Planar Sensor for Material Characterization Based on the Sierpinski Fractal CurveRead the full article
Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.
Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.
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Dual-Band Maritime Imagery Ship Classification Based on Multilayer Convolutional Feature Fusion
Addressing to the problems of few annotated samples and low-quality fused feature in visible and infrared dual-band maritime ship classification, this paper leverages hierarchical features of deep convolutional neural network to propose a dual-band maritime ship classification method based on multilayer convolutional feature fusion. Firstly, the VGGNet model pretrained on the ImageNet dataset is fine-tuned to capture semantic information of the specific dual-band ship dataset. Secondly, the pretrained and fine-tuned VGGNet models are used to extract low-level, middle-level, and high-level convolutional features of each band image, and a number of improved recursive neural networks with random weights are exploited to reduce feature dimension and learn feature representation. Thirdly, to improve the quality of feature fusion, multilevel and multilayer convolutional features of dual-band images are concatenated to fuse hierarchical information and spectral information. Finally, the fused feature vector is fed into a linear support vector machine for dual-band maritime ship category recognition. Experimental results on the public dual-band maritime ship dataset show that multilayer convolution feature fusion outperforms single-layer convolution feature by about 2% mean per-class classification accuracy for single-band image, dual-band images perform better than single-band image by about 2.3%, and the proposed method achieves the best accuracy of 89.4%, which is higher than the state-of-the-art method by 1.2%.
A Cross-Platform Web3D Monitoring System of the Three-Machine Equipment in a Fully Mechanized Coalface Based on the Skeleton Model and Sensor Data
A fully mechanized coalface is a rugged environment that has poor visibility. The traditional video monitoring system has problems such as a lack of realism, a blurry monitoring effect, and poor reliability. It is an important task to monitor the operations of the three-machine equipment (we will refer to the shearer, hydraulic support, and scraper conveyor as the three-machine equipment) intuitively, accurately, and timely and ensure that it is operating safely. This study proposed a cross-platform Web3D monitoring system for the three-machine equipment. First, the virtual mesh model and skeleton model that was embedded in the mesh model were established according to three-machine ontology and basic motion units. Second, the kinematic model of the three-machine skeleton was established via the inverse kinematic modeling of the hydraulic support and the coordinate calculation of the vertices on the three-machine skeleton. Third, the motion data, which were captured by sensors, were applied to drive the movement of the three-machine skeleton and mesh model. Finally, WebGL, which is the latest Internet graphics standard, was used to render the three-machine models, and the performance of this monitoring system is tested on different equipment in the laboratory. The results of the test show that the three-machine cross-platform monitoring system has splendid performance, and it realizes cross-platform 3D monitoring effectively in the laboratory. In the future, this system will be used as a supervisory tool and be integrated with the traditional monitoring system to monitor the three-machine equipment with the field staff.
Smart Home IoT System by Using RF Energy Harvesting
IoT system becomes a hot topic nowadays for smart home. IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circularly polarized array rectenna system to harvest enough power from any direction to increase lifetime of the batteries used in the IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node, and a low-power microcontroller are used to operate in lower power modes. The proposed system has an 84.6-day lifetime which is approximately 10 times the lifetime for a similar system. On the other hand, the proposed power management circuit is operated at 0.3 V DC to boost the voltage to ~3.7 V from radio frequency energy harvesting and manage battery level to increase the battery lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a nonstatic plan, approve energy consumption, and avoid failure of sensor nodes in a smart home.
A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection
Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate. Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR. To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively. The current step length models mostly depend on individual parameters that need to be predetermined for different users. Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking. Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all. The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities. Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking. Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model.
An Enhanced Method for Detecting and Repairing the Cycle Slips of Dual-Frequency Onboard GPS Receivers of LEO Satellites
Cycle slip detection and repair play important roles in the processing of data from dual-frequency GPS receivers onboard low-Earth orbit (LEO) satellites. To detect and repair cycle slips more comprehensively, an enhanced error method (EEM) is proposed. EEM combines single-frequency and narrow-lane carrier phase observations to construct special observations and observation equation groups. These special observations differ across time and satellite (ATS). ATS observations are constructed by three steps. The first step is differencing single-frequency and narrow-lane observations through a time difference (TD). The second step is to select a satellite as a reference satellite and other satellites as nonreference satellites. The third step is to difference the single-frequency TD observations from the reference satellite and the narrow-lane TD observations from the nonreference satellites by a satellite difference. If cycle slips occur at the reference satellite, the correction values for these ATS observations can be significantly enlarged. To process all satellites, the EEM selects each satellite as a reference satellite and builds the corresponding equation group. The EEM solves these observation equation groups according to the weighted least-squares adjustment (LSA) criterion and obtains the correction values; these correction values are then used to construct the values corresponding to different equation groups, and the EEM subsequently carries out a chi-square distribution test for these . The satellite corresponding to the maximum will be marked. Then, the EEM iteratively processes the other satellites. Cycle slips can be estimated by rounding the float solutions of changes in the ambiguities of cycle slip satellites to the nearest integer. The simulation test results show that the EEM can be used to detect special cycle slip pairs such as (1, 1) and (9, 7). The EEM needs only observation data in two adjacent epochs and is still applicable to observation epochs with continuous cycle slips.
Implementation of Camshift Target Tracking Algorithm Based on Hybrid Filtering and Multifeature Fusion
In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Camshift method only uses hue components in HSV to extract features. This paper uses the combination of H and S components in HSV space to build a two-dimensional color feature histogram and with the image’s LBP feature histogram to increase tracking accuracy. Meanwhile, for the sake of target occlusion and nonlinear changes in the tracking process, this paper introduces a Gaussian-Hermit particle filter that is updated by the Kalman filter. Experimental result demonstrates that the real-time performance of the proposal in this paper is better than Mean shift, Camshift, simple particle filter, and Kalman filter.