A Real-Time Monitoring System of Industry Carbon Monoxide Based on Wireless Sensor Networks

Carbon monoxide (CO) burns or explodes at over-standard concentration. Hence, in this paper, a Wifi-based, real-time monitoring of a CO system is proposed for application in the construction industry, in which a sensor measuring node is designed by low-frequency modulation method to acquire CO concentration reliably, and a digital filtering method is adopted for noise filtering. According to the triangulation, the Wifi network is constructed to transmit information and determine the position of nodes. The measured data are displayed on a computer or smart phone by a graphical interface. The experiment shows that the monitoring system obtains excellent accuracy and stability in long-term continuous monitoring

SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks

Spiking neural networks are efficient computation models for low-power environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful techniques for SNN training. Nevertheless, the spike-base BP training is slow and requires large memory costs. Though ANN2NN provides a low-cost way to train SNNs, it requires many inference steps to mimic the well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN (SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN and SNN and b) spiking mapping units. Firstly, the architecture trains the weight-sharing parameters on the ANN branch, resulting in fast training and low memory costs for SNN. Secondly, the spiking mapping units ensure that the activation values of the ANN are the spiking features. As a result, the classification error of the SNN can be optimized by training the ANN branch. Besides, we design an adaptive threshold adjustment (ATA) algorithm to address the noisy spike problem. Experiment results show that our SNN2ANN-based models perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet). Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps, 0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the Spike-based BP model

Beam pointing stabilization of an acousto-optic modulator with thermal control

Diffraction beams generated by an acousto-optic modulator (AOM) are widely used in various optical experiments, some of which require high angular stability with the temporal modulation of optical power. Usually, it is difficult to realize both angular stability and high-power modulation in a passive setup without a servo system of radio-frequency compensation. Here, we present a method to suppress the angular drift and pointing noise only with the thermal management of the AOM crystal. We analyze the dependence of the angular drift on the refractive index variation and find that the angular drift is very sensitive to the temperature gradient, which could induce the refractive index gradient inside the AOM crystal. It reminds us that such angular drift could be significantly suppressed by carefully overlapping the zero temperature gradient area with the position of the acousto-optic interaction zone. We implement a water-cooling setup and find that the angular drift of an AOM is reduced over 100 times during the thermal transient and the angular noise is also suppressed to one-third of the non-cooled case. It should be emphasized that this thermal control method generally used to suppress the beam drift in both the diffraction and the perpendicular-to-diffraction directions. The refractive index thermal coefficient of tellurium dioxide crystal at 1064 nm determined by this angular drift-temperature model is 16×10 −6 K −1, consistent with previous studies. This thermal control technique provides potential applications for optical trapping and remote sensoring that demand for intensity ramps

Effective Action Recognition with Embedded Key Point Shifts

Temporal feature extraction is an essential technique in video-based action recognition. Key points have been utilized in skeleton-based action recognition methods but they require costly key point annotation. In this paper, we propose a novel temporal feature extraction module, named Key Point Shifts Embedding Module ($KPSEM$), to adaptively extract channel-wise key point shifts across video frames without key point annotation for temporal feature extraction. Key points are adaptively extracted as feature points with maximum feature values at split regions, while key point shifts are the spatial displacements of corresponding key points. The key point shifts are encoded as the overall temporal features via linear embedding layers in a multi-set manner. Our method achieves competitive performance through embedding key point shifts with trivial computational cost, achieving the state-of-the-art performance of 82.05% on Mini-Kinetics and competitive performance on UCF101, Something-Something-v1, and HMDB51 datasets.Comment: 35 pages, 10 figure

Comparison of lignocellulose composition in four major species of Miscanthus

Miscanthus is a perennial grass rich in lignocellulose that has attracted interest as a non-food crop for renewable bioenergy with major environmental and economic benefits for China. The lignocellulose composition of whole stems of four major species of Miscanthus was assessed. The average values of total moisture content (TMC) (61.90%) and hemicelluloses (34.86%) were the highest while cellulose (32.71%) and acid detergent lignin (ADL) (8.90%) were the lowest in Miscanthus floridulus. On the contrary, the contents of cellulose (42.11%) and ADL (13.64%) were the highest and total ash (TA) (2.89%) was the lowest in Miscanthus lutarioriparius. The Shannon–Weaver diversity indices of components for the four species showed that hemicellulose content (H’= 2.00±0.11) was the most variable trait followed by cellulose (H’= 1.84±0.07), then ADL (H’= 1.84±0.07). The variational range of each component was relatively higher in Miscanthus sacchariflorus. In M. lutarioriparius, the diversity indices of each component were moderate. The diversity of cellulose was the highest and hemicellulose, ADL, TA and TMC were low in Miscanthus sinensis. By correlation analysis, neutral detergent fiber (NDF) significantly and positively correlated with ADF, cellulose and ADL at P<0.01 as well as the relationship of cellulose and ADL in the four species. Hemicellulose showed significant (P<0.01) but negative correlation with cellulose and ADL in M. floridulus, M. lutarioriparius and M. sacchariflorus. By principal component analysis (PCA), the components ADF and cellulose were the PC1 that were considered the foremost for the evaluation and selection of resource in the four species. The conclusions show that lignocellulose composition contents of Miscanthus culms were different. M. floridulus was more fit to ethanol fermentation. Though the components contents in M. sinensis and M. sacchariflorus were moderate, the range of choice was large. It provided a possible means to screen the appropriate materials according to different utilization. M. lutarioriparius had more superiorities relatively. So the four species of Miscanthus were appropriate for extension as excellent herbaceous energy plants, though, reasonable species choice should be employed according to the conversion approach and the growth characteristics, productivity levels and biomass quality characteristics of these tall grasses.Keywords: Miscanthus, bioenergy, lignocellulose compositions, detergent fiber, diversity analysis, PC