135 research outputs found
Turbo Bayesian Compressed Sensing
Compressed sensing (CS) theory specifies a new signal acquisition approach, potentially allowing the acquisition of signals at a much lower data rate than the Nyquist sampling rate. In CS, the signal is not directly acquired but reconstructed from a few measurements. One of the key problems in CS is how to recover the original signal from measurements in the presence of noise. This dissertation addresses signal reconstruction problems in CS. First, a feedback structure and signal recovery algorithm, orthogonal pruning pursuit (OPP), is proposed to exploit the prior knowledge to reconstruct the signal in the noise-free situation. To handle the noise, a noise-aware signal reconstruction algorithm based on Bayesian Compressed Sensing (BCS) is developed. Moreover, a novel Turbo Bayesian Compressed Sensing (TBCS) algorithm is developed for joint signal reconstruction by exploiting both spatial and temporal redundancy. Then, the TBCS algorithm is applied to a UWB positioning system for achieving mm-accuracy with low sampling rate ADCs. Finally, hardware implementation of BCS signal reconstruction on FPGAs and GPUs is investigated. Implementation on GPUs and FPGAs of parallel Cholesky decomposition, which is a key component of BCS, is explored. Simulation results on software and hardware have demonstrated that OPP and TBCS outperform previous approaches, with UWB positioning accuracy improved by 12.8x. The accelerated computation helps enable real-time application of this work
Decentralized Turbo Bayesian ompressed Sensing with application to UWB Systems
In many situations, there exist plenty of spatial and temporal redundancies in original signals. Based on this observation, a novel Turbo Bayesian Compressed Sensing (TBCS) algorithm is proposed to provide an efficient approach to transfer and incorporate this redundant information for joint sparse signal reconstruction. As a case study, the TBCS algorithm is applied in Ultra-Wideband (UWB) systems. A space-time TBCS structure is developed for exploiting and incorporating the spatial and temporal a priori information for space-time signal reconstruction. Simulation results demonstrate that the proposed TBCS algorithm achieves much better performance with only a few measurements in the presence of noise, compared with the traditional Bayesian Compressed Sensing (BCS) and multitask BCS algorithms
Prompt-based All-in-One Image Restoration using CNNs and Transformer
Image restoration aims to recover the high-quality images from their degraded
observations. Since most existing methods have been dedicated into single
degradation removal, they may not yield optimal results on other types of
degradations, which do not satisfy the applications in real world scenarios. In
this paper, we propose a novel data ingredient-oriented approach that leverages
prompt-based learning to enable a single model to efficiently tackle multiple
image degradation tasks. Specifically, we utilize a encoder to capture features
and introduce prompts with degradation-specific information to guide the
decoder in adaptively recovering images affected by various degradations. In
order to model the local invariant properties and non-local information for
high-quality image restoration, we combined CNNs operations and Transformers.
Simultaneously, we made several key designs in the Transformer blocks
(multi-head rearranged attention with prompts and simple-gate feed-forward
network) to reduce computational requirements and selectively determines what
information should be persevered to facilitate efficient recovery of
potentially sharp images. Furthermore, we incorporate a feature fusion
mechanism further explores the multi-scale information to improve the
aggregated features. The resulting tightly interlinked hierarchy architecture,
named as CAPTNet, despite being designed to handle different types of
degradations, extensive experiments demonstrate that our method performs
competitively to the task-specific algorithms
Cross-Layer Software-Defined 5G Network
In the past few decades, the world has witnessed a rapid growth in mobile
communication and reaped great benefits from it. Even though the fourth
generation (4G) mobile communication system is just being deployed worldwide,
proliferating mobile demands call for newer wireless communication technologies
with even better performance. Consequently, the fifth generation (5G) system is
already emerging in the research field. However, simply evolving the current
mobile networks can hardly meet such great expectations, because over the years
the infrastructures have generally become ossified, closed, and vertically
constructed. Aiming to establish a new paradigm for 5G mobile networks, in this
article, we propose a cross-layer software-defined 5G network architecture. By
jointly considering both the network layer and the physical layer together, we
establish the two software-defined programmable components, the control plane
and the cloud computing pool, which enable an effective control of the mobile
network from the global perspective and benefit technological innovations.
Specifically, by the cross-layer design for software-defining, the logically
centralized and programmable control plane abstracts the control functions from
the network layer down to the physical layer, through which we achieve the
fine-grained controlling of mobile network, while the cloud computing pool
provides powerful computing capability to implement the baseband data
processing of multiple heterogeneous networks. We discuss the main challenges
of our architecture, including the fine-grained control strategies, network
virtualization, and programmability. The architecture significantly benefits
the convergence towards heterogeneous networks and it enables much more
controllable, programmable and evolvable mobile networks.Comment: 9 pages, 5 figures, submitted to Mobile Networks & Application
Intrinsically unidirectional chemically fuelled rotary molecular motors
Biological systems mainly utilize chemical energy to fuel autonomous molecular motors, enabling the system to be driven out of equilibrium1. Taking inspiration from rotary motors such as the bacterial flagellar motor2 and adenosine triphosphate synthase3, and building on the success of light-powered unidirectional rotary molecular motors4–6, scientists have pursued the design of synthetic molecular motors solely driven by chemical energy7–13. However, designing artificial rotary molecular motors operating autonomously using a chemical fuel and simultaneously featuring the intrinsic structural design elements to allow full 360° unidirectional rotary motion like adenosine triphosphate synthase remains challenging. Here we show that a homochiral biaryl Motor-3, with three distinct stereochemical elements, is a rotary motor that undergoes repetitive and unidirectional 360° rotation of the two aryl groups around a single-bond axle driven by a chemical fuel. It undergoes sequential ester cyclization, helix inversion and ring opening, and up to 99% unidirectionality is realized over the autonomous rotary cycle. The molecular rotary motor can be operated in two modes: synchronized motion with pulses of a chemical fuel and acid–base oscillations; and autonomous motion in the presence of a chemical fuel under slightly basic aqueous conditions. This rotary motor design with intrinsic control over the direction of rotation, simple chemical fuelling for autonomous motion and near-perfect unidirectionality illustrates the potential for future generations of multicomponent machines to perform mechanical functions
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
Sequential recommendation is one of the most important tasks in recommender
systems, which aims to recommend the next interacted item with historical
behaviors as input. Traditional sequential recommendation always mainly
considers the collected positive feedback such as click, purchase, etc.
However, in short-video platforms such as TikTok, video viewing behavior may
not always represent positive feedback. Specifically, the videos are played
automatically, and users passively receive the recommended videos. In this new
scenario, users passively express negative feedback by skipping over videos
they do not like, which provides valuable information about their preferences.
Different from the negative feedback studied in traditional recommender
systems, this passive-negative feedback can reflect users' interests and serve
as an important supervision signal in extracting users' preferences. Therefore,
it is essential to carefully design and utilize it in this novel recommendation
scenario. In this work, we first conduct analyses based on a large-scale
real-world short-video behavior dataset and illustrate the significance of
leveraging passive feedback. We then propose a novel method that deploys the
sub-interest encoder, which incorporates positive feedback and passive-negative
feedback as supervision signals to learn the user's current active
sub-interest. Moreover, we introduce an adaptive fusion layer to integrate
various sub-interests effectively. To enhance the robustness of our model, we
then introduce a multi-task learning module to simultaneously optimize two
kinds of feedback -- passive-negative feedback and traditional randomly-sampled
negative feedback. The experiments on two large-scale datasets verify that the
proposed method can significantly outperform state-of-the-art approaches. The
code is released at https://github.com/tsinghua-fib-lab/RecSys2023-SINE.Comment: Accepted by RecSys'2
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