Subjects -> ELECTRONICS (Total: 207 journals)
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 Sensing and Imaging : An International JournalJournal Prestige (SJR): 0.255 Citation Impact (citeScore): 1Number of Followers: 2      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1557-2072 - ISSN (Online) 1557-2064 Published by Springer-Verlag  [2469 journals]
• Carcinogenic Chromium (VI) Sensing Using Transducing Characteristics of
Fiber Bragg Grating and Physical Swelling of Hydrogel

Abstract: Abstract A Chemo-mechanical-optical sensing approach for the detection of hexagonal chromium (Cr6+) metal ion is demonstrated. A new sensor head is designed by epoxying fiber Bragg grating (FBG) on a thin silicon membrane beneath which a Chromium (VI) responsive hydrogel is embedded. When the gel is exposed to chromium spiked solutions, it suffers a volume change due to its stimulus responsive property and deforms a silicon membrane which in turn causes a wavelength peak shift of FBG. Hydrogel synthesized from the blends of (3-Acrylamidopropyl)—trimethylammonium chloride is used for the purpose. The relation between FBG peak shifts with change in volume of hydrogel due to it swelling is experimentally established. The FBG wavelength peak shift is directly correlated with the concentration of the Cr (VI) metal ion. The estimated sensitivity and resolution of the sensor are 0.1 nm/ppb with a limit of detection of the sensor is 0.75 ppb. The sensor has demonstrated good sensitivity, selectivity, and repeatability.
PubDate: 2022-08-05

• Simulation of Optical Hollow Microbottle Resonator for Sensing
Applications

Abstract: Abstract A silica hollow microbottle resonator (HMBR) combined with a pair of curved silicon micro-mirrors on the outside wall of the microbottle is proposed and numerically investigated using the Finite-Difference Time-Domain (FDTD) algorithm. The microbottle has only 32 μm length, 26 μm width and 1.5 μm wall thickness. The curved micro-mirrors overcome the light diffraction loss through light focusing, while the microbottle, in which gas analytes are introduced, provides additional light confinement and hence improves the performance of the sensor. The obtained Q-factor is about 4590 at 1543.21 nm and the free spectral range (FSR) is more than 31 nm. An internal sensitivity of 1567 nm per refractive index unit (RIU) is achieved in the near-infrared (NIR), which is the highest ever reported for an refractive index (RI) gas sensor based on HMBR. With the introduction of an air gap layer between the silica HMBR and the silicon micro-mirrors, both the Q-factor and sensitivity have been improved to 6729 and 1730 nmRIU− 1 respectively. We believe that the proposed architecture will be used in future sensing applications.
PubDate: 2022-08-03

• Correction to: Adaptive Higher‑Order Spectral Analysis for Image
Recovery Under Distortion of Moving Water Surface Using
Dragonfly‑Colliding Bodies Optimization

PubDate: 2022-07-20

• Motion Capture Sensing Technologies and Techniques: A Sensor Agnostic

Abstract: Abstract Body area sensing systems specifically designed for motion capture need to consider the wearer’s comfort and wearability criteria. In this paper, after studying body models and their approximation by link-segment models, the kinematics and inverse kinematics problems for determining motion are explored. Different sensor technologies and related motion capture systems are then discussed within the context of wearability and portability challenges of the systems. For such systems, the weight and size of the system need to be kept small and the system should not interfere with the user’s movements. The requirements will be considered in terms of portability: portable motion capture systems should be less sensitive in accurate positioning of sensors and have more battery lifetime or less power consumption for their wider adoption as an assisted rehabilitation platform. Therefore, a proposed signal processing technique is validated in a controlled setting to address the challenges. By reducing sampling frequency, the power consumption will be reduced but there would be more variability in data whereas by utilising an adaptive filtering approach the variation can be compensated for. It is shown how by using the technique it is possible to reduce the energy consumption; therefore, the potential to decrease the battery size leading to a less bulky on-body sensing system with more comfort to the wearer.
PubDate: 2022-07-20

• Modeling Electrical Resistance Behavior of Soft and Flexible
Piezoresistive Sensors Based on Carbon-Black/Silicone Elastomer Composites

Abstract: Abstract Soft and flexible strain piezoresistive sensors are gaining interest in wearable and robotic applications, but resistance relaxation limits the widespread use of the sensors. As soft, flexible, and stretchable sensors, they can easily be retrofitted into any existing robotic hand. To understand the resistance relaxation of stretchable sensors, three different elastomers were used to fabricate soft piezoresistive sensors. The experimental results showed that the sensor has good linearity and scalability while their resistance is strongly influenced by the stretching speed and modulus of the elastomer. Thus, the Kevin Voigt model was adopted to describe the sensor’s change of resistance during the stretching process. The model is sufficient to describe the change of resistance of the carbon black/elastomer filler when the sensors are stretched before the fracturing of the conductive filler. However, when the filler fractures, the model is invalid. The behavior indicates that the elongation of the sensor must not exceed the strain that causes the filler to fracture.
PubDate: 2022-07-01

• BI-LSTM Based Encoding and GAN for Text-to-Image Synthesis

Abstract: Abstract Synthesizing images from text is to produce images with reliable content as specified text depiction that is an extremely demanding task with the most important problems like: content consistency and visual realism. Owing to considerable progression of GAN, it is now possible to produce images with good visual certainty. The translation of text descriptions to images with higher content reliability, on the other hand, is still a work in progress. This paper intends to frame a novel text-to-image synthesis approach, which includes two major phases namely; (1) Text to image encoding and (2) GAN. Initially, during text to image encoding, cross modal feature alignment takes place including text and image features. Consequently, BI-LSTM is deployed to transfer the text embedding to feature vector. At second stage, the image is synthesized based on the encoding. Consequently, text feature group are given as input to GAN, which offers the final synthesized images. Finally, the supremacy of developed approach is examined via evaluation over extant techniques.
PubDate: 2022-07-01

• Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled
Neural Network Based Consistency Verification

Abstract: Abstract Multi-focus image fusion technique is an important approach to generate a composite image with all objects in focus. Accurate focused pixel detection from multiple source images is crucial for multi-focus image fusion. However, false detection of focused pixels is inevitably due to the low-level image features being usually used to achieve focus pixel classification in most fusion methods. Consistency verification operation is frequently used to revise the falsely detected focused pixels in many fusion schemes. However, most consistency verification strategies cannot achieve the desired results. In this paper, we modify the parameter adaptive pulse coupled neural network (PA-PCNN) by introducing a new strategy to measure the linking strength of neurons. Thus, the PA-PCNN can greatly improve the accuracy of identifying focused pixels. The proposed method contains four steps. Firstly, the residual between an image and its filtered version by efficient mean filter is used to calculate the sharpness of a source image. Then, a new consistency verification method based on adaptive pulse coupled neural network (PA-PCNN) is adopted to improve the accuracy of the initial sharpness. Next, the focus detection maps are constructed by comparing the refined sharpness of two source images. Finally, the fused image is constructed according to the focus detection map. Experimental results show that the proposed method has achieved comparable or even better results compared with the state-of-the-art approaches in both visual quality and objective evaluation.
PubDate: 2022-06-29

• Floating Point Implementation of the Improved QRD and OMP for Compressive
Sensing Signal Reconstruction

Abstract: Abstract In this paper, the Floating-Point Core Architecture based QR decomposition is proposed for solving least square problems in the Orthogonal Matching Pursuit algorithm (OMP-FPCA-QRD). To improve the computational performance of Orthogonal Matching Pursuit (OMP), it is necessary to modify the Orthogonal Matching Pursuit algorithm for analysing a wide range of signals in field programmable gate array (FPGA). As a result, it highly benefits from the available resources and acquires a scalable computational complexity. Since the solution of least square problem involves some iterative parts, like square root and division units, the processing time of the proposed QR Decomposition (QRD) approach is decreased by increasing parallelism using processing element driven systolic array implementation across all data-dependent operations. The hardware implementation on the ALTERA field programmable gate array shows optimal performance depends on hardware complexity and frequency of operation with the improved computational accuracy over existing QR Decomposition implementations. Moreover, the implementation of Orthogonal Matching Pursuit algorithm for signal reconstruction is also proposed to validate the performance metrics of floating point unit (FPU). The experimental results show that the optimization of floating point unit offers significant resource optimization in QR decomposition, and also better performance of high peak signal-to-noise ratio of 32.99 dB, which outperforms all other fixed point Orthogonal Matching Pursuit systems.
PubDate: 2022-06-26

• Adaptive Higher-Order Spectral Analysis for Image Recovery Under
Distortion of Moving Water Surface using Dragonfly-Colliding Bodies
Optimization

Abstract: Abstract Reconstruction of an underwater object from a sequence of images distorted by moving water waves is a challenging task. Most of the environmental research has been employing image data in recent days. The precision of this research is often dependent on the superiority of image data. In the existing approaches, the problem of analyzing video sequences when the water surface is disturbed by waves. The water waves will affect the appearance of the individual video frames such that no single frame is completely free of geometric distortion. Thus, the image acquisition from the environmental condition is more complex and crucial, but it must be focused on getting the high spectral and spatial quality. The primary intent of this paper is to plan for the intelligent higher-order spectral analysis for recovering the images from the moving water surface. The three main phases of the proposed image recovery model are (a) image pre-processing, (b) lucky region selection, and (c) image recovery. Once the pre-processing of the image is carried out, the lucky region selection is performed by computing the dice coefficient method. As a modification to the existing methods, the proposed model adopts optimized bispectra to enhance the quality of the recovered image. A hybrid algorithm with Dragonfly-Colliding Body Optimization (D-CBO) is used for enhancing the bispectra method. The proposed model has been tested on distorted underwater images. From the experimental analysis, in terms of PSNR measure, the suggested D-CBO-bispectra gets better efficiency than other conventional models, in which D-CBO-bispectra is 10.7%, 8.7%, 19%, 6.8% and 5% progressed than Blind deconv, Bispectra, Bispectra with Fourier, and Radon transform, respectively. Finally, the comparison of the proposed model with the existing approaches proves the method's efficiency.
PubDate: 2022-05-17
DOI: 10.1007/s11220-022-00388-0

• Electric Resistance Tomograph (ERT): a review as non-destructive Tool
(NDT) in deciphering interiors of standing trees

Abstract: Abstract The Electric Resistance Tomograph (ERT) is a customized tree specific novel German-technology which was developed to monitor and estimate the tree growth development by looking into the inner structure of the tree to analyse the growth and health status. This technique contributes to detect and study the internal assembly of a tree for the mapping of decay, hollowness, and also to distinguish the sapwood and heartwood demarcation, this way of discovering the internal growth at an early stage, mainly for the timber trees which are economically important can help to regulate thereafter to check whether the growth is not hindered. This paper highlights the device operational methods, electric resistance testing of trees and its applications, also reviewed the various successful application of this equipment in various tree species through-out the world to estimate non-destructively for accurate quantification of standing trees. The performance of this instrument has created breakthrough among various studies and methodologies to spot the internal condition in a standing tree with less invasive ways.
PubDate: 2022-05-11
DOI: 10.1007/s11220-022-00385-3

• A Dynamic Image Encryption Algorithm Based on Improved Ant Colony Walking
Path Thought

Abstract: Abstract According to the existing chaotic image encryption technology, this paper designs an algorithm based on the thought of improved ant colony walking path to encrypt the image. In this paper, the key is generated using SHA-512, and the required chaotic sequence is generated using the PWLCM one-dimensional chaotic system. First, the image is scrambled by the row and column indexes by the improved algorithm so that complete the first scrambling. Then, the image is converted into one-dimensional array, and randomly scrambled according to the designed algorithm. Finally, the one-dimensional array is divided into two segments with the middle point serving as the boundary, and each segment is created using distinct rules based on the array’s number and parity to complete the diffusion, after which the array is restored to its original size. Simulation results show that this algorithm has better performance and robustness in encryption compared with other algorithms.
PubDate: 2022-05-03
DOI: 10.1007/s11220-022-00387-1

• DSSEMFF: A Depthwise Separable Squeeze-and-excitation Based on
Multi-feature Fusion for Image Classification

Abstract: Abstract Image classification refers to the classification of the input image according to some algorithms. The general steps of image classification include image preprocessing, image feature extraction and image classification judgment. Convolutional neural network (CNN) imitates the visual perception mechanism of biology, solves the complicated engineering of traditional manual feature extraction, and realizes automatic feature extraction from data. However, CNN still has the disadvantages of low efficiency and incomplete feature extraction. In this paper, we propose a depthwise separable squeeze-and-excitation based on multi-feature fusion (DSSEMFF) for image classification. Through feature fusion of multiple models, the network can learn the input features with different levels of images, increase feature complementarity and improve feature extraction ability. By adding the attention module, the network can pay more attention to the targeted area and reduce irrelevant background interference information. Finally, we conduct experiments with other state-of-the-art classification methods, the accuracy is higher than 90% and the error rate is lower than 18% the results show that the effectiveness of the proposed method obtains the better effect.
PubDate: 2022-05-03
DOI: 10.1007/s11220-022-00383-5

• Quality of Experience Evaluation Model with No-Reference VMAF Metric and
Deep Spatio-temporal Features of Video

Abstract: Abstract For mobile streaming media service providers, it is necessary to accurately predict the quality of experience (QoE) to formulate appropriate resource allocation and service quality optimization strategies. In this paper, a QoE evaluation model is proposed by considering various influencing factors (IFs), including perceptual video quality, video content characteristics, stalling, quality switching and video genre attribute. Firstly, a no-reference video multimethod assessment fusion (VMAF) model is constructed to measure the perceptual quality of the video by the deep bilinear convolutional neural network. Then, the deep spatio-temporal features of video are extracted using a TSM-ResNet50 network, which incorporates temporal shift module (TSM) with ResNet50, obtaining feature representation of video content characteristics while balancing computational efficiency and expressive ability. Secondly, video genre attribute, which reflects the user’s preference for different types of videos, is considered as a IF while constructing the QoE model. The statistical parameters of other IFs, including the video genre attribute, stalling and quality switching, are combined with VMAF and deep spatio-temporal features of video to form an overall description parameters vector of IFs for formulating the QoE evaluation model. Finally, the mapping relationship model between the parameters vector of IFs and the mean opinion score is established through designing a deep neural network. The proposed QoE evaluation model is validated on two public video datasets: WaterlooSQoE-III and LIVE-NFLX-II. The experimental results show that the proposed model can achieve the state-of-the-art QoE prediction performance.
PubDate: 2022-04-18
DOI: 10.1007/s11220-022-00386-2

• SSRNet: A CT Reconstruction Network Based on Sparse Connection and Weight
Sharing for Parameters Reduction

Abstract: Abstract In recent years, the neural networks are frequently adopted to address the issues of cone beam CT imaging. However, most of the research so far has been to build neural networks individually either in the image domain or in the projection domain for specific purposes, while the connections between them are not well exploited. A reconstruction network that focuses on the filtering and backward projection process can fully exploit the potential connections between the projection domain and the image domain, and can be plugged into other learned reconstruction models for high-quality results. However, until now most of the existing reconstruction networks have taken the fully-connected layer as the backbone, which leads to an explosive growth in model parameters, especially in reconstruction of cone-beam CT. In this paper, a sparse-sharing-reconstruction network (SSRNet) with sparse connections and multi-group weight sharing is proposed, which can be regarded as a substitute of the filtering and backward projection process and can significantly reduce the number of network parameters up to 0.4% of that of the previous models. The experimental results show that the reconstruction results of the 2D SSRNet are basically consistent with those of the traditional FBP algorithm. The 3D SSRNet outperforms the traditional FDK algorithm at 50 layers away from the midplane, while still keeping the number of network parameters within a manageable range.
PubDate: 2022-03-28
DOI: 10.1007/s11220-022-00384-4

• Optimized Cantilever Sensor Based on Parallel High Dielectric Material

Abstract: Abstract Cantilever is dramatically used as a resonator sensor to detect the presence of a particular molecule or cell in an environment and to measure their amount. Electrostatic force is commonly used to actuate MEMS based cantilevers to resonate because a cantilever has a simple capacitor structure. In this paper, a novel design is proposed to optimize the cantilevers performance by use of high dielectric material in its capacitor structure. The mathematical model of the proposed design and the performance of the cantilever sensor and its quality has been evaluated and reported by finite element simulations in this way.
PubDate: 2022-03-22
DOI: 10.1007/s11220-022-00381-7

• A Framework for Super Resolution of Color Images with Missing Samples
Using Low Rank Approximation

Abstract: Abstract Images captured from extreme surveillance environments may contain missing pixels because of the damaged sensors and dusty particles including debris from combustion fuels and spider webs. This paper addresses the challenging problem of super resolving color images from low resolution images with missing sample values. In conventional methods, missing pixel estimation and super resolution are carried out separately. Unlike the conventional techniques, the proposed method performs simultaneous missing pixel estimation, super resolution and color reconstruction in a unified framework. The problem is posed in a regularized optimization framework with a suitable objective function. The formulated objective function consists of a weighted data fidelity term to control the contribution from each pixel position, a nuclear norm regularization term for enforcing image completion, a three dimensional total variation term for ensuring edge consistency and a sparsity based color regularization term for enforcing proper color reconstruction. Significance of each terms in the regularization function is studied and the results are presented. Experimental results show that proposed method gives improved performance than the conventional methods in most of the cases.
PubDate: 2022-03-16
DOI: 10.1007/s11220-022-00380-8

• A Comparative Analysis of the Algorithms for De-noising Images
Contaminated with Impulse Noise

Abstract: Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively.
PubDate: 2022-03-11
DOI: 10.1007/s11220-022-00382-6

• Design and Simulation of pH-ISFET Readout Circuit for Low Thermal
Sensitivity Applications Through an Automatic Selection of an Isothermal
Point

Abstract: Abstract The purpose of this paper is to present a high-performance pH-ISFET readout circuit, which carries out a temperature insensitivity, linearity and temporal drift compensation, by using a new architecture that automates the control of an isothermal point. Unlike many existing readout circuits in the literature, this circuit can be optimized for several isothermal pH values as desired and for any structure compatible with the standard ISFET sensor. To eliminate the effect of the temporal drift, generally observed in ISFET type sensors, the same readout circuit was used in conjunction with Machine Learning (ML) implementation. The ML model was trained using a dataset from simulations performed using the ISFET macro-model including the drift effect. Through simulations, we show that the proposed scheme reduces drastically the temperature sensitivity of the sensor to less than $$1.5\times 10^{-4}\,{\mathrm{pH}}/^{\circ }{\mathrm{C}}$$ for pH $$\pm \,2$$ around any isothermal point at a wide pH range (from 1 to 12). For small changes of the pH around the isothermal point, the readout circuit outperforms many other designs with a thermal sensibility of less than $$3.2\times 10^{-6}\,{\mathrm{pH}}/^\circ {\mathrm{C}}$$ . Results show that the system was able to predict the long-term behavior of the pH-ISFET (several days) with a relative error, of the output, that not exceed $$0.19\%$$ for the 3-sigma testing.
PubDate: 2022-03-03
DOI: 10.1007/s11220-022-00378-2

• Hand Detection by Two-Level Segmentation with Double-Tracking and Gesture
Recognition Using Deep-Features

Abstract: Vision-based hand gesture recognition involves a visual analysis of handshape, position and/or movement. Most of the previous approaches require complex gesture representation as well as the selection of robust features for proper gesture recognition. To eliminate the problem of illumination variation and occlusion in gesture videos, a simple model-based framework has been presented here using a deep network for hand gesture recognition. The model is fed with ‘hand-trajectory-based-contour-images’. These images represent the motion trajectory of the hand for isolated trajectory gestures obtained via pre-processing steps—a two-level segmentation process and a double-tracking system. Deep features extracted from these images are used for estimating the hand gestures. Conventional machine learning methods involve tedious feature engineering schemes, while deep learning approaches can learn image features hierarchically from local to global with multiple layers of abstraction from a vast number of raw sample images. The feature learning capability of CNN architecture has been used here and it has shown outstanding results on three different datasets.
PubDate: 2022-02-22
DOI: 10.1007/s11220-022-00379-1

• RE-SHFC: Renyi Entropy-Based Spotted Hyena Fractional Calculus Algorithm
for MR Image Reconstruction

Abstract: Abstract Magnetic Resonance Imaging (MRI) is a powerful non-invasive procedure for imaging that offers critical functional, structural, anatomical details about a patient. However, the maximum time needed to scan the whole process causes motion artifacts that may worsen the image quality and result in data distortion and patient discomfort. Hence, an effective mechanism is designed for the reconstruction of MR images. Here, the MR image is gathered from the MRI dataset in the MR image acquisition module. Once the MR image is identified, it is given to the sub-sampling module. After sampling the MR image, it is further given to the weighted compressive sensing module, which is performed using the proposed optimization algorithm, named Renyi Entropy-based Spotted Hyena Fractional Calculus algorithm (RE-SHFC) to get the final reconstructed image. Here, the RE-SHFC is devised by combining Renyi Entropy (RE) measure, and the SHFC algorithm. SHFC is the integration of Spotted Hyena Optimizer and Fractional Calculus. The proposed RE-SHFC algorithm outperformed other methods with minimal Mean Square Error, maximal Peak Signal-to-Noise-ratio, and maximal Structural Similarity Index Module.
PubDate: 2022-02-16
DOI: 10.1007/s11220-022-00377-3

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