267 research outputs found
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
Semantic communications can reduce the resource consumption by transmitting
task-related semantic information extracted from source messages. However, when
the source messages are utilized for various tasks, e.g., wireless sensing data
for localization and activities detection, semantic communication technique is
difficult to be implemented because of the increased processing complexity. In
this paper, we propose the inverse semantic communications as a new paradigm.
Instead of extracting semantic information from messages, we aim to encode the
task-related source messages into a hyper-source message for data transmission
or storage. Following this paradigm, we design an inverse semantic-aware
wireless sensing framework with three algorithms for data sampling,
reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised
decoding, respectively. Specifically, on the one hand, we propose a novel RIS
hardware design for encoding several signal spectrums into one MetaSpectrum. To
select the task-related signal spectrums for achieving efficient encoding, a
semantic hash sampling method is introduced. On the other hand, we propose a
self-supervised learning method for decoding the MetaSpectrums to obtain the
original signal spectrums. Using the sensing data collected from real-world, we
show that our framework can reduce the data volume by 95% compared to that
before encoding, without affecting the accomplishment of sensing tasks.
Moreover, compared with the typically used uniform sampling scheme, the
proposed semantic hash sampling scheme can achieve 67% lower mean squared error
in recovering the sensing parameters. In addition, experiment results
demonstrate that the amplitude response matrix of the RIS enables the
encryption of the sensing data
Dictionary selection for Compressed Sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning
Online monitoring of electroencephalogram (EEG) signals is challenging due to the high volume of data and power requirements. Compressed sensing (CS) may be employed to address these issues. Compressed sensing using sparse binary matrix, owing to its low power features, and reconstruction/decompression using spatiotemporal sparse Bayesian learning have been shown to constitute a robust framework for fast, energy efficient and accurate multichannel bio-signal monitoring. EEG signal, however, does not show a strong temporal correlation. Therefore, the use of sparsifying dictionaries has been proposed to exploit the sparsity in a transformed domain instead. Assuming sparsification adds values, a challenge, therefore, in employing this CS framework for the EEG signal is to identify the suitable dictionary. Using real multichannel EEG data from 15 subjects, in this paper, we systematically evaluated the performance of the framework when using various wavelet bases while considering their key attributes of number of vanishing moments and coherence with sensing matrix. We identified Beylkin as the wavelet dictionary leading to the best performance. Using the same dataset, we then compared the performance of Beylkin with discrete cosine basis, often used in the literature, and the case of using no sparsifying dictionary. We further demonstrate that using dictionaries (Beylkin and DCT) may improve performance tangibly only for a high compression ratio (CR) of 80% and with smaller block sizes; as compared to when using no dictionaries
MFPA: Mixed-Signal Field Programmable Array for Energy-Aware Compressive Signal Processing
Compressive Sensing (CS) is a signal processing technique which reduces the number of samples taken per frame to decrease energy, storage, and data transmission overheads, as well as reducing time taken for data acquisition in time-critical applications. The tradeoff in such an approach is increased complexity of signal reconstruction. While several algorithms have been developed for CS signal reconstruction, hardware implementation of these algorithms is still an area of active research. Prior work has sought to utilize parallelism available in reconstruction algorithms to minimize hardware overheads; however, such approaches are limited by the underlying limitations in CMOS technology. Herein, the MFPA (Mixed-signal Field Programmable Array) approach is presented as a hybrid spin-CMOS reconfigurable fabric specifically designed for implementation of CS data sampling and signal reconstruction. The resulting fabric consists of 1) slice-organized analog blocks providing amplifiers, transistors, capacitors, and Magnetic Tunnel Junctions (MTJs) which are configurable to achieving square/square root operations required for calculating vector norms, 2) digital functional blocks which feature 6-input clockless lookup tables for computation of matrix inverse, and 3) an MRAM-based nonvolatile crossbar array for carrying out low-energy matrix-vector multiplication operations. The various functional blocks are connected via a global interconnect and spin-based analog-to-digital converters. Simulation results demonstrate significant energy and area benefits compared to equivalent CMOS digital implementations for each of the functional blocks used: this includes an 80% reduction in energy and 97% reduction in transistor count for the nonvolatile crossbar array, 80% standby power reduction and 25% reduced area footprint for the clockless lookup tables, and roughly 97% reduction in transistor count for a multiplier built using components from the analog blocks. Moreover, the proposed fabric yields 77% energy reduction compared to CMOS when used to implement CS reconstruction, in addition to latency improvements
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
Impact of New Method for Laying Separate Sewer System on Pavement Layers
The method of installing underground infrastructure has a significant influence on road resistance and performance under live loads such as traffic. This research presents a new method for laying separate sewer systems by using one trench to sit both sanitary pipe and storm pipe and considers the effects of this approach on the pavement strength. Experimental tests have been conducted in the laboratory using a trench 2.5x0.45x1 metre to install two pipes one over the other (sanitary pipe in the bottom and storm pipe on top). Two cases have tested, the first case using 5 cm surface layer of cold mix asphalt while the second is using soil. A series of loads were applied to test the behaviour of this new system and its effects on the pavement surface layer and the buried pipe. The comparison between the rut print of the live load on the soil layer and the pavement layer was conducted. Results demonstrated that using the cold mix asphalt is still insufficient to provide enough safety to protect buried pipe as a reason of needing to relatively long time to acquire high stiffness. Therefore, minimum cover depth to protect pipelines still required
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