Research on the spectral reconstruction of a low-dimensional filter array micro-spectrometer based on a truncated singular value decomposition-convex optimization algorithm

Currently, the engineering of miniature spectrometers mainly faces three problems: the mismatch between the number of filters at the front end of the detector and the spectral reconstruction accuracy; the lack of a stable spectral reconstruction algorithm; and the lack of a spectral reconstruction evaluation method suitable for engineering. Therefore, based on 20 sets of filters, this paper classifies and optimizes the filter array by the K-means algorithm and particle swarm algorithm, and obtains the optimal filter combination under different matrix dimensions. Then, the truncated singular value decomposition-convex optimization algorithm is used for high-precision spectral reconstruction, and the detailed spectral reconstruction process of two typical target spectra is described. In terms of spectral evaluation, due to the strong randomness of the target detected during the working process of the spectrometer, the standard value of the target spectrum cannot be obtained. Therefore, for the first time, we adopt the method of joint cross-validation of multiple sets of data for spectral evaluation. The results show that when the random error of positive or negative 2 code values is applied multiple times for reconstruction, the spectral angle cosine value between the reconstructed curves becomes more than 0.995, which proves that the spectral reconstruction under this algorithm has high stability. At the same time, the spectral angle cosine value of the spectral reconstruction curve and the standard curve can reach above 0.99, meaning that it realizes a high-precision spectral reconstruction effect. A high-precision spectral reconstruction algorithm based on truncated singular value-convex optimization, which is suitable for engineering applications, is established in this paper, providing important scientific research value for the engineering application of micro-spectrometers.Comment: 22pages 11figure

Damage and Recovery of Hair Cells in Fish Canal (But Not Superficial) Neuromasts after Gentamicin Exposure

Recent evidence demonstrating the presence of two types of sensory hair cells in the ear of a telcost fish (Astronotus ocellatus, the oscar) indicates that hair cell heterogeneity may exist not only in amniotic vertebrates but also in anamniotes. Here we report that a similar heterogeneity between hair cell types may also occur in the other mechanosensory organ of the oscar, the lateral line. We exposed oscars to the aminoglycoside (ototoxic) antibiotic gentamicin sulfate and found damaged sensory hair cells in one class of the lateral line receptors, the canal neuromasts, but not in the other class, the superficial neuromasts. This effect was not due to the canal environment. Moreover, new ciliary bundles on hair cells of the canal neuromasts were found after, and during, gentamicin exposure. The pattern of hair cell destruction and recovery in canal neuromasts is similar to that of type 1-like hair cells found in the striolar region of the utricle and lagena of the oscar after gentamicin treatment. These results suggest that the hair cells in the canal and superficial neuromasts may be similar to type 1-like and type 2 hair cells, respectively, in the fish ear

Influences of Divalent Ions in Natural Seawater/River Water on Nanofluidic Osmotic Energy Generation

Besides the dominant NaCl, natural seawater/river water contains trace multivalent ions, which can provide effective screening to surface charges. Here, in both negatively and positively charged nanopores, influences from divalent ions as counterions and coions have been investigated on the performance of osmotic energy conversion (OEC) under natural salt gradients. As counterions, trace Ca2+ ions can suppress the electric power and conversion efficiency significantly. The reduced OEC performance is due to the bivalence and low diffusion coefficient of Ca2 ions, instead of the uphill transport of divalent ions discovered in the previous work. Effectively screened charged surfaces by Ca2+ ions induce enhanced diffusion of Cl ions which simultaneously decreases the net ion penetration and ionic selectivity of the nanopore. While as coions, Ca2+ ions have weak effects on the OEC performance. The promotion from charged exterior surfaces on OEC processes for ultra-short nanopores is also studied, which effective region is ~200 nm in width beyond pore boundaries independent of the presence of Ca2+ ions. Our results shed light on the physical details of the nanofluidic OEC process under natural seawater/river water conditions, which can provide a useful guide for high-performance osmotic energy harvesting.Comment: 24 pages, 5 figure

Patched Line Segment Learning for Vector Road Mapping

This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and predict a suitable line segment within each patch. This strategy enables us to capture spatial and structural cues from these patch-based line segments, simplifying the process of constructing the road network graph without the necessity of additional neural networks for connectivity. In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture. Furthermore, our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs in terms of GPU hours

Radio frequency fingerprint collaborative intelligent identification using incremental learning

For distributed sensor systems using neural networks, each sub-network has a different electromagnetic environment, and these recognition accuracy is also different. In this paper, we propose a distributed sensor system using incremental learning to solve the problem of radio frequency fingerprint identification. First, the intelligent representation of the received signal is linearly fused into a four-channel image. Then, convolutional neural network is trained by using the existing data to obtain the preliminary model of the network, and decision fusion is used to solve the problem in the distributed system. Finally, using new data, instead of retraining the model, we employ incremental learning by fine-tuning the preliminary model. The proposed method can significantly reduce the training time and is adaptive to streaming data. Extensive experiments show that the proposed method is computationally efficient, and also has satisfactory recognition accuracy, especially at low signal-to-noise ratio (SNR) regime

The lateral line system in the Florida gar, Lepisosteus platyrhincus de Kay.

This study represents a complete survey of the morphology and distribution of superficial and canal neuromasts, their innervation, and the central projections of rami of the lateral line nerves in a single species, the Florida gar (Lepisosteus platyrhincus). The methods used included scanning electron microscopy (SEM), gross dissection, histological analysis, and labeling with horseradish peroxidase (HRP). The results reveal that both superficial and canal neuromasts are collections of polarized hair cells, that receive afferent as well as efferent fibers from three pairs of lateral line nerves; centrally they terminate in an area of the octavolateralis nuclei in the medulla. There is no significant difference between these two kinds of mechanoreceptors. Superficial neuromasts are smaller and have fewer hair cells than canal neuromasts; however, the density of hair cells and the length of their kinocilia (presumably the height of the cupulae) are greater in superficial neuromasts than in canal neuromasts. This study also includes the first experimental confirmation of the existence of a pair of middle lateral line nerves in addition to the anterior and posterior lateral line nerves. Each has a distinct ganglion and root, and innervates one canal and superficial neuromasts of the middle pit line located in the temporal region. It is closely associated with the glossopharyngeal nerve within the cranium, but centrally it projects to the medial octavolateralis column (MON) of the medulla, to an area sandwiched between the terminal fields of the anterior and posterior lateral line nerves. Confirmation of the middle lateral line nerve, the discovery of subganglionic structures of the anterior and posterior lateral line ganglia in gars, and a review of the ontogenetic and phylogenetic data suggest that the earliest jawed vertebrate may have possessed six or seven pairs of lateral line nerves. Terminations of individual rami of the lateral line nerves suggest that spatial information from neuromasts on the rostrocaudal axis of the body is better preserved in the MON than neuromasts on the dorsoventral axis.Ph.D.Aquatic sciencesBiological SciencesMorphologyNeurosciencesZoologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/128452/2/9014017.pd

Seawater carbonate chemistry and early development and escape behavior of marine medaka (Oryzias melastigma)

Ocean acidification is predicted to affect a wide diversity of marine organisms. However, no studies have reported the effects of ocean acidification on Indian Ocean fish. We have used the Indian Ocean medaka (Oryzias melastigma) as a model species for a marine fish that lives in coastal waters. We investigated the impact of ocean acidification on the embryonic development and the stereotyped escape behavior (mediated by the Mauthner cell) in newly hatched larvae. Newly fertilized eggs of medaka were reared in seawater at three different partial pressures of carbon dioxide (pCO2): control at 450 μatm, moderate at 1160 μatm, and high at 1783 μatm. Hatching rates, embryonic duration, and larval malformation rates were compared and were not significantly different between the treatments and the control. In the high pCO2 group, however, the yolks of larvae were significantly smaller than in the control group, and the newly hatched larvae were significantly longer than the larvae in the control. In the moderate pCO2 group, the eye distance decreased significantly. No significantly negative growth effects were observed in the larvae when exposed to pCO2 levels that are predicted as a result of ocean acidification in the next 100–200 years. Larvae reared under control conditions readily produced C-start escape behavior to mechanosensory stimuli; however, in the moderate and high pCO2 experimental groups, the probabilities of C-start were significantly lower than those of the control group. Therefore, the sensory integration needed for the C-start escape behavior appears to be vulnerable to ocean acidification. Altered behavior in marine larval fish, particularly behaviors involved in escape from predation, could have potentially negative implications to fish populations, and, further, to the marine ecosystems at the levels of CO2 projected for the future

Research on the Spectral Reconstruction of a Low-Dimensional Filter Array Micro-Spectrometer Based on a Truncated Singular Value Decomposition-Convex Optimization Algorithm

Currently, the engineering of miniature spectrometers mainly faces three problems: the mismatch between the number of filters at the front end of the detector and the spectral reconstruction accuracy; the lack of a stable spectral reconstruction algorithm; and the lack of a spectral reconstruction evaluation method suitable for engineering. Therefore, based on 20 sets of filters, this paper classifies and optimizes the filter array by the K-means algorithm and particle swarm algorithm, and obtains the optimal filter combination under different matrix dimensions. Then, the truncated singular value decomposition-convex optimization algorithm is used for high-precision spectral reconstruction.In terms of spectral evaluation, due to the strong randomness of the target detected during the working process of the spectrometer, the standard value of the target spectrum cannot be obtained. Therefore, we adopt the method of joint cross-validation of multiple sets of data for spectral evaluation. The results show that when the random error of +/− 2 code values is applied multiple times for reconstruction, the spectral angle cosine value between the reconstructed curves becomes more than 0.995, which proves that the spectral reconstruction under this algorithm has high stability. At the same time, the spectral angle cosine value of the spectral reconstruction curve and the standard curve can reach above 0.99, meaning that it realizes a high-precision spectral reconstruction effect. A high-precision spectral reconstruction algorithm based on truncated singular value-convex optimization, is established in this paper, providing important scientific research value for the engineering application of micro-spectrometers

The algorithm research of low-rank matrix spectral reconstruction for ground targets

Recently, micro-spectrometer based on filter array has received extensive attention in terms of cost and size. Yet, the spectrometer will produce large noise in the work, which has a great impact on the spectral reconstruction. In this paper, a low-dimensional filter array is selected based on the K-means-PSO（Particle Swarm Optimization） method to achieve the purpose of data dimensionality reduction, which further reduces the cost and processing difficulty of the micro-spectrometer. To address the redundancy and poor accuracy of spectral reconstruction data obtained by micro-spectrometers, a convex optimization algorithm constrained by three-segment regularization of a low-rank-matrix (IReg-Cvx algorithm) was proposed for spectral reconstruction in this study. In order to test algorithm universality and stability better, we selected 120 kinds of ground spectral curves, and the low-dimensional filter array is fused with the IReg-Cvx algorithm. Apply the corresponding constraints according to the different slopes of the curve, and the high-quality spectral reconstruction of the ground object target spectrum can be stably realized under the noise environment of 30, 25, and 20 dB