Improving Packet Delivery Performance in Water Column Variations through LOCAN in Underwater Acoustic Sensor NetworkRead 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.
Journal of Sensors maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Accurate 3D Surface Reconstruction for Smart Farming Application with an Inexpensive Shape from Focus System
In precision agriculture, 3D vision systems are becoming increasingly important. By applying different optical 3D vision techniques, the acquired 3D data can provide information regarding the most important phenotype features in every agricultural scenario. However, most of these 3D vision systems are expensive, except some of the triangulation techniques. In this study, we focus on estimating accurate shapes using shape from focus (SFF), which is a triangulation technique. Typically, the SFF system incurs significant errors from images, including noise. As a solution to this problem, a simple low-pass filter such as the Gaussian filter has generally been used in most studies. However, when a low filter is applied, the noise is depressed but the signals are also blurred, which results in inaccuracies regarding the depth map. In this study, the noise is depressed independently without losing the original signals, and the edge components, which play important roles in finding a focused surface, are enhanced using the independent component analysis (ICA). The edge signals are amplified with a simple basis vector correction in the IC vector space. The experiments are implemented with simulated objects and real objects. The experimental results demonstrate that the obtained accuracy is comparable to that of existing methods.
Block-Split Array Coding Algorithm for Long-Stream Data Compression
With the advent of IR (Industrial Revolution) 4.0, the spread of sensors in IoT (Internet of Things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. Hence, the study of big data compression is valuable in the field of sensors. A problem is how to compress the long-stream data efficiently with the finite memory of a sensor. To maintain the performance, traditional techniques of compression have to treat the data streams on a small and incompetent scale, which will reduce the compression ratio. To solve this problem, this paper proposes a block-split coding algorithm named “CZ-Array algorithm,” and implements it in the shareware named “ComZip.” CZ-Array can use a relatively small data window to cover a configurable large scale, which benefits the compression ratio. It is fast with the time complexity O() and fits the big data compression. The experiment results indicate that ComZip with CZ-Array can obtain a better compression ratio than gzip, lz4, bzip2, and p7zip in the multiple stream data compression, and it also has a competent speed among these general data compression software. Besides, CZ-Array is concise and fits the hardware parallel implementation of sensors.
Dynamic Measurement of Legs Motion in Sagittal Plane Based on Soft Wearable Sensors
Human motion capture is widely used in exoskeleton robots, human-computer interaction, sports analysis, rehabilitation training, and many other fields. However, soft-sensor-based wearable dynamic measurement has not been well achieved. In this paper, the dynamic measurements of legs were investigated by using dielectric elastomers as stain sensors, and an alternating signal was applied to detect the dynamic rotational angles of the legs. To realize a quick response, parameters of the sensors were optimized by circuit analysis. The sensor can detect hip, knee, and ankle joint motions with a sample frequency of 200 Hz. The measurements of the sensors were compared with a commercial motion capture system from PhaseSpace, and dynamic errors between them were smaller than 3° when squatting and walking at low speed and smaller than 5° when walking at high speed. Experiments therefore demonstrate the feasibility of the integrated wearable stretch sensors with pants.
Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease
The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.
Adjacent Channel Interference Modeling of Single Vibration Point on Multichannel Dynamic Pressure Sensors
Pulse waves of a radial artery under different pressures applied through a cuff play an important role in disease diagnosis, especially in traditional chinese medicine (TCM). Pulse waves could be collected by a pressure sensor array affixed to an inflatable cuff. During a process of collecting pulse waves, one sensor of a sensor array moves up and down when the sensor is shocked by a pulse wave. Movement of the sensor leads to the passive displacement of other nearby sensors because of a connecting structure between them. Then, vibration signals will be generated by the nearby sensors although these sensors do not receive radial artery pulse waves. These vibration signals considered an interference are usually superimposed on real signals obtained from these nearby sensors and degrade signal quality. The problem mentioned above does not only generally exist in a pressure sensor array attached to a wristband but also is easy to ignore. This paper proposes a novel interference suppression algorithm based on Welch’s method for estimating and weakening adjacent sensor channel interference to overcome the problem. At first, a sensor array attached to an inflatable cuff and a vibration generator is proposed to establish an experimental platform for simplifying the pulse wave collection process. Then, the interference suppression algorithm is proposed according to mechanical analysis and Welch’s method based on the proposed sensor array and vibration generator. Next anti-interference abilities of the algorithm based on a simplified process are evaluated by different vibration frequencies and applied pressures. The anti-interference abilities of the algorithm based on pulse waves of the radial artery are evaluated indirectly. The results show that the novel interference suppression algorithm could weaken adjacent sensor channel interference and upgrade the signal quality.
Characterization of Filamentous Flocs to Predict Sedimentation Parameters Using Image Analysis
In wastewater treatment plants, the degradation of complex substances that contaminate water is carried out by microorganisms, which are fixed by a network formed by filamentous bacteria, creating large flocs that settle easily. However, the excessive growth of said bacteria causes a series of drawbacks such as the reduction of settling velocity, leakage of activated sludge with the effluent, and formation of supernatant, a phenomenon known as bulking. This research work seeks to develop and evaluate a procedure for the physical characterization of the flocs to determine the parameters that affect the settling velocity and thereby detect and control bulking. For this purpose, sedimentation and image analysis tests were carried out from wastewater from the Aguas Antofagasta treatment plant (Chile). The image analysis was performed with images captured from an optical microscope in two magnifications (100x and 50x), which were analyzed by marking each floc individually and characterized by an image processing software. Additionally, sedimentation tests were performed on columns (area of 74 (cm2) and height of 70 (cm)). As a result, an inversely proportional dependence was found on the settling velocity evaluated by the Vesilind equation in the zone of constant fall velocity with respect to the number of flocs connected per cluster, giving an estimate of the settling velocity depending on the number of flocs connected. This would allow predicting settling velocity with image analysis, taking into account that the problem of bulking is determined by the type of filamentous bacteria that causes it and the sedimentation process is affected in large part by local factors. It can be concluded through this study that as the number of flocs connected per cluster increases, the settling velocity decreases. This study provides wastewater treatment plants with a practical tool to determine sedimentation times and thus improve the quality of the treated water, avoiding problems of flocs leaking with the effluent. In addition, the image analysis itself allows rapid detection of the phenomenon of bulking and its severity.