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JournalISSN: 2379-9153

IEEE sensors journal 

About: IEEE sensors journal is an academic journal. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 2379-9153. Over the lifetime, 1282 publications have been published receiving 5353 citations.

Papers published on a yearly basis

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TL;DR: The special issue intends to promote and to collect original and novel papers that will give significant contribution in the field of sensors and sensing systems dedicated to neurophysiology.
Abstract: Neurophysiology is a specific branch of physiology that deals with neuro-cerebral areas, in particular with the nervous system. The need for monitoring all the nervous system is nowadays, partially possible thanks to the advances in micro and nanotechnologies. Neurophysiology mostly includes topics involving muscles, ears and audiometry, eye (ERG, EOG, visual field) and brain issues. The brain, as the most important organ studied in neurophysiology, attracts interest as subject of wide research activities, e.g. brain imaging new modalities (including X-ray, CT, MRI, SPECT, PET, Biomagnetism, DTI, etc..), BCI, EEG, evoked potentials, cell potentials, neuro-rehabilitation, etc. Micro and nanosensors are contributing hugely to the above research. The same applies to bio-materials for sensors, as well as other polymers, underpinned by advances in materials science and engineering. The special issue intends to promote and to collect original and novel papers that will give significant contribution in the field of sensors and sensing systems dedicated to neurophysiology. Regular papers, tutorials and review papers are sought in neurophysiology areas including (but not limited to):

127 citations

Journal ArticleDOI
TL;DR: In this paper , a photonic crystal fiber (PCF) based plasmonic biosensor for the detection of various blood compositions like red blood cells, hemoglobin, white blood cells (WBCs), plasma, and water was proposed.
Abstract: This paper proposes a photonic crystal fiber (PCF) based plasmonic biosensor for the detection of various blood compositions like red blood cells (RBCs), hemoglobin (HB), white blood cells (WBCs), plasma, and water. The finite element method (FEM) has been used to simulate and quantitatively evaluate this biosensor. The gold and titanium dioxide coated PCF operates on the surface plasmon resonance (SPR) theory, where the gold layer acts as a plasmonic material, and the titanium dioxide layer improves adhesion between the gold layer and the PCF surface. SPR occurs at the interface between gold and the sensing channel, when the core propagation mode is coupled with the surface plasmon polariton (SPP) mode in the vicinity of the phase-matching point. Due to the occurrence of SPR, the loss peak is noticed in the core propagation mode, and this loss peak is extremely sensitive to the various blood compositions that each have their unique refractive index (RI) poured into the sensing channel of the PCF. The proposed biosensor has maximum wavelength sensitivity of 12400 nm/RIU. However, the maximum amplitude sensitivity is −574.3 RIU−1. Furthermore, with the maximum detection limit of 0.02, the refractive index resolution varies from $8.06\times {10}^{-6} {\mathrm {RIU}}$ to $5.0\times {10}^{-5} {\mathrm {RIU}}$ . As a result, it is safe to say that this biosensor will work admirably in terms of detecting blood compositions. Thus, the proposed biosensor will explore the broad realms of medical diagnostics.

101 citations

Journal ArticleDOI
TL;DR: This survey presents a holistic (historical as well as architectural) overview of wireless sensor (WS) nodes, providing a classical definition, in-depth analysis of different modules involved in the design of a WS node, and the ways in which they can be used to measure a system performance.
Abstract: The addition of massive machine type communication (mMTC) as a category of Fifth Generation (5G) of mobile communication, have increased the popularity of Internet of Things (IoT). The sensors are one of the critical component of any IoT device. Although the sensors posses a well-known historical existence, but their integration in wireless technologies and increased demand in IoT applications have increased their importance and the challenges in terms of design, integration, etc. This survey presents a holistic (historical as well as architectural) overview of wireless sensor (WS) nodes, providing a classical definition, in-depth analysis of different modules involved in the design of a WS node, and the ways in which they can be used to measure a system performance. Using the definition and analysis of a WS node, a more comprehensive classification of WS nodes is provided. Moreover, the need to form a wireless sensor network (WSN), their deployment, and communication protocols is explained. The applications of WS nodes in various use cases have been discussed. Additionally, an overlook of challenges and constraints that these WS nodes face in various environments and during the manufacturing process, are discussed. Their main existing developments which are expected to augment the WS nodes, to meet the requirements of the emerging systems, are also presented.

60 citations

Journal ArticleDOI
TL;DR: In this paper , a gas detection sensor based on a microstructured-core photonic crystal fiber (PCF) was proposed, which is used to analyze the quantitative dependence of guiding properties on geometrical parameters along wavelength.
Abstract: This paper describes a gas detection sensor that is based on a microstructured-core photonic crystal fiber (PCF). The finite element method (FEM) is used to analyze the quantitative dependence of guiding properties on geometrical parameters along wavelength. The result shows that the structure provides high relativity sensitivity with minimal confinement loss for detecting various gases such as methane (CH 4 ) and hydrogen fluoride (HF) because the proposed PCF introduces microstructured-core. According to the results of this simulation, an absorptive line of CH 4 /HF gases could have a maximum relative sensitivity of about 44.47% at wavelength of $1.33 ~\mu \text{m}$ with the optimal design of the PCF. The proposed sensor has a confinement loss of around ${1.83\times 10}^{-8}$ dB/m, that is very low and suitable for use as a gas sensor. Furthermore, numerical aperture (NA), V-parameter, Marcuse spot size (MSS), and beam divergence (BD) are extensively investigated in the wavelength range of 1.3 to $2.2 ~\mu \text{m}$ . These findings should aid in the development of a high-efficiency PCF for gas sensing and monitoring air pollution.

52 citations

Journal ArticleDOI
TL;DR: In this article , a vehicle localization system based on vehicle chassis sensors considering vehicle lateral velocity is proposed, which combines the advantages of vehicle dynamic model in low dynamic driving conditions and the advantage of kinematic model in highly dynamic driving condition.
Abstract: Vehicle localization is essential for intelligent and autonomous vehicles. To improve the accuracy of vehicle stand-alone localization in highly dynamic driving conditions during GNSS (Global Navigation Satellites Systems) outages, this paper proposes a vehicle localization system based on vehicle chassis sensors considering vehicle lateral velocity. Firstly, a GNSS/On-board sensors fusion localization framework is established, which could estimate vehicle states such as attitude, velocity, and position. Secondly, when the vehicle has a large lateral motion, nonholonomic constraint in the lateral direction loses fidelity. Instead of using nonholonomic constraint, we propose a vehicle dynamics/kinematics fusion lateral velocity estimation algorithm, which combines the advantage of vehicle dynamic model in low dynamic driving conditions and the advantage of kinematic model in highly dynamic driving conditions. Thirdly, vehicle longitudinal velocity estimated by WSS (Wheel Speed Sensor) and lateral velocity estimated by proposed method are as measurements for the localization system. All information is fused by an adaptive Kalman filter. Finally, vehicle experiments in U-turn maneuver and left-turn maneuver at a traffic intersection are conducted to verify the proposed method. Four different methods are compared in the experiments, and the results show that the estimated position accuracy of our method is below half a meter during a 5s GNSS outage and could keep a sub-meter-level during a 20s GNSS outage while the vehicle has a relatively large lateral motion.

51 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
20231,874
20222,524