Bio-Inspired Systems and Signal Processing

Topic Information

Dear Colleagues,

Functional biological systems, from small cells to large biological individuals, frequently generate and convey signals in various forms, including but not limited to image, audio, electrophysiological, mechanical, and chemical signals. There is huge research interest in transforming the models and techniques inspired by these biological systems to have science and engineering applications. To achieve these applications, the comprehensive signal analysis of these bio-inspired systems is necessary, which requires the integration of multiple disciplinary fields, including signal acquisition/sensing/amplification, signal processing, image analysis, pattern recognition/classification, Artificial neural networks (ANNs), and artificial intelligence. These bio-inspired systems have been widely used to tackle various practical challenges, including the COVID-19 pandemic, where many types of sensors and biosensors were proposed to improve the speed, sensitivity and specificity of virus detection by presenting and amplifying various forms of signals and symptoms. This Special Issue encourages the authors to submit original research papers on one or more of the following topics: bio-inspired system modeling and design, sensors, biosensors, signal processing, image analysis, and pattern recognition. We particularly welcome submissions that integrate advanced artificial intelligence algorithms in a complex bio-inspired system that could include both hardware and software, and/or bio-inspired systems that can successfully tackle the current practical challenges (e.g., COVID-19, monkeypox).

Prof. Dr. Donald Y.C. Lie
Prof. Dr. Chung-Chih Hung
Dr. Jian Xu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Biosensors biosensors	4.9	6.6	2011	18.9 Days	CHF 2200
Journal of Imaging jimaging	2.7	5.9	2015	18.3 Days	CHF 1800
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Signals signals	-	3.2	2020	28.3 Days	CHF 1000
Chips chips	-	-	2022	22.2 Days	CHF 1000

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Published Papers (8 papers)

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All Applied Sciences Biosensors J. Imaging Sensors Signals Chips

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15 pages, 1271 KiB  

Open AccessArticle

An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition

by Jin Zhang, Haobo He, Wei Li, Lidan Kuang, Fei Yu and Jiajia Zhao

Appl. Sci. 2024, 14(1), 87; https://doi.org/10.3390/app14010087 - 21 Dec 2023

Viewed by 1116

Abstract

In human and other organisms’ perception, olfaction plays a vital role, and biomimetic olfaction models offer a pathway for studying olfaction. The most optimal existing biomimetic olfaction model is the KIII model proposed by Professor Freeman; however, it still exhibits certain limitations. This [...] Read more.

In human and other organisms’ perception, olfaction plays a vital role, and biomimetic olfaction models offer a pathway for studying olfaction. The most optimal existing biomimetic olfaction model is the KIII model proposed by Professor Freeman; however, it still exhibits certain limitations. This study aims to address these limitations: In the feature extraction stage, it introduces adaptive histogram equalization, Gaussian filtering, and discrete cosine transform methods, effectively enhancing and extracting high-quality image features, thereby bolstering the model’s recognition capabilities. To tackle the computational cost issue associated with solving the numerical solutions of neuronal dynamics equations in the KIII model, it replaces the original method with the faster Euler method, reducing time expenses while maintaining good recognition results. In the decision-making stage, several different dissimilarity metrics are compared, and the results indicate that the Spearman correlation coefficient performs best in this context. The improved KIII model is applied to a new domain of traffic sign recognition, demonstrating that it outperforms the baseline KIII model and exhibits certain advantages compared to other models. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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12 pages, 2201 KiB  

Open AccessArticle

Oscillatory Responses to Tactile Stimuli of Different Intensity

by Alexander Kuc, Ivan Skorokhodov, Alexey Semirechenko, Guzal Khayrullina, Vladimir Maksimenko, Anton Varlamov, Susanna Gordleeva and Alexander Hramov

Sensors 2023, 23(22), 9286; https://doi.org/10.3390/s23229286 - 20 Nov 2023

Cited by 6 | Viewed by 2016

Abstract

Tactile perception encompasses several submodalities that are realized with distinct sensory subsystems. The processing of those submodalities and their interactions remains understudied. We developed a paradigm consisting of three types of touch tuned in terms of their force and velocity for different submodalities: [...] Read more.

Tactile perception encompasses several submodalities that are realized with distinct sensory subsystems. The processing of those submodalities and their interactions remains understudied. We developed a paradigm consisting of three types of touch tuned in terms of their force and velocity for different submodalities: discriminative touch (haptics), affective touch (C-tactile touch), and knismesis (alerting tickle). Touch was delivered with a high-precision robotic rotary touch stimulation device. A total of 39 healthy individuals participated in the study. EEG cluster analysis revealed a decrease in alpha and beta range (mu-rhythm) as well as theta and delta increase most pronounced to the most salient and fastest type of stimulation. The participants confirmed that slower stimuli targeted to affective touch low-threshold receptors were the most pleasant ones, and less intense stimuli aimed at knismesis were indeed the most ticklish ones, but those sensations did not form an EEG cluster, probably implying their processing involves deeper brain structures that are less accessible with EEG. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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12 pages, 2836 KiB  

Open AccessArticle

A Simple Denoising Algorithm for Real-World Noisy Camera Images

by Manfred Hartbauer

J. Imaging 2023, 9(9), 185; https://doi.org/10.3390/jimaging9090185 - 18 Sep 2023

Cited by 3 | Viewed by 4778

Abstract

The noise statistics of real-world camera images are challenging for any denoising algorithm. Here, I describe a modified version of a bionic algorithm that improves the quality of real-word noisy camera images from a publicly available image dataset. In the first step, an [...] Read more.

The noise statistics of real-world camera images are challenging for any denoising algorithm. Here, I describe a modified version of a bionic algorithm that improves the quality of real-word noisy camera images from a publicly available image dataset. In the first step, an adaptive local averaging filter was executed for each pixel to remove moderate sensor noise while preserving fine image details and object contours. In the second step, image sharpness was enhanced by means of an unsharp mask filter to generate output images that are close to ground-truth images (multiple averages of static camera images). The performance of this denoising algorithm was compared with five popular denoising methods: bm3d, wavelet, non-local means (NL-means), total variation (TV) denoising and bilateral filter. Results show that the two-step filter had a performance that was similar to NL-means and TV filtering. Bm3d had the best denoising performance but sometimes led to blurry images. This novel two-step filter only depends on a single parameter that can be obtained from global image statistics. To reduce computation time, denoising was restricted to the Y channel of YUV-transformed images and four image segments were simultaneously processed in parallel on a multi-core processor. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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18 pages, 5233 KiB  

Open AccessArticle

Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence Algorithm-Based Feature Selection Methods

by Yue Zhang, Maoxun Sun, Chunming Xia, Jie Zhou, Gangsheng Cao and Qing Wu

Sensors 2023, 23(15), 6939; https://doi.org/10.3390/s23156939 - 4 Aug 2023

Cited by 1 | Viewed by 1458

Abstract

Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorithm (GOA) are [...] Read more.

Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorithm (GOA) are proposed for the pattern recognition of knee and ankle movements in the sitting and standing positions. Wireless multichannel MMG acquisition systems were designed and used to collect MMG movements from four sites on the subjects thighs. The relationship between the threshold values and classification accuracy was analyzed, and comparatively high recognition rates were obtained after redundant information was eliminated. When the threshold value rose, the recognition rates from the CSA fluctuated within a small range: up to 88.17% (sitting position) and 90.07% (standing position). However, the recognition rates from the GOA drop dramatically when increasing the threshold value. The comparison results demonstrated that using a GOA consumes less time and selects fewer features, while a CSA gives higher recognition rates of knee and ankle movements. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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16 pages, 2307 KiB  

Open AccessArticle

A Comparative Study on Cyanine Dyestuffs as Sensor Candidates for Macromolecular Crowding In Vitro and In Vivo

by Leon Koch, Roland Pollak, Simon Ebbinghaus and Klaus Huber

Biosensors 2023, 13(7), 720; https://doi.org/10.3390/bios13070720 - 8 Jul 2023

Cited by 2 | Viewed by 1858

Abstract

Pseudo isocyanine chloride (PIC) has been identified in a preceding work as a sensor suited to probe macromolecular crowding both in test tubes with solutions of synthetic crowding agents and in HeLa cells as a representative of living systems. The sensing is based [...] Read more.

Pseudo isocyanine chloride (PIC) has been identified in a preceding work as a sensor suited to probe macromolecular crowding both in test tubes with solutions of synthetic crowding agents and in HeLa cells as a representative of living systems. The sensing is based on a delicate response of the self-assembly pattern of PIC towards a variation in macromolecular crowding. Based on a suitable selection of criteria established in the present study, four additional cyanine dyestuffs (TDBC, S071, S2275, and PCYN) were scrutinized for their ability to act as such a sensor, and the results were compared with the corresponding performance of PIC. UV-VIS and fluorescence spectroscopy were applied to investigate the photo-physical properties of the four candidates and, if possible, light scattering was used to characterize the self-assembly of the dyestuffs in solution. Finally, HeLa cells were exposed to solutions of the most promising candidates in order to analyze their ability to infiltrate the cells and to self-assemble therein. None of the dyestuff candidates turned out to be as similarly promising in probing crowding effects in cells as PIC turned out to be. S0271 and S2275 are at least stable enough and meet the photophysical requirements necessary to act as sensors responding to changes in macromolecular crowding. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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30 pages, 2814 KiB  

Open AccessArticle

Implementation of a Bio-Inspired Neural Architecture for Autonomous Vehicles on a Multi-FPGA Platform

by Tarek Elouaret, Sylvain Colomer, Frédéric De Melo, Nicolas Cuperlier, Olivier Romain, Lounis Kessal and Stéphane Zuckerman

Sensors 2023, 23(10), 4631; https://doi.org/10.3390/s23104631 - 10 May 2023

Cited by 2 | Viewed by 3231

Abstract

Autonomous vehicles require efficient self-localisation mechanisms and cameras are the most common sensors due to their low cost and rich input. However, the computational intensity of visual localisation varies depending on the environment and requires real-time processing and energy-efficient decision-making. FPGAs provide a [...] Read more.

Autonomous vehicles require efficient self-localisation mechanisms and cameras are the most common sensors due to their low cost and rich input. However, the computational intensity of visual localisation varies depending on the environment and requires real-time processing and energy-efficient decision-making. FPGAs provide a solution for prototyping and estimating such energy savings. We propose a distributed solution for implementing a large bio-inspired visual localisation model. The workflow includes (1) an image processing IP that provides pixel information for each visual landmark detected in each captured image, (2) an implementation of N-LOC, a bio-inspired neural architecture, on an FPGA board and (3) a distributed version of N-LOC with evaluation on a single FPGA and a design for use on a multi-FPGA platform. Comparisons with a pure software solution demonstrate that our hardware-based IP implementation yields up to $9\times$ lower latency and $7\times$ higher throughput (frames/second) while maintaining energy efficiency. Our system has a power footprint as low as 2.741 W for the whole system, which is up to 5.5–6× less than what Nvidia Jetson TX2 consumes on average. Our proposed solution offers a promising approach for implementing energy-efficient visual localisation models on FPGA platforms. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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9 pages, 419 KiB  

Open AccessCommunication

Feedback-Controlled Adaptive Signal Detection Scheme for Diffusion-Based Molecular Communication Systems

by Heejung Byun

Appl. Sci. 2023, 13(4), 2171; https://doi.org/10.3390/app13042171 - 8 Feb 2023

Cited by 3 | Viewed by 1540

Abstract

This paper proposes a feedback-controlled adaptive method for detecting signals in diffusion-based molecular communication (MC) systems. Signal detection via a receiver nanomachine is a critical challenge for the exchange of information in MC systems. Incorrect estimations or small errors in signal detection can [...] Read more.

This paper proposes a feedback-controlled adaptive method for detecting signals in diffusion-based molecular communication (MC) systems. Signal detection via a receiver nanomachine is a critical challenge for the exchange of information in MC systems. Incorrect estimations or small errors in signal detection can lead to high data detection errors. Existing methods for improving detection performance require high time costs or computational complexity. This paper proposes a simple and practical method that enables receiver nanomachines to automatically estimate signal detection times according to the measured molecular concentrations and weighted feedback errors. The proposed method adjusts the detection time even when the initial parameter values of the system are unknown to the receiver nanomachines. Simulations were performed to evaluate the bit error rate performance of the proposed and existing methods in terms of different data rates, transmission distances, and estimation error lengths under different initial conditions. The simulation results reveal that the implementation of the proposed method is simpler and demonstrates superior performance compared with that of existing methods. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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15 pages, 9026 KiB  

Open AccessArticle

Low-Noise, Low-Power Readout IC for Two-Electrode ECG Recording Using Common-Mode Charge Pump for Robust 20-V_PP Common-Mode Interference

by Kyeongsik Nam, Gyuri Choi, Mookyoung Yoo, Sanggyun Kang, Byeongkwan Jin, Hyeoktae Son, Kyounghwan Kim and Hyoungho Ko

Appl. Sci. 2022, 12(24), 12897; https://doi.org/10.3390/app122412897 - 15 Dec 2022

Cited by 1 | Viewed by 2439

Abstract

A low-noise and -power readout integrated circuit (IC) for two-electrode electrocardiogram (ECG) recording is developed in this study using a common-mode charge pump (CMCP) for a robust 20-V_PP common-mode interference (CMI). Two-electrode ECG recording offers more comfort than three-electrode ECG recording. Contrasting [...] Read more.

A low-noise and -power readout integrated circuit (IC) for two-electrode electrocardiogram (ECG) recording is developed in this study using a common-mode charge pump (CMCP) for a robust 20-V_PP common-mode interference (CMI). Two-electrode ECG recording offers more comfort than three-electrode ECG recording. Contrasting to the three-electrode ECG recording, the two-electrode ECG recording is affected by CMI during measurements; the intervention of a large CMI will distort the ECG signal measurement. To achieve robustness for the CMI, the proposed ECG readout IC adopts CMCP—it uses switched capacitors that store and subtract CMI by control logic. In this paper, a window comparator structure is applied to CMCP to obtain a signal with less distortion. The window voltage ranges were set between the input common-mode ranges in which IA can operate. Therefore, a signal with less distortion was obtained by stopping the operation of CMCP between the window voltage ranges. It also reduced additional current consumption. To achieve this, the proposed circuit is implemented using a chopper stabilization technique. The chopper implemented in the amplifier can reduce low-frequency noise components, such as 1/f noise, and it comprises a CMCP, current feedback instrumentation amplifier, QRS peak detector, relaxation oscillator, voltage reference, timing generator, and serial peripheral interface on a single chip. The proposed circuit was designed using a standard 0.18 μm CMOS process with an active area of 0.54 mm². The proposed CMCP achieves a CMI robustness of 20 V_PP at 60 Hz. The measured input-referred noise level was 119 nV/√Hz at 1 Hz, and the power consumption was 23.83 μW with a 1.8 V power supply. Full article

(This article belongs to the Topic Bio-Inspired Systems and Signal Processing)

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