215 research outputs found
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration
Efficient and accurate low-rank approximation (LRA) methods are of great
significance for large-scale data analysis. Randomized tensor decompositions
have emerged as powerful tools to meet this need, but most existing methods
perform poorly in the presence of noise interference. Inspired by the
remarkable performance of randomized block Krylov iteration (rBKI) in reducing
the effect of tail singular values, this work designs an rBKI-based Tucker
decomposition (rBKI-TK) for accurate approximation, together with a
hierarchical tensor ring decomposition based on rBKI-TK for efficient
compression of large-scale data. Besides, the error bound between the
deterministic LRA and the randomized LRA is studied. Numerical experiences
demonstrate the efficiency, accuracy and scalability of the proposed methods in
both data compression and denoising
Automatic Measurement and Monitoring Technology for Oil Well
Measurement technology of oil well develops and improves constantly at present. Reducing operation cost and energy consumption and improving efficiency of labor provides reliable technical guarantee for simplifying and optimizing ground process system. According to the needs of parametric measurement and monitoring for oil well, the paper combines with actual situation of the second factory in Dagang Oilfield, the second factory proposes the research on automatic measurement and monitoring technology for wireless oil well and application project, and scientific and information department in Dagang Oilfield ratifies the project. The paper studies automatic measurement and monitoring technology for wireless oil well, and evaluates the economic and social benefit
Contactless Electrocardiogram Monitoring with Millimeter Wave Radar
The electrocardiogram (ECG) has always been an important biomedical test to
diagnose cardiovascular diseases. Current approaches for ECG monitoring are
based on body attached electrodes leading to uncomfortable user experience.
Therefore, contactless ECG monitoring has drawn tremendous attention, which
however remains unsolved. In fact, cardiac electrical-mechanical activities are
coupling in a well-coordinated pattern. In this paper, we achieve contactless
ECG monitoring by breaking the boundary between the cardiac mechanical and
electrical activity. Specifically, we develop a millimeter-wave radar system to
contactlessly measure cardiac mechanical activity and reconstruct ECG without
any contact in. To measure the cardiac mechanical activity comprehensively, we
propose a series of signal processing algorithms to extract 4D cardiac motions
from radio frequency (RF) signals. Furthermore, we design a deep neural network
to solve the cardiac related domain transformation problem and achieve
end-to-end reconstruction mapping from RF input to the ECG output. The
experimental results show that our contactless ECG measurements achieve timing
accuracy of cardiac electrical events with median error below 14ms and
morphology accuracy with median Pearson-Correlation of 90% and median
Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results
indicate that the system enables the potential of contactless, continuous and
accurate ECG monitoring
Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact
Digital images can be forged easily with today’s widely available image processing software. In this paper, we describe a passive approach to detect digital forgeries by checking inconsistencies of blocking artifact. Given a digital image, we find that the blocking artifacts introduced during JPEG compression could be used as a “natural authentication code”. A blocking artifact measure is then proposed based on the estimated quantization table using the power spectrum of the DCT coefficient histogram. Experimental results also demonstrate the validity of the proposed approach. 1
Recaptured photo detection using specularity distribution
Detection of planar surfaces in a generic scene is difficult when the illumination is complex and less intense, and the surfaces have non-uniform colors (e.g., a movie poster). As a result, the specularity, if appears, is superimposed with the surface color pattern, and hence the observation of uniform specularity is no longer sufficient for identifying planar sur-faces in a generic scene as it does under a distant point light source. In this paper, we address the problem of planar sur-face recognition in a single generic-scene image. In partic-ular, we study the problem of recaptured photo recognition as an application in image forensics. We discover that the specularity of a recaptured photo is modulated by the micro-structure of the photo surface, and its spatial distribution can be used for differentiating recaptured photos from the origi-nal photos. We validate our findings in real images of generic scenes. Experimental results show that there is a distinguish-able feature of natural scene and recaptured images. Given the definition of specular ratio as the percentage of specularity in the overall measured intensity, the distribution of specular ra-tio image’s gradient of natural images is Laplacian-like while that of recaptured images is Rayleigh-like. Index Terms — Image forensics, recaptured photo detec-tion, dichromatic reflectance model, specularity 1
AST:Adaptive Self-supervised Transformer for optical remote sensing representation
Due to the variation in spatial resolution and the diversity of object scales, the interpretation of optical remote sensing images is extremely challenging. Deep learning has become the mainstream solution to interpret such complex scenes. However, the explosion of deep learning model architectures has resulted in the need for hundreds of millions of remote sensing images for which labels are very costly or often unavailable publicly. This paper provides an in-depth analysis of the main reasons for this data thirst, i.e., (i) limited representational power for model learning, and (ii) underutilization of unlabeled remote sensing data. To overcome the above difficulties, we present a scalable and adaptive self-supervised Transformer (AST) for optical remote sensing image interpretation. By performing masked image modeling in pre-training, the proposed AST releases the rich supervision signals in massive unlabeled remote sensing data and learns useful multi-scale semantics. Specifically, a cross-scale Transformer architecture is designed to collaboratively learn global dependencies and local details by introducing a pyramid structure, to facilitate multi-granular feature interactions and generate scale-invariant representations. Furthermore, a masking token strategy relying on correlation mapping is proposed to achieve adaptive masking of partial patches without affecting key structures, which enhances the understanding of visually important regions. Extensive experiments on various optical remote sensing interpretation tasks show that AST has good generalization capability and competitiveness.</p
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
Exact recovery of tensor decomposition (TD) methods is a desirable property
in both unsupervised learning and scientific data analysis. The numerical
defects of TD methods, however, limit their practical applications on
real-world data. As an alternative, convex tensor decomposition (CTD) was
proposed to alleviate these problems, but its exact-recovery property is not
properly addressed so far. To this end, we focus on latent convex tensor
decomposition (LCTD), a practically widely-used CTD model, and rigorously prove
a sufficient condition for its exact-recovery property. Furthermore, we show
that such property can be also achieved by a more general model than LCTD. In
the new model, we generalize the classic tensor (un-)folding into reshuffling
operation, a more flexible mapping to relocate the entries of the matrix into a
tensor. Armed with the reshuffling operations and exact-recovery property, we
explore a totally novel application for (generalized) LCTD, i.e., image
steganography. Experimental results on synthetic data validate our theory, and
results on image steganography show that our method outperforms the
state-of-the-art methods.Comment: AAAI-202
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal
Human gesture recognition using millimeter wave (mmWave) signals provides
attractive applications including smart home and in-car interface. While
existing works achieve promising performance under controlled settings,
practical applications are still limited due to the need of intensive data
collection, extra training efforts when adapting to new domains (i.e.
environments, persons and locations) and poor performance for real-time
recognition. In this paper, we propose DI-Gesture, a domain-independent and
real-time mmWave gesture recognition system. Specifically, we first derive the
signal variation corresponding to human gestures with spatial-temporal
processing. To enhance the robustness of the system and reduce data collecting
efforts, we design a data augmentation framework based on the correlation
between signal patterns and gesture variations. Furthermore, we propose a
dynamic window mechanism to perform gesture segmentation automatically and
accurately, thus enable real-time recognition. Finally, we build a lightweight
neural network to extract spatial-temporal information from the data for
gesture classification. Extensive experimental results show DI-Gesture achieves
an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments
and locations, respectively. In real-time scenario, the accuracy of DI-Gesutre
reaches over 97% with average inference time of 2.87ms, which demonstrates the
superior robustness and effectiveness of our system.Comment: The paper is submitted to the journal of IEEE Transactions on Mobile
Computing. And it is still under revie
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