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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 47718

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Dr. Pedro Peris-Lopez

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Department of Computer Science, Universidad Carlos III de Madrid, 28903 Getafe, Madrid, Spain
Interests: hardware security; cryptography; signal processing
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Special Issue Information

Dear Colleagues,

We live in an era where real-time information acquisition is crucial, and much of this information is provided by small devices called sensors. The use of these sensors is highly varied, and they are used in areas as diverse as the agriculture, health, and military sectors, to name a few. In the past, sensors had very constrained computing capabilities, so their functionality was mainly limited to data acquisition and transmission. Energy consumption is often also a determining factor in these devices.

Today, some sensors have signal processing capabilities and are called intelligent sensors. On the other hand, the intelligence of these sensors (or of the systems in which they are integrated) can be improved with artificial intelligence techniques. In addition, it is also a current trend to merge information from various types of sensors to have a richer and more reliable system in decision-making.

This Special Issue tries to bring together all the latest developments in the area of intelligent sensors systems. The topics of this issue include but are not limited to the following:

signal processing for intelligent sensors
artificial intelligence for intelligent sensors
Big Data for intelligent sensors systems
security and privacy for intelligent sensors
cybersecurity for intelligent sensors
low-cost solutions for intelligent sensors
hardware design and solutions for intelligent sensors
e-health and intelligent sensors
intelligent sensors in the biomedical context
new trends and applications for intelligent sensors

Dr. Pedro Peris-Lopez
Guest Editor

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Published Papers (11 papers)

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Research

14 pages, 2149 KiB

Open AccessArticle

Intelligent Force-Measurement System Use in Shock Tunnel

by Yunpeng Wang and Zonglin Jiang

Sensors 2020, 20(21), 6179; https://doi.org/10.3390/s20216179 - 30 Oct 2020

Cited by 7 | Viewed by 2837

Abstract

The inertial vibration of the force measurement system (FMS) has a large influence on the force measuring result of aircraft, especially on some tests carried out in high-enthalpy impulse facilities, such as in a shock tunnel. When force tests are conducted in a shock tunnel, the low-frequency vibrations of the FMS and its motion cannot be addressed through digital filtering because of the inertial forces, which are caused by the impact flow during the starting process of the shock tunnel. Therefore, this paper focuses on the dynamic characteristics of the performance of the FMS. A new method—i.e., deep-learning-based single-vector dynamic self-calibration (DL-based SV-DSC) of an impulse FMS, is proposed to increase the accuracy of aerodynamic force measurements in a shock tunnel. A deep-learning technique is used to train the dynamic model of the FMS in this study. Convolutional neural networks with a simple structure are applied to describe the dynamic modeling so that the low-frequency vibration signals are eliminated from the test results of the shock tunnel. By validation of the force test results measured in a shock tunnel, the current trained model can realize intelligent processing of the balance signals of the FMS. Based on this new method of dynamic calibration, the reliability and accuracy of force data processing are well verified. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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23 pages, 1690 KiB

Open AccessArticle

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

by Afifatul Mukaroh, Thi-Thu-Huong Le and Howon Kim

Sensors 2020, 20(19), 5674; https://doi.org/10.3390/s20195674 - 5 Oct 2020

Cited by 15 | Viewed by 2961

Abstract

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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14 pages, 3826 KiB

Open AccessArticle

Mechanical and Electronic Video Stabilization Strategy of Mortars with Trajectory Correction Fuze Based on Infrared Image Sensor

by Cong Zhang and Dongguang Li

Sensors 2020, 20(9), 2461; https://doi.org/10.3390/s20092461 - 26 Apr 2020

Cited by 7 | Viewed by 3679

Abstract

For a higher attack accuracy of projectiles, a novel mechanical and electronic video stabilization strategy is proposed for trajectory correction fuze. In this design, the complexity of sensors and actuators were reduced. To cope with complex combat environments, an infrared image sensor was used to provide video output. Following the introduction of the fuze’s workflow, the limitation of sensors for mechanical video stabilization on fuze was proposed. Particularly, the parameters of the infrared image sensor that strapdown with fuze were calculated. Then, the transformation relation between the projectile’s motion and the shaky video was investigated so that the electronic video stabilization method could be determined. Correspondingly, a novel method of dividing sub-blocks by adaptive global gray threshold was proposed for the image pre-processing. In addition, the gray projection algorithm was used to estimate the global motion vector by calculating the correlation between the curves of the adjacent frames. An example simulation and experiment were implemented to verify the effectiveness of this strategy. The results illustrated that the proposed algorithm significantly reduced the computational cost without affecting the accuracy of the motion estimation. This research provides theoretical and experimental basis for the intelligent application of sensor systems on fuze. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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16 pages, 2567 KiB

Open AccessArticle

An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning

by Chi Yoon Jeong and Mooseop Kim

Sensors 2019, 19(17), 3688; https://doi.org/10.3390/s19173688 - 25 Aug 2019

Cited by 18 | Viewed by 4714

Abstract

Human activity recognition (HAR), which is important in context awareness services, needs to occur continuously in daily life, owing to which an energy-efficient method is needed. However, because human activities have a longer cycle than HAR methods, which have analysis cycles of a few seconds, continuous classification of human activities using these methods is computationally and energy inefficient. Therefore, we propose segment-level change detection to identify activity change with very low computational complexity. Additionally, a fully convolutional network (FCN) with a high recognition rate is used to classify the activity only when activity change occurs. We compared the accuracy and energy consumption of the proposed method with that of a method based on a convolutional neural network (CNN) by using a public dataset on different embedded platforms. The experimental results showed that, although the recognition rate of the proposed FCN model is similar to that of the CNN model, the former requires only 10% of the network parameters of the CNN model. In addition, our experiments to measure the energy consumption on the embedded platforms showed that the proposed method uses as much as 6.5 times less energy than the CNN-based method when only HAR energy consumption is compared. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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17 pages, 8740 KiB

Open AccessArticle

AI-Based Early Change Detection in Smart Living Environments

by Giovanni Diraco, Alessandro Leone and Pietro Siciliano

Sensors 2019, 19(16), 3549; https://doi.org/10.3390/s19163549 - 14 Aug 2019

Cited by 15 | Viewed by 4812

Abstract

In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers’ daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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14 pages, 3083 KiB

Open AccessArticle

Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems

by Xiaowen Xu, Ying Liang, Pei He and Junliang Yang

Sensors 2019, 19(13), 2916; https://doi.org/10.3390/s19132916 - 1 Jul 2019

Cited by 55 | Viewed by 6850

Abstract

Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (SNR) value of the signal. The output results of the practical ECG signal test show that motion artifact in the real recorded ECG is effectively filtered out. The proposed method is feasible for reducing motion artifacts from ECG signals, whether from simulation ECG signals or practical non-contact ECG monitoring systems. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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16 pages, 931 KiB

Open AccessArticle

Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation

by Carmen Camara, Narayan P. Subramaniyam, Kevin Warwick, Lauri Parkkonen, Tipu Aziz and Ernesto Pereda

Sensors 2019, 19(11), 2507; https://doi.org/10.3390/s19112507 - 31 May 2019

Cited by 10 | Viewed by 3573

Abstract

Parkinson’s Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using

ε

-recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that

ε

-recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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19 pages, 4785 KiB

Open AccessArticle

Phase Diagram-Based Sensing with Adaptive Waveform Design and Recurrent States Quantification for the Instantaneous Frequency Law Tracking

by Angela Digulescu, Cornel Ioana and Alexandru Serbanescu

Sensors 2019, 19(11), 2434; https://doi.org/10.3390/s19112434 - 28 May 2019

Cited by 6 | Viewed by 3321

Abstract

Monitoring highly dynamic environments is a difficult task when the changes within the systems require high speed monitoring systems. An active sensing system has to solve the problem of overlapped responses coming from different parts of the surveyed environment. Thus, the need of a new representation space which separates the overlapped responses, is mandatory. This paper describes two new concepts for high speed active sensing systems. On the emitter side, we propose a phase-space-based waveform design that presents a unique shape in the phase space, which can be easily converted into a real signal. We call it phase space lobe. The instantaneous frequency (IF) law of the emitted signal is found inside the time series. The main advantage of this new concept is its capability to generate several distinct signals, non-orthogonal in the time/frequency domain but orthogonal within the representation space, namely the phase diagram. On the receiver side, the IF law information is estimated in the phase diagram representation domain by quantifying the recurrent states of the system. This waveform design technique gives the possibility to develop the high speed sensing methods, adapted for monitoring complex dynamic phenomena In our paper, as an applicative context, we consider the problem of estimating the time of flight in an dynamic acoustic environment. In this context, we show through experimental trials that our approach provides three times more accurate estimation of time of flight than spectrogram based technique. This very good accuracy comes from the capability of our approach to generate separable IF law components as well as from the quantification in phase diagram, both of them being the key element of our approach for high speed sensing. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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17 pages, 3228 KiB

Open AccessArticle

Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering

by Xianzhao Xia, Rui Chen, Pinquan Wang and Yiqiang Zhao

Sensors 2019, 19(10), 2311; https://doi.org/10.3390/s19102311 - 19 May 2019

Cited by 2 | Viewed by 3173

Abstract

The laser detection and ranging system (LADAR) is widely used in various fields that require 3D measurement, detection, and modeling. In order to improve the system stability and ranging accuracy, it is necessary to obtain the complete waveform of pulses that contain target information. Due to the inevitable noise, there are distinct deviations between the actual and expected waveforms, so noise suppression is essential. To achieve the best effect, the filters’ parameters that are usually set as empirical values should be adaptively adjusted according to the different noise levels. Therefore, we propose a novel noise suppression method for the LADAR system via eigenvalue-based adaptive filtering. Firstly, an efficient noise level estimation method is developed. The distributions of the eigenvalues of the sample covariance matrix are analyzed statistically after one-dimensional echo data are transformed into matrix format. Based on the boundedness and asymptotic properties of the noise eigenvalue spectrum, an estimation method for noise variances in high dimensional settings is proposed. Secondly, based on the estimated noise level, an adaptive guided filtering algorithm is designed within the gradient domain. The optimized parameters of the guided filtering are set according to an estimated noise level. Through simulation analysis and testing experiments on echo waves, it is proven that our algorithm can suppress the noise reliably and has advantages over the existing relevant methods. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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15 pages, 4531 KiB

Open AccessArticle

Flying Small Target Detection for Anti-UAV Based on a Gaussian Mixture Model in a Compressive Sensing Domain

by Chuanyun Wang, Tian Wang, Ershen Wang, Enyan Sun and Zhen Luo

Sensors 2019, 19(9), 2168; https://doi.org/10.3390/s19092168 - 10 May 2019

Cited by 33 | Viewed by 4315

Abstract

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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27 pages, 10456 KiB

Open AccessArticle

A Successive Approximation Time-to-Digital Converter with Single Set of Delay Lines for Time Interval Measurements

by Jakub Szyduczyński, Dariusz Kościelnik and Marek Miśkowicz

Sensors 2019, 19(5), 1109; https://doi.org/10.3390/s19051109 - 5 Mar 2019

Cited by 8 | Viewed by 5818

Abstract

The paper is focused on design of time-to-digital converters based on successive approximation (SA-TDCs—Successive Approximation TDCs) using binary-scaled delay lines in the feedforward architecture. The aim of the paper is to provide a tutorial on successive approximation TDCs (SA-TDCs) on the one hand, and to make the contribution to optimization of SA-TDC design on the other. The proposed design optimization consists essentially in reduction of circuit complexity and die area, as well as in improving converter performance. The main paper contribution is the concept of reducing SA-TDC complexity by removing one of two sets of delay lines in the feedforward architecture at the price of simple output decoding. For 12 bits of resolution, the complexity reduction is close to 50%. Furthermore, the paper presents the implementation of 8-bit SA-TDC in 180 nm CMOS technology with a quantization step 25 ps obtained by asymmetrical design of pair of inverters and symmetrized multiplexer control. Full article

(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)

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