
analysis suggests that the disassortative nature of recommender systems
contributes to such observation. In a word, getting in-depth understanding on
recommender systems could pave the way for the owners to popularize their
cold-start products with low costs.Comment: 6 pages, 6 figure
CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval
Text-based Person Retrieval (TPR) aims to retrieve the target person images
given a textual query. The primary challenge lies in bridging the substantial
gap between vision and language modalities, especially when dealing with
limited large-scale datasets. In this paper, we introduce a CLIP-based
Synergistic Knowledge Transfer (CSKT) approach for TPR. Specifically, to
explore the CLIP's knowledge on input side, we first propose a Bidirectional
Prompts Transferring (BPT) module constructed by text-to-image and
image-to-text bidirectional prompts and coupling projections. Secondly, Dual
Adapters Transferring (DAT) is designed to transfer knowledge on output side of
Multi-Head Attention (MHA) in vision and language. This synergistic two-way
collaborative mechanism promotes the early-stage feature fusion and efficiently
exploits the existing knowledge of CLIP. CSKT outperforms the state-of-the-art
approaches across three benchmark datasets when the training parameters merely
account for 7.4% of the entire model, demonstrating its remarkable efficiency,
effectiveness and generalization.Comment: ICASSP2024(accepted). minor typos revision compared to version 1 in
arxi
Evaluation of a 300 GHz Near Field Antenna Measurement System
Terahertz (THz) and millimeter-wave technology has been identified as one of the most promising techniques for high data rate mobile communications beyond 5G, next generation autonomous radars and security imaging. 0.3 THz is one of the preferred candidate frequency bands due to its relatively low atmospheric absorption rate, sub-millimetre resolution and tens of GHz bandwidth. At these frequencies, antenna characterisation particularly in the far-field, is very challenging due to low output power and dynamic range of commercial test equipment
Audiovisual n-Back Training Alters the Neural Processes of Working Memory and Audiovisual Integration: Evidence of Changes in ERPs
(1) Background: This study investigates whether audiovisual n-back training leads to training effects on working memory and transfer effects on perceptual processing. (2) Methods: Before and after training, the participants were tested using the audiovisual n-back task (1-, 2-, or 3-back), to detect training effects, and the audiovisual discrimination task, to detect transfer effects. (3) Results: For the training effect, the behavioral results show that training leads to greater accuracy and faster response times. Stronger training gains in accuracy and response time using 3- and 2-back tasks, compared to 1-back, were observed in the training group. Event-related potentials (ERPs) data revealed an enhancement of P300 in the frontal and central regions across all working memory levels after training. Training also led to the enhancement of N200 in the central region in the 3-back condition. For the transfer effect, greater audiovisual integration in the frontal and central regions during the post-test rather than pre-test was observed at an early stage (80-120 ms) in the training group. (4) Conclusion: Our findings provide evidence that audiovisual n-back training enhances neural processes underlying a working memory and demonstrate a positive influence of higher cognitive functions on lower cognitive functions
A Quasi-optical THz Imaging System Using a One-port Vector Network Analyser
In this paper we demonstrate a simple single-pixel quasi-optical imaging setup with a one-port 140 GHz-220 GHz vector network analyser (VNA), a pyramidal horn antenna, a plano-convex lens and an X-Y translation stage. By placing an object at the focal plane of the lens and measuring its one-port S-parameters while scanning the object fixed onto the X-Y translation stage, 2D images of the object are constructed using S-parameter magnitude and phase information. The results show that the phase constructed image is better than magnitude constructed image in some cases. This finding indicates that VNA can be useful in THz imaging as it provides more information from the perspective of phase which can be used to enhance imaging
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