Bipolar measurement matrix using chaotic sequence

In this paper, a novel paradigm of constructing measurement matrices is proposed for compressive sensing. More precisely, the chaotic bipolar sequences ({1,−1} elements) are utilized to build binarization measurement matrices of general size. By means of concentration inequalities together with covering argument, we show that our proposed bipolar matrices meet the restricted isometry property, which can ensure exact recovery from the linear samples. Moreover, numerical experiments illustrate that the proposed matrices outperform its counterparts, such as random Gaussian matrix. For practical applications, our proposed matrices are highly efficient for storage, multiplier and rapid data acquisition, and hardware realization. We hope that our framework reveals new directions for practitioners, as it attempts to put some of the chaos theory in perspective for practical compressive sensing applications

A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks

In wireless multi-hop networks, instead of using the traditional store-and-forward method, the relay nodes can exploit the network coding idea to encode and transmit the packets in the distributed coding-aware routing (DCAR) mechanisms, which can decrease the transmission number and achieve higher throughput. However, depending on the primary coding conditions of DCAR, the DCAR-type schemes may not only detect more coding opportunities, but also lead to an imbalanced distribution of the network load. Especially, they are not energy efficient in more complex scenarios, such as wireless ad-hoc networks. In this paper, to solve these shortcomings, we propose a constrained coding-aware routing (CCAR) mechanism with the following benefits: (1) by the constrained coding conditions, the proposed mechanism can detect good coding opportunities and assure a higher decoding probability; (2) we propose a tailored “routing + coding„ discovery process, which is more lightweight and suitable for the CCAR scheme; and (3) by evaluating the length of the output queue, we can estimate the states of coding nodes to improve the efficient coding benefit. To those ends, we implement the CCAR scheme in different topologies with the ns-2 simulation tool. The simulation results show that a higher effective coding benefit ratio can be achieved by the constrained coding conditions and new coding benefit function. Moreover, the CCAR scheme has significant advantages regarding throughput, average end-to-end delay, and energy consumption

Distribution of Geochemical Fractions of Phosphorus in Surface Sediment in Daya Bay, China

Surface sediment samples were collected from 19 sites throughout Daya Bay, China, to study the concentrations, and spatial distributions of different fractions of phosphorus through sequential extraction methods. Like many coastal and marine areas, De-P was the dominant form of P, contributing 47.5% of TP, followed by O-P, contributing 25.5% of TP. Ex-P and Fe-P contribute the lowest to TP. The concentration of sedimentary TP ranged from 290.3~525.1 µg/g, with the average of 395.3 µg/g, which was a similar range to other estuaries and coastal areas. Based on the spatial distribution, Pearson correlation and Principal component analysis, different fractions of phosphorus showed different spatial distributions due to different sources. The molar ratio of organic carbon to phosphorus (TOC/O-P) ranged from 199 to 609, with the average of 413, which was much higher than the Redfield ratio, suggesting terrestrial sources of organic matter in Daya Bay surface sediment. The average bioavailable phosphorus was 149.6 µg/g and contributed 37.8% (24.6~56.0%) of TP, indicating that the surface sediments of Day Bay act as an important internal source of P

Chaotic Pattern Array for Single-Pixel Imaging

Single-pixel imaging (SPI) is an emerging framework that can capture the image of a scene via a single-point detector at a considerably low cost. It measures the projection at the detector of the scene under view with certain patterns. One can reconstruct the image of the scene via post-processing the measurements modulated by the patterns. However, the most commonly-used random patterns are not always desirable in many applications, especially for real-time, resource-limited occasions, due to their high memory requirement and huge cost in software and hardware implementation. In this paper, a chaotic pattern array is proposed for the SPI architecture. Compared with random patterns, the proposed chaotic pattern array can not only promise to increase the capabilities of the SPI device, but can also reduce the memory cost and complexity of hardware implementation in the meantime. Moreover, convincing experiment results are given to illustrate that the proposed pattern array is suitable for single-pixel cameras, as well as other compressive imaging applications

Interhemispheric Brain Switching Correlates with Severity of Sleep-Disordered Breathing for Obstructive Sleep Apnea Patients

(1) Background: Alternating interhemispheric slow-wave activity during sleep is well-established in birds and cetaceans, but its investigation in humans has been largely neglected. (2) Methods: Fuzzy entropy was used to calculate a laterality index (LI) from C3 and C4 EEG channels. The subjects were grouped according to an apnoea-hypopnoea index (AHI) for statistical analyses: Group A AHI < 15 (mild); Group B 15 ≤ AHI < 30 (moderate); Group C AHI ≥ 30 (severe). The LI distribution was analysed to characterise the brain activity variation in both hemispheres, and the cross-zero switching rate was given statistical tests to find the correlations with the severity of obstructive sleep apnea and sleep states, i.e., wake (W), light sleep (LS), deep sleep (DS), and REM. (3) Results: EEG brain switching activity was observed in all sleep stages, and the LI distribution shows that, for obstructive sleep apnea patients, the interhemispheric asymmetry of brain activity is more obvious than healthy people. A one-way ANOVA revealed a significant difference of switching rate among three groups (F(2,95) = 7.23, p = 0.0012), with Group C shows the least, and also a significant difference among four sleep stages (F(3,94) = 5.09, p = 0.0026), with REM the highest. (4) Conclusions: The alternating interhemispheric activity is confirmed ubiquitous for humans during sleep, and sleep-disordered breathing intends to exacerbate the interhemispheric asymmetry

Electrocardiogram reconstruction based on compressed sensing

Compressed Sensing (CS) attempts to acquire and reconstruct a sparse signal from a sampling much below the Nyquist rate. In this paper, we proposed novel CS algorithms for reconstructing under-sampled and compressed electrocardiogram (ECG) signal. In the proposed CS-ECG scheme, the ECG signal was first sub-sampled randomly and mapped onto a two-dimensional (2D) space by using Cut and Align (CAB), for the purpose of promoting sparsity. A nonlinear optimization model was then used to reconstruct the 2D signal. In the compression scheme, the ECG signal was mapped into the frequency domain, and the compression was achieved by a series of multiplying and accumulating between the original ECG and a Gaussian random matrix. For the reconstruction, two matching pursuits (MP) methods and two blocks sparse Bayesian learning (BSBL) methods were implemented and evaluated by the percentage root-mean-square difference (PRD). Based on the test with real ECG data, it was found that the proposed CS scheme was capable of faithfully reconstructing ECG signals with only 30% acquisition

High Energy Density Shape Memory Polymers Using Strain-Induced Supramolecular Nanostructures.

Shape memory polymers are promising materials in many emerging applications due to their large extensibility and excellent shape recovery. However, practical application of these polymers is limited by their poor energy densities (up to ∼1 MJ/m3). Here, we report an approach to achieve a high energy density, one-way shape memory polymer based on the formation of strain-induced supramolecular nanostructures. As polymer chains align during strain, strong directional dynamic bonds form, creating stable supramolecular nanostructures and trapping stretched chains in a highly elongated state. Upon heating, the dynamic bonds break, and stretched chains contract to their initial disordered state. This mechanism stores large amounts of entropic energy (as high as 19.6 MJ/m3 or 17.9 J/g), almost six times higher than the best previously reported shape memory polymers while maintaining near 100% shape recovery and fixity. The reported phenomenon of strain-induced supramolecular structures offers a new approach toward achieving high energy density shape memory polymers