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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 (30 September 2020) | Viewed by 27374

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Special Issue Editors

Prof. Dr. Luciano Sánchez

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Computer Science Department, Universidad de Oviedo, E.P.I. Gijón. Sedes Departamentales 1.1.28. 33202 Gijón, Spain
Interests: intelligent data analysis; learning under uncertainty; computational intelligence; fuzzy sets; mathematical models; signal processing; dimensional metrology; industrial applications (ecoefficiency, rechargeable batteries, clean energy)
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Dr. José Ranilla Pastor

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Computer Science Department, Universidad de Oviedo, E.P.I. Gijón. Sedes Departamentales 1.1.04. 33202 Gijón, Spain
Interests: high-performance and parallel algorithms; consumer-aware computational applications; quantum algorithms; intelligent data analysis; signal processing

Special Issue Information

Dear Colleague，

Intelligent or smart sensors make use of dedicated signal processing components, either to improve the characteristics of the sensor device or to enhance the signal. Although this is a well-established technology, present-day sensors pose new challenges and can benefit from the application of Artificial Intelligence (AI) techniques to digital data processing. This Special Issue addresses the application of AI to data processing at a sensor level, with special interest in the following (non-exclusive) list of aspects:

Handling in real time huge volumes of data;
Online extraction of features and intelligent signal conditioning at the sensor level (deep learning, medical devices, human activity recognition, etc.);
Dealing with uncertain, imprecise and incomplete information: inherent measurement noise (for example, in GPS), missing samples or features, censored, interval-valued, coarsely discretized or fuzzy data, etc.;
Resilience to sensor failures through redundancy and information fusion, with special interest in unattended systems, life-supporting and other critical equipment;
Sofware sensors for health monitoring and remaining useful life estimation of complex equipment such as aviation engines, offshore windmills, electrical vehicles, etc.;
Energy-efficient software for autonomous and battery operated environments: adiabatic and reversible computing, quantum computing, energy harvesting, energy-efficient protocols in sensor networks, etc.;
Ecoefficiency and life-cycle concerns in sensor systems design (sustainability assessment, recycling, waste management, second life of equipment and batteries).

Prof. Dr. Luciano Sánchez
Dr. José Ranilla Pastor
Guest Editors

Manuscript Submission Information

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Keywords

Smart sensors
Feature extraction at the sensor level
Intelligent signal conditioning
Uncertain and missing data
Failure resilient sensors
Soft sensors
Health monitoring
Energy efficient software
Rechargeable batteries
Ecoefficiency

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Published Papers (7 papers)

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Research

25 pages, 1125 KiB

Open AccessArticle

Spatio-Temporal Scale Coded Bag-of-Words

by Divina Govender and Jules-Raymond Tapamo

Sensors 2020, 20(21), 6380; https://doi.org/10.3390/s20216380 - 9 Nov 2020

Cited by 2 | Viewed by 2879

Abstract

The Bag-of-Words (BoW) framework has been widely used in action recognition tasks due to its compact and efficient feature representation. Various modifications have been made to this framework to increase its classification power. This often results in an increased complexity and reduced efficiency. Inspired by the success of image-based scale coded BoW representations, we propose a spatio-temporal scale coded BoW (SC-BoW) for video-based recognition. This involves encoding extracted multi-scale information into BoW representations by partitioning spatio-temporal features into sub-groups based on the spatial scale from which they were extracted. We evaluate SC-BoW in two experimental setups. We first present a general pipeline to perform real-time action recognition with SC-BoW. Secondly, we apply SC-BoW onto the popular Dense Trajectory feature set. Results showed SC-BoW representations to successfully improve performance by 2–7% with low added computational cost. Notably, SC-BoW on Dense Trajectories outperformed more complex deep learning approaches. Thus, scale coding is a low-cost and low-level encoding scheme that increases classification power of the standard BoW without compromising efficiency. Full article

(This article belongs to the Special Issue Data Processing of Intelligent Sensors)

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21 pages, 3290 KiB

Open AccessArticle

Multi-Objective Evolutionary Design of an Electric Vehicle Chassis

by Pablo Luque, Daniel A. Mántaras, Álvaro Maradona, Jorge Roces, Luciano Sánchez, Luis Castejón and Hugo Malón

Sensors 2020, 20(13), 3633; https://doi.org/10.3390/s20133633 - 28 Jun 2020

Cited by 11 | Viewed by 4487

Abstract

An iterative algorithm is proposed for determining the optimal chassis design of an electric vehicle, given a path and a reference time. The proposed algorithm balances the capacity of the battery pack and the dynamic properties of the chassis, seeking to optimize the tradeoff between the mass of the vehicle, its energy consumption, and the travel time. The design variables of the chassis include geometrical and inertial values, as well as the characteristics of the powertrain. The optimization is constrained by the slopes, curves, grip, and posted speeds of the different sections of the track. Particular service constraints are also considered, such as limiting accelerations due to passenger comfort or cargo safety. This methodology is applicable to any vehicle whose route and travel time are known in advance, such as delivery vehicles, buses, and race cars, and has been validated using telemetry data from an internal combustion rear-wheel drive race car designed for hill climb competitions. The implementation of the proposed methodology allows to reduce the weight of the battery pack by up to 20%, compared to traditional design methods. Full article

(This article belongs to the Special Issue Data Processing of Intelligent Sensors)

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23 pages, 2515 KiB

Open AccessArticle

SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications

by Nsikak Pius Owoh and Manmeet Mahinderjit Singh

Sensors 2020, 20(11), 3280; https://doi.org/10.3390/s20113280 - 9 Jun 2020

Cited by 11 | Viewed by 3991

Abstract

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework. Full article

(This article belongs to the Special Issue Data Processing of Intelligent Sensors)

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24 pages, 7403 KiB

Open AccessArticle

Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning

by Obed Tettey Nartey, Guowu Yang, Sarpong Kwadwo Asare, Jinzhao Wu and Lady Nadia Frempong

Sensors 2020, 20(9), 2684; https://doi.org/10.3390/s20092684 - 8 May 2020

Cited by 19 | Viewed by 4496

Abstract

Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications. Full article

(This article belongs to the Special Issue Data Processing of Intelligent Sensors)

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22 pages, 6344 KiB

Open AccessArticle

A Multi-Clustering Algorithm to Solve Driving Cycle Prediction Problems Based on Unbalanced Data Sets: A Chinese Case Study

by Yuewei Wu, Wutong Zhang, Long Zhang, Yuanyuan Qiao, Jie Yang and Cheng Cheng

Sensors 2020, 20(9), 2448; https://doi.org/10.3390/s20092448 - 25 Apr 2020

Cited by 6 | Viewed by 2953

Abstract

Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clustering algorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset. Full article

(This article belongs to the Special Issue Data Processing of Intelligent Sensors)

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12 pages, 4752 KiB

Open AccessArticle

An Angle Error Compensation Method Based on Harmonic Analysis for Integrated Joint Modules

by Yi Hu, Yuyi Zhan, Liang Han, Penghao Hu, Bing Ye and Yue Yu

Sensors 2020, 20(6), 1715; https://doi.org/10.3390/s20061715 - 19 Mar 2020

Cited by 6 | Viewed by 2805

Abstract

Nowadays, integrated joint modules are increasingly adopted in manipulators for their advantages of high integration, miniaturization and high repeatability positioning accuracy. The problem of generally low absolute positioning accuracy (namely angle measurement accuracy) must be solved before they can be introduced into the self-driven articulated arm coordinate measuring machine which is under study in our laboratory. In this study, the sources of joint module’s angle error were analyzed and the error model based on harmonic analysis was established. Two integrated joint modules were calibrated on the self-designed calibration platform and the model parameters were deduced, respectively. The angle error was then compensated in the experiments and the results demonstrated that the angle error of the joint modules was reduced by 82.03% on average. The established angle error model can be effectively applied into the self-driven articulated arm coordinated measuring machine. Full article

(This article belongs to the Special Issue Data Processing of Intelligent Sensors)

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23 pages, 8628 KiB

Open AccessArticle

LPI Radar Waveform Recognition Based on Features from Multiple Images

by Zhiyuan Ma, Zhi Huang, Anni Lin and Guangming Huang

Sensors 2020, 20(2), 526; https://doi.org/10.3390/s20020526 - 17 Jan 2020

Cited by 30 | Viewed by 4772

Abstract

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB. Full article

(This article belongs to the Special Issue Data Processing of Intelligent Sensors)

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