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JournalISSN: 1424-8220

Sensors 

About: Sensors is an academic journal. The journal publishes majorly in the area(s): Wireless sensor network & Convolutional neural network. It has an ISSN identifier of 1424-8220. It is also open access. Over the lifetime, 39465 publication(s) have been published receiving 618638 citation(s).


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Journal ArticleDOI
Cheng-Xiang Wang1, Longwei Yin, Luyuan Zhang, Dong Xiang, Rui Gao 
15 Mar 2010-Sensors
TL;DR: A brief review of changes of sensitivity of conductometric semiconducting metal oxide gas sensors due to the five factors: chemical components, surface-modification and microstructures of sensing layers, temperature and humidity.
Abstract: Conductometric semiconducting metal oxide gas sensors have been widely used and investigated in the detection of gases. Investigations have indicated that the gas sensing process is strongly related to surface reactions, so one of the important parameters of gas sensors, the sensitivity of the metal oxide based materials, will change with the factors influencing the surface reactions, such as chemical components, surface-modification and microstructures of sensing layers, temperature and humidity. In this brief review, attention will be focused on changes of sensitivity of conductometric semiconducting metal oxide gas sensors due to the five factors mentioned above.

1,725 citations

Journal ArticleDOI
01 Feb 2012-Sensors
TL;DR: The calibration of the Kinect sensor is discussed, and an analysis of the accuracy and resolution of its depth data is provided, based on a mathematical model of depth measurement from disparity.
Abstract: Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

1,592 citations

Journal ArticleDOI
18 Jan 2016-Sensors
TL;DR: A generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which is suitable for multimodal wearable sensors, does not require expert knowledge in designing features, and explicitly models the temporal dynamics of feature activations is proposed.
Abstract: Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

1,363 citations

Journal ArticleDOI
07 Jul 2014-Sensors
TL;DR: This review focuses on recent advances in the field of Smart Textiles and pays particular attention to the materials and their manufacturing process, to highlight a possible trade-off between flexibility, ergonomics, low power consumption, integration and eventually autonomy.
Abstract: Electronic Textiles (e-textiles) are fabrics that feature electronics and interconnections woven into them, presenting physical flexibility and typical size that cannot be achieved with other existing electronic manufacturing techniques. Components and interconnections are intrinsic to the fabric and thus are less visible and not susceptible of becoming tangled or snagged by surrounding objects. E-textiles can also more easily adapt to fast changes in the computational and sensing requirements of any specific application, this one representing a useful feature for power management and context awareness. The vision behind wearable computing foresees future electronic systems to be an integral part of our everyday outfits. Such electronic devices have to meet special requirements concerning wearability. Wearable systems will be characterized by their ability to automatically recognize the activity and the behavioral status of their own user as well as of the situation around her/him, and to use this information to adjust the systems' configuration and functionality. This review focuses on recent advances in the field of Smart Textiles and pays particular attention to the materials and their manufacturing process. Each technique shows advantages and disadvantages and our aim is to highlight a possible trade-off between flexibility, ergonomics, low power consumption, integration and eventually autonomy.

1,325 citations

Journal ArticleDOI
07 Mar 2007-Sensors
Abstract: The gas sensors fabricated by using conducting polymers such as polyaniline (PAni), polypyrrole (PPy) and poly (3,4-ethylenedioxythiophene) (PEDOT) as the active layers have been reviewed. This review discusses the sensing mechanism and configurations of the sensors. The factors that affect the performances of the gas sensors are also addressed. The disadvantages of the sensors and a brief prospect in this research field are discussed at the end of the review.

1,199 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
20217,606
20207,326
20195,700
20184,616
20173,025
20162,349