Showing papers in "Journal of Sensors in 2018"
TL;DR: A collocated comparison of four models of low-cost optical sensors with a TEOM 1400a analyser found that the trends of outputs from sensors were generally similar to TEOM data, but significant overestimation of PM2.5 concentrations was observed for the sensor raw data.
Abstract: Low-cost sensors are an opportunity to improve the spatial and temporal resolution of particulate matter data. However, such sensors should be calibrated under conditions close to the final ones before any monitoring actions. The paper presents the results of a collocated comparison of four models of low-cost optical sensors with a TEOM 1400a analyser. SDS011 (Nova Fitness), ZH03A (Winsen), PMS7003 (Plantower), and OPC-N2 (Alphasense) sensors were used in this research. Three copies of each sensor model were placed in a common box to compare the sensor performance under the same measurement conditions. Monitoring of the PM2.5 fraction was conducted for almost half a year from 21 August 2017 to 19 February 2018 in Wroclaw (Poland). Reproducibility between sensor units was assessed on the basis of coefficient of variation (CV). CV values were lower than 7% in the case of SDS011 and PMS7003 sensors and equal to 20% for OPC-N2 units. CV was higher than 50% for ZH03A, mainly due to malfunctions. During the measurements, the trends of outputs from sensors were generally similar to TEOM data, but significant overestimation of PM2.5 concentrations was observed for the sensor raw data. A high linear relationship between TEOM and sensors was noticed for 1 min, 15 min, and 1-hour averaged data for PMS7003 sensors ( –0.89), for SDS011 units ( –0.86), and for one unit of ZH03A ( –0.81). values for daily averages were at the level 0.91–0.93 for PMS7003, 0.87–0.90 for SDS011, and 0.89 for ZH03A. OPC-N2 had only a moderate linear relationship with TEOM ( –0.69 for daily data and 0.43–0.61 for shorter time averages). Quite large dispersion of data and high relative errors of PM2.5 estimation were observed for concentration ranges below 20–30 μg/m3. The impact of high relative humidity level was observed for SDS011 and OPC-N2 devices—clear overestimation of outputs was observed above 80% RH.
152 citations
TL;DR: Aeroponics using intelligent techniques (wireless sensors) provides a wide range of information which could be essential for plant researchers and provides a greater understanding of how the key parameters of aeroponics correlate with plant growth in the system.
Abstract: In recent years, intelligent sensor techniques have achieved significant attention in agriculture. It is applied in agriculture to plan the several activities and missions properly by utilising limited resources with minor human interference. Currently, plant cultivation using new agriculture methods is very popular among the growers. However, the aeroponics is one of the methods of modern agriculture, which is commonly practiced around the world. In the system, plant cultivates under complete control conditions in the growth chamber by providing a small mist of the nutrient solution in replacement of the soil. The nutrient mist is ejected through atomization nozzles on a periodical basis. During the plant cultivation, several steps including temperature, humidity, light intensity, water nutrient solution level, pH and EC value, CO2 concentration, atomization time, and atomization interval time require proper attention for flourishing plant growth. Therefore, the object of this review study was to provide significant knowledge about early fault detection and diagnosis in aeroponics using intelligent techniques (wireless sensors). So, the farmer could monitor several paraments without using laboratory instruments, and the farmer could control the entire system remotely. Moreover, the technique also provides a wide range of information which could be essential for plant researchers and provides a greater understanding of how the key parameters of aeroponics correlate with plant growth in the system. It offers full control of the system, not by constant manual attention from the operator but to a large extent by wireless sensors. Furthermore, the adoption of the intelligent techniques in the aeroponic system could reduce the concept of the usefulness of the system due to complicated manually monitoring and controlling process.
129 citations
TL;DR: Examining the smart home service features that current users require and empirically evaluating the relationship between the critical factors and the adoption behavior with 216 samples from Korea provides various theoretical and practical implications.
Abstract: The word “smart” has been used in various fields and is widely accepted to mean intelligence. Smart home service, one of the representative emerging technologies in the IoT era, has changed house equipment into being more intelligent, remote controllable, and interconnected. However, the intelligence and controllability of a smart home service are contradictory concepts, under certain aspects. In addition, the level of intelligence or controllability of a smart home service that users want may differ according to the user. As potential users of smart home services have diversified in recent years, providing the appropriate functions and features is critical to the diffusion of the service. Thus, this study examines the smart home service features that current users require and empirically evaluates the relationship between the critical factors and the adoption behavior with 216 samples from Korea. The moderating effect of personal characteristics on behavior is also tested. The results of the analysis provide various theoretical and practical implications.
109 citations
TL;DR: An overview of the recent advances of research in DAS based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) is provided, and a review of recent advances in more precise quantitative measurement of an external impact based on frequency shift and phase demodulation methods using simple direct detection ϕ-OTSDR schemes is given.
Abstract: Distributed acoustic sensing (DAS) using coherent Rayleigh backscattering in an optical fiber has become a ubiquitous technique for monitoring multiple dynamic events in real time. It has continued to constitute a steadily increasing share of the fiber-optic sensor market, thanks to its interesting applications in many safety, security, and integrity monitoring systems. In this contribution, an overview of the recent advances of research in DAS based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) is provided. Some advanced techniques used to enhance the performance of ϕ-OTDR sensors for measuring backscattering intensity changes through reduction of measurement noise are presented, in addition to methods used to increase the dynamic measurement capacity of ϕ-OTDR schemes beyond conventional limits set by the sensing distance. Recent ϕ-OTDR configurations which significantly enhance the measurement spatial resolution, including those which decouple it from the probing pulse width, are also discussed. Finally, a review of recent advances in more precise quantitative measurement of an external impact based on frequency shift and phase demodulation methods using simple direct detection ϕ-OTDR schemes is given.
107 citations
TL;DR: This paper proposes to use fog computing to help monitor patients suffering from chronic diseases such that the data are collected and processed in an efficient manner and reduces the amount of data that is transported back and forth between the cloud and the sensors.
Abstract: Wireless sensor networks (WSNs) are widely used in the area of health informatics. Wireless and wearable sensors have become prevalent devices to monitor patients at risk for chronic diseases. This helps ascertain that patients comply by the treatment plans and also safeguard them during sudden attacks. The amount of data that are gathered from various sensors is numerous. In this paper, we propose to use fog computing to help monitor patients suffering from chronic diseases such that the data are collected and processed in an efficient manner. The main challenge would be to only sort out context-sensitive data that are relevant to the health of the patient. Just having a simple sensor-to-cloud architecture is not viable, and this is where having a fog computing layer makes a difference. This increases the efficiency of the entire system, as it not only reduces the amount of data that is transported back and forth between the cloud and the sensors but also eliminates the risk that a data center failure bears with it. We also analyze the security and deployment issues of this fog computing layer.
98 citations
TL;DR: An inexpensive, off-the-shelf, and contactless measuring system for respiration signals taking as region of interest the pit of the neck, which demonstrates good performances for contactless monitoring of both breathing pattern and breath-by-breath respiratory rate over time.
Abstract: Vital signs monitoring is pivotal not only in clinical settings but also in home environments. Remote monitoring devices, systems, and services are emerging as tracking vital signs must be performed on a daily basis. Different types of sensors can be used to monitor breathing patterns and respiratory rate. However, the latter remains the least measured vital sign in several scenarios due to the intrusiveness of most adopted sensors. In this paper, we propose an inexpensive, off-the-shelf, and contactless measuring system for respiration signals taking as region of interest the pit of the neck. The system analyses video recorded by a single RGB camera and extracts the respiratory pattern from intensity variations of reflected light at the level of the collar bones and above the sternum. Breath-by-breath respiratory rate is then estimated from the processed breathing pattern. In addition, the effect of image resolution on monitoring breathing patterns and respiratory rate has been investigated. The proposed system was tested on twelve healthy volunteers (males and females) during quiet breathing at different sensor resolution (i.e., HD 720, PAL, WVGA, VGA, SVGA, and NTSC). Signals collected with the proposed system have been compared against a reference signal in both the frequency domain and time domain. By using the HD 720 resolution, frequency domain analysis showed perfect agreement between average breathing frequency values gathered by the proposed measuring system and reference instrument. An average mean absolute error (MAE) of 0.55 breaths/min was assessed in breath-by-breath monitoring in the time domain, while Bland-Altman showed a bias of −0.03 ± 1.78 breaths/min. Even in the case of lower camera resolution setting (i.e., NTSC), the system demonstrated good performances (MAE of 1.53 breaths/min, bias of −0.06 ± 2.08 breaths/min) for contactless monitoring of both breathing pattern and breath-by-breath respiratory rate over time.
77 citations
TL;DR: A hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier is proposed, which does not require expert knowledge in extracting features, and is more suitable to classify the extracted features and shortens the runtime.
Abstract: Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.
55 citations
TL;DR: The developed system was successfully able to monitor parameter variations, based on specific events, and low-cost sensor modules reduce the overall cost to provide a comprehensive, portable, and real-time monitoring solution.
Abstract: This article describes the design and development of a low-cost, portable monitoring system for indoor environment quality (IEQ). IEQ is a holistic concept that encompasses elements of indoor air quality (IAQ), indoor lighting quality (ILQ), acoustic comfort, and thermal comfort (temperature and relative humidity). The unit is intended for the monitoring of temperature, humidity, PM2.5, PM10, total VOCs (×3), CO2, CO, illuminance, and sound levels. Experiments were conducted in various environments, including a typical indoor working environment and outdoor pollution, to evaluate the unit’s potential to monitor IEQ parameters. The developed system was successfully able to monitor parameter variations, based on specific events. A custom IEQ index was devised to rate the parameter readings with a simple scoring system to calculate an overall IEQ percentage. The advantages of the proposed system, with respect to commercial units, is associated with better customisation and flexibility to implement a variety of low-cost sensors. Moreover, low-cost sensor modules reduce the overall cost to provide a comprehensive, portable, and real-time monitoring solution. This development facilities researchers and interested enthusiasts to become engaged and proactive in participating in the study, management, and improvement of IEQ.
54 citations
TL;DR: The use of an autoencoder in DL models can improve the accuracy of building recognition in fused LiDAR–orthophoto data and comparison experiments with the support vector machine (SVM) show that the proposed model with or without dimensionality reduction outperforms the SVM models in the working area.
Abstract: This paper reports on a building detection approach based on deep learning (DL) using the fusion of Light Detection and Ranging (LiDAR) data and orthophotos. The proposed method utilized object-based analysis to create objects, a feature-level fusion, an autoencoder-based dimensionality reduction to transform low-level features into compressed features, and a convolutional neural network (CNN) to transform compressed features into high-level features, which were used to classify objects into buildings and background. The proposed architecture was optimized for the grid search method, and its sensitivity to hyperparameters was analyzed and discussed. The proposed model was evaluated on two datasets selected from an urban area with different building types. Results show that the dimensionality reduction by the autoencoder approach from 21 features to 10 features can improve detection accuracy from 86.06% to 86.19% in the working area and from 77.92% to 78.26% in the testing area. The sensitivity analysis also shows that the selection of the hyperparameter values of the model significantly affects detection accuracy. The best hyperparameters of the model are 128 filters in the CNN model, the Adamax optimizer, 10 units in the fully connected layer of the CNN model, a batch size of 8, and a dropout of 0.2. These hyperparameters are critical to improving the generalization capacity of the model. Furthermore, comparison experiments with the support vector machine (SVM) show that the proposed model with or without dimensionality reduction outperforms the SVM models in the working area. However, the SVM model achieves better accuracy in the testing area than the proposed model without dimensionality reduction. This study generally shows that the use of an autoencoder in DL models can improve the accuracy of building recognition in fused LiDAR–orthophoto data.
52 citations
TL;DR: Compared with autoregressive and moving average (ARMA), random forest (RF), support vector machine (SVM), and back propagation neural networks (BPNNs), LSTM not only performs as well as ARMA in real-time rainfall prediction but also outperforms the other four models in seasonal rainfall pattern description and seasonal real- time rainfall prediction.
Abstract: A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems’ (MEMS) sensors can provide high time and spatial resolution of weather parameter measurement, but they suffer from stochastic measurement error. In order to apply MEMS sensors in real-time rainfall prediction in Wuhan, firstly, seasonal trend decomposition using Loess (STL) algorithm is utilized to decompose the observed time series into trend, seasonal, and remainder components. The trend of the observed series is compared with the corresponding trend of the data downloaded from the authoritative website with the same weather parameter in terms of Euclidean distance and cosine similarity. The similarity demonstrates that the observation of MEMS sensors is believable. Secondly, the long short-term memory (LSTM) is used to predict the real-time rainfall based on the observed data. Compared with autoregressive and moving average (ARMA), random forest (RF), support vector machine (SVM), and back propagation neural networks (BPNNs), LSTM not only performs as well as ARMA in real-time rainfall prediction but also outperforms the other four models in seasonal rainfall pattern description and seasonal real-time rainfall prediction. Our experiment results show that more detailed, timely, and accurate rainfall prediction can be achieved by using LSTM on the MEMS weather sensors.
52 citations
TL;DR: It can be obtained that, firstly, acoustic variability of cough sounds within and between individuals makes it difficult to assess the intensity of coughing.
Abstract: Cough is a common symptom of many respiratory diseases. Many medical literatures underline that a system for the automatic, objective, and reliable detection of cough events is important and very promising to detect pathology severity in chronic cough disease. In order to track the development status of an audio-based cough monitoring system, we briefly described the history of objective cough detection and then illustrated the cough sound generating principle. The probable endpoints of cough clinical studies, including cough frequency, intensity of coughing, and acoustic properties of cough sound, were analyzed in this paper. Finally, we introduce some successful cough monitoring equipment and their recognition algorithm in detail. It can be obtained that, firstly, acoustic variability of cough sounds within and between individuals makes it difficult to assess the intensity of coughing. Furthermore, now great progress in audio-based cough detection is being made. Moreover, accurate portable objective monitoring systems will be available and widely used in home care and clinical trials in the near future.
TL;DR: A microfluidic biosensor using a microwave substrate-integrated waveguide (SIW) cavity resonator to detect and analyze biomaterial using tiny liquid volumes (3 μL).
Abstract: A microfluidic biosensor is proposed using a microwave substrate-integrated waveguide (SIW) cavity resonator. The main objectives of this noninvasive biosensor are to detect and analyze biomaterial using tiny liquid volumes (3 μL). The sensing mechanism of our proposed biosensor relies on the dielectric perturbation phenomenon of biomaterial under test, which causes a change in resonance frequency and return loss (amplitude). First, an SIW cavity is realized on a Rogers RT/Duroid 5870 substrate. Then, a microwell made from polydimethylsiloxane (PDMS) material is loaded on the SIW cavity to observe the perturbation phenomenon. The microwell is filled with phosphate-buffered saline (PBS) solution (reference biological medium). To demonstrate the sensing behavior, the fibroblast (FB) cells from the lungs of a human male subject are analyzed and one-port S-parameters are measured. The resonance frequency of the structure with FB cells is observed to be 13.48 GHz. The reproducibility and repeatability of our proposed biosensor are successfully demonstrated through full-wave simulations and measurements. The resonance frequency of the FB-loaded microwell showed a shift of 170 MHz and 20 MHz, when compared to those of empty and PBS-loaded microwells. Its analytical limit of detection is 213 cells/μL. Our proposed biosensor is noncontact and reliable. Furthermore, it is miniaturized, inexpensive, and fabricated using simple- and easy-design processes.
TL;DR: A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting and the relationship between tree’s height and age was significant which supports the polynomial regression function (PRF) model with a kernel size for under 10–12-year-old oil palm trees.
Abstract: The current study proposes a new method for oil palm age estimation and counting from Worldview-3 satellite image and light detection and range (LiDAR) airborne imagery. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. The sensitivity analysis was conducted on four SVM kernel types with associated segmentation parameters to obtain the optimal crown coverage delineation. Extracting tree’s crown was integrated with height model and multiregression methods to accurately estimate the age of trees. The multiregression model with multikernel sizes was examined to achieve the most optimized model for age estimation. Applied models were trained and examined over five different oil palm plantations. The results of oil palm counting had an overall accuracy of 98.80%, while the overall accuracy of age estimation showed 84.91%, over all blocks. The relationship between tree’s height and age was significant which supports the polynomial regression function (PRF) model with a kernel size for under 10–12-year-old oil palm trees, while exponential regression function (ERF) is more fitted for older trees (i.e., 22 years old). Overall, recent remote sensing dataset and machine learning techniques are useful in monitoring and detecting oil palm plantation to maximize productivity.
TL;DR: A genetic algorithm (GA) is proposed to obtain the fuzzy rules of a energy-balanced routing protocol (EBRP) for wireless sensor networks, which prolongs the network lifetime by 57%, 63%, and 63%, respectively.
Abstract: The wireless sensor network is an intelligent self-organizing network which consists of many sensor nodes deployed in the monitoring area. The greatest challenge of designing a wireless sensor network is to balance the energy consumption and prolong the lifetime of the network, seeing that the nodes can be powered only by batteries in most conditions. An energy-balanced routing protocol (EBRP) for wireless sensor networks is proposed in this paper. In EBRP, we divide the network into several clusters by using K-means++ algorithm and select the cluster head by using the fuzzy logical system (FLS). Since the previous researches did not demonstrate how to get the fuzzy rules for different networks, we propose a genetic algorithm (GA) to obtain the fuzzy rules. We code the rules as a chromosome, and the lifetime of the network is treated as a fit function. Then, through the selection, crossover, and mutation of each generation, the best offspring can be decoded as the best rule for each network model. Through the simulation, comparing with the existing routing protocols such as low-energy adaptive clustering hierarchy (LEACH), low-energy adaptive clustering hierarchy-centralized (LEACH-C), and stable election protocol (SEP), the EBRP prolongs the network lifetime (first node dies) by 57%, 63%, and 63%, respectively.
TL;DR: This research shows that CNN-based models are robust for land cover classification using aerial photographs, however, the architecture and hyperparameters of these models should be carefully selected and optimized.
Abstract: Classification of aerial photographs relying purely on spectral content is a challenging topic in remote sensing. A convolutional neural network (CNN) was developed to classify aerial photographs into seven land cover classes such as building, grassland, dense vegetation, waterbody, barren land, road, and shadow. The classifier utilized spectral and spatial contents of the data to maximize the accuracy of the classification process. CNN was trained from scratch with manually created ground truth samples. The architecture of the network comprised of a single convolution layer of 32 filters and a kernel size of 3 × 3, pooling size of 2 × 2, batch normalization, dropout, and a dense layer with Softmax activation. The design of the architecture and its hyperparameters were selected via sensitivity analysis and validation accuracy. The results showed that the proposed model could be effective for classifying the aerial photographs. The overall accuracy and Kappa coefficient of the best model were 0.973 and 0.967, respectively. In addition, the sensitivity analysis suggested that the use of dropout and batch normalization technique in CNN is essential to improve the generalization performance of the model. The CNN model without the techniques above achieved the worse performance, with an overall accuracy and Kappa of 0.932 and 0.922, respectively. This research shows that CNN-based models are robust for land cover classification using aerial photographs. However, the architecture and hyperparameters of these models should be carefully selected and optimized.
TL;DR: The vegetation indices from Sentinel-2 multispectral imagery can provide a good result in terms of reporting the AGB on private forests, and it indicates that private forests are good for biomass storage.
Abstract: Private forests have a crucial role in maintaining the functioning of the Indonesian forest ecosystem especially because of the continuous degradation of natural forests. Private forests are a part of social forestry which becomes a tool for the Indonesian government to reduce carbon dioxide (CO2) emission by 26% by 2030. The United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation has encouraged the Indonesian government to establish a forest monitoring system by estimating forest carbon stock using a combination of forest inventory and remote sensing. This study is aimed at assessing the potential of vegetation indices derived from Sentinel-2 for estimating aboveground biomass (AGB) of private forests. We used 45 sample plots and 7 vegetation indices to evaluate the ability of Sentinel-2 in estimating AGB on private forests. Normalised difference index (NDI) 45 exhibited a strong correlation with AGB compared to other indices (r = 0.89; R2 = 0.79). Stepwise linear regression fitted for establishing the model between field AGB and vegetation indices (R2 = 0.81). We also found that AGB in the study area based on spatial analysis was 72.54 Mg/ha. A root mean square error (RMSE) value from predicted and observed AGB was 27 Mg/ha. The AGB value in the study area is higher than the AGB value from some of forest types, and it indicates that private forests are good for biomass storage. Overall, vegetation indices from Sentinel-2 multispectral imagery can provide a good result in terms of reporting the AGB on private forests.
TL;DR: The simulation results show that the thermal comfort-based control is more effective to maintaining occupants’ thermal satisfaction and is therefore recommended for use providing human care services using IoT platforms in smart buildings.
Abstract: This paper presents an Internet of Things (IoT) platform for a smart building which provides human care services for occupants. The individual health profiles of the occupants are acquired by the IoT-based smart building, which uses the accumulated knowledge of the occupants to provide better services. To ensure the thermal comfort of the occupants inside the building, we propose a dynamic thermal model of occupants. This model is based on the heat balance equation of human body and thermal characteristics of the occupants. We implement this model in two smart building models with heaters controlled by a temperature and thermal comfort index using MATLAB/Simulink®. The simulation results show that the thermal comfort-based control is more effective to maintaining occupants’ thermal satisfaction and is therefore recommended for use providing human care services using IoT platforms in smart buildings.
TL;DR: A novel hybrid model is proposed with the purpose of detecting network intrusion effectively and experimental results show that the proposed model is superior to the compared methods.
Abstract: In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods.
TL;DR: A novel bimodal optoelectronic sensor based on Fresnel lenses and the associated stereo-recording device that records the wingbeat event of an insect in flight as backscattered and extinction light is presented.
Abstract: We present a novel bimodal optoelectronic sensor based on Fresnel lenses and the associated stereo-recording device that records the wingbeat event of an insect in flight as backscattered and extinction light. We investigate the complementary information of these two sources of biometric evidence and we finally embed part of this technology in an electronic e-trap for fruit flies. The e-trap examines the spectral content of the wingbeat of the insect flying in and reports wirelessly counts and species identity. We design our devices so that they are optimized in terms of detection accuracy and power consumption, but above all, we ensure that they are affordable. Our aim is to make more widespread the use of electronic insect traps that report in virtually real time the level of the pest population from the field straight to a human controlled agency. We have the vision to establish remote automated monitoring for all insects of economic and hygienic importance at large spatial scales, using their wingbeat as biometric evidence. To this end, we provide open access to the implementation details, recordings, and classification code we developed.
TL;DR: Novel electromagnetic bridge energy harvesters utilizing bridge vibrations and ambient wind surges to power wireless sensor nodes used for bridges’ health monitoring are presented.
Abstract: This paper presents novel electromagnetic bridge energy harvesters (BEHs) utilizing bridge vibrations and ambient wind surges to power wireless sensor nodes used for bridges’ health monitoring. The developed BEHs are cantilever-type and are comprised of a wound coil, permanent magnet, an airfoil, cantilever beam, and a support. Harvesters are characterized in-lab under different vibration levels and are subjected to variable speed air surges. The harvesters exhibit multiresonant frequencies; prototype I has resonant frequencies of 3.6, 14.9, and 17.6 Hz. However, 7.6, 33, and 45 Hz are the resonant frequencies for prototype II. Under vibration testing, prototype I produced a maximum voltage of 206 mV and an optimum power of 354.51 μW at a frequency of 3.6 Hz and 0.4 acceleration. However, at a frequency of 7.6 Hz and 0.6 acceleration, prototype II showed the capability of generating a maximum voltage of 430 mV and an optimum power of 2214.32 μW. Moreover, when BEHs are characterized under variable speed air surges, prototype I generated a load voltage of 19 mV and a power of 7.84 μW at an air speed of 9 m/s; however, 22 mV and 9.14 μW load voltage and power, respectively, are developed by prototype II at 6 m/s air speed.
TL;DR: An improved threshold segmentation method that combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method and achieves good performance of hand detection and positioning at different distances.
Abstract: Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances.
TL;DR: A comprehensive survey of wearable computing as a research field and a systematic review of recent work specifically on wrist-worn wearables is presented.
Abstract: Wearable technology impacts the daily life of its users. Wearable devices are defined as devices embedded within clothes, watches, or accessories. Wrist-worn devices, as a type of wearable devices, have gained popularity among other wearable devices. They allow quick access to vital information, and they are suitable for many applications. This paper presents a comprehensive survey of wearable computing as a research field and provides a systematic review of recent work specifically on wrist-worn wearables. The focus of this research is on wrist-worn wearable studies because there is a lack of systematic literature reviews related to this area. This study reviewed journal and conference articles from 2015 and 2017 with some studies from 2014 and 2018, resulting in a selection of 54 studies that met the selection criteria. The literature showed that research in wrist-worn wearables spans three domains, namely, user interface and interaction studies, user studies, and activity/affect recognition studies. Our study then concludes with challenges and open research directions.
TL;DR: This paper has classified agriculture into five categories and reviewed the state-of-the-art technology in practice and ongoing research in each of these areas, and discussed current and future challenges.
Abstract: The application of sensors and information and communication technology (ICT) in agriculture has played a vital role in improving agricultural production and the value chain. Recently, the use of data analytics has shifted agriculture from input-intensive to knowledge-intensive as a large amount of agricultural data can be stored, shared, and analyzed to create information. In this paper, we have reviewed existing sensors and data analytics techniques used in different areas of agriculture. We have classified agriculture into five categories and reviewed the state-of-the-art technology in practice and ongoing research in each of these areas. Also, we have presented a case study of Korean scenario compared with other developed nations and addressed some of the issues associated with it. Finally, we have discussed current and future challenges and provided our views on how such issues can be addressed.
TL;DR: A detection and isolation technique for sensor calibration drifts on the purpose of measurement validation was developed and Artificial neural networks were chosen for this purpose considering their high performance in nonlinear environments.
Abstract: This paper proposes a method for sensor validation and fault detection in wind turbines. Ensuring validity of sensor measurements is a significant part in overall condition monitoring as sensor faults lead to incorrect results in monitoring a system’s state of health. Although identifying abrupt failures in sensors is relatively straightforward, calibration drifts are more difficult to detect. Therefore, a detection and isolation technique for sensor calibration drifts on the purpose of measurement validation was developed. Temperature sensor measurements from the Supervisory Control and Data Acquisition system of a wind turbine were used for this aim. Low output rate of the measurements and nonlinear characteristics of the system drive the necessity to design an advanced fault detection algorithm. Artificial neural networks were chosen for this purpose considering their high performance in nonlinear environments. The results demonstrate that the proposed method can effectively detect existence of calibration drift and isolate the exact sensor with faulty behaviour.
TL;DR: In contrast to the current setups, the proposed methodology allows the identification of poses characterized by different configurations of fingers and wrist joint displacements with the utilization of only 3 transducers and a simple interrogation scheme, being suitable to further applications in human-computer interfaces.
Abstract: A low-cost optical fiber force myography sensor for noninvasive hand posture identification is proposed. The transducers are comprised of 10 mm periodicity silica multimode fiber microbending devices mounted in PVC plates, providing 0.05 N−1 sensitivity over ~20 N range. Next, the transducers were attached to the user forearm by means of straps in order to monitor the posterior proximal radial, the anterior medial ulnar, and the posterior distal radial muscles, and the acquired FMG optical signals were correlated to the performed gestures using a 5 hidden layers, 20-neuron artificial neural network classifier with backpropagation architecture, followed by a competitive layer. The overall results for 9 postures and 6 subjects indicated a 98.4% sensitivity and 99.7% average accuracy, being comparable to the electromyographic approaches. Moreover, in contrast to the current setups, the proposed methodology allows the identification of poses characterized by different configurations of fingers and wrist joint displacements with the utilization of only 3 transducers and a simple interrogation scheme, being suitable to further applications in human-computer interfaces.
TL;DR: The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for the database in comparison with others that can be found in the literature.
Abstract: The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a “DrunkSpace Classifier.” The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.
TL;DR: This study gets the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area and finds that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.
Abstract: Precipitable water vapor (PWV) content detection is vital to heavy rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In general, these methods can be divided into two categories, ground-based or space-based. In this study, we analyze the advantages and disadvantages of these technologies, compare retrieved atmosphere parameters by different RO (radio occultation) observations, like FORMOSAT-3/COSMIC (Formosa Satellite-3 and Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3C (China Feng Yun 3C), and assess retrieved PWV precision with a radiosonde. Besides, we interpolate PWV from NWP (numerical weather prediction) reanalysis data for more comparison and analysis with RO. Specifically, ground-based GNSS is of high precision and continuous availability to monitor PWV distribution; in our paper, we show cases to validate and compare GNSS retrieving PWV with a radiosonde. Except GNSS PWV, we give two different radio occultation sounding results, COSMIC and FY3C, to validate the precision to monitor PWV from space in a global area. FY3C results containing Beidou (China Beidou Global Satellite Navigation System) radio occultation events need to be emphasized. So, in our study, we get the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area. Besides, other space-based methods, such as microwave satellite, are also useful in detecting PWV distribution situations in a global area from space; in this study, we present a case of retrieved PWV using microwave satellite observation. NWP reanalysis data ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim and the new-generation reanalysis data ERA5 provide global grid atmosphere parameters, like surface temperature, different-level pressures, and precipitable water. We show cases of retrieved PWV and validate the precision with radiosonde results and compare new reanalysis dataset ERA5 with ERA-Interim, finding that ERA5 can get higher precision-retrieved atmosphere parameters and PWV. In the end, from our comparison, we find that the retrieved PWV from RO (FY3C and COSMIC) and ECMWF reanalysis data (ERA-Interim and ERA5) have a high positive correlation and that almost all values exceed 0.9, compare retrieved PWV with a radiosonde, and find that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.
TL;DR: A new security-aware routing technique called VANSec that is more immune and resistive against different kinds of attacks and thwarts malicious node penetration attempts to the entire network is presented.
Abstract: VANET is an application and subclass of MANETs, a quickly maturing, promising, and emerging technology these days. VANETs establish communication among vehicles (V2V) and roadside infrastructure (V2I). As vehicles move with high speed, hence environment and topology change with time. There is no optimum routing protocol which ensures full-pledge on-time delivery of data to destination nodes, and an absolutely optimum scheme design for flawless packet exchange is still a challenging task. In VANETs, accurate and on-time delivery of fundamental safety alert messages (FSAMs) is highly important to withstand against maliciously inserted security threats affectively. In this paper, we have presented a new security-aware routing technique called VANSec. The presented scheme is more immune and resistive against different kinds of attacks and thwarts malicious node penetration attempts to the entire network. It is basically based on trust management approach. The aim of the scheme is to identify malicious data and false nodes. The simulation results of VANSec are compared with already existing techniques called trust and LT in terms of trust computation error (TCE), end-to-end delay (EED), average link duration (ALD), and normalized routing overhead (NRO). In terms of TCE, VANSec is 11.6% and 7.3% efficient than LT and trust, respectively, while from EED comparison we found VANSec to be 57.6% more efficient than trust and 5.2% more efficient than LT. Similarly, in terms of ALD, VANSec provides 29.7% and 7.8% more stable link duration than trust and LT do, respectively, and in terms of NRO, VANSec protocol has 27.5% and 14% lesser load than that of trust and LT, respectively.
TL;DR: A system to monitor soil moisture using standard UHF RFID tags buried on the soil, which allows manual or automatic reading through irrigation systems or other systems to control irrigation systems and an autonomous mobile robot, which is capable to navigate on the field and automatically read the sensors.
Abstract: This paper presents a system to monitor soil moisture using standard UHF RFID tags buried on the soil. An autonomous mobile robot is also presented, which is capable to navigate on the field and automatically read the sensors, even if they are completely buried on the soil. Thus, passive RFID tags are buried on the soil, allowing wireless moisture measurement without the need of batteries for long periods. The system dispenses external cables and antennas and may be composed of a single RFID tag buried on the soil or by several RFID tags buried at different depths on the soil. An antenna coupled to a RFID reader can be pointed to the place of installation of these tags, and by measuring the received signal strength indicator (RSSI) and other parameters, it allows to estimate the amount of water on the soil. The estimation of volumetric water content (VWC) on the soil was successfully obtained and calibrated with using neural networks trained with experimental data from a reference capacitive soil moisture sensor. In addition to the simplified installation procedure, the system allows manual or automatic reading through irrigation systems or other systems to control irrigation systems. The system has been evaluated in several experiments, and nine tags were buried on the field, being used for at least three years. Experimental results show that it is possible to read tags at 40 cm deep in the soil with the RFID reader antenna 10 cm far from the soil surface.
TL;DR: The mechanical, electrical, and autonomy aspects of designing a novel, modular, and reconfigurable cleaning robot, dubbed as sTetro (stair Tetro), are presented, which is capable of navigating over flat surfaces as well as staircases and significantly extending the automated cleaning capabilities as compared to conventional home cleaning robots.
Abstract: The mechanical, electrical, and autonomy aspects of designing a novel, modular, and reconfigurable cleaning robot, dubbed as sTetro (stair Tetro), are presented. The developed robotic platform uses a vertical conveyor mechanism to reconfigure itself and is capable of navigating over flat surfaces as well as staircases, thus significantly extending the automated cleaning capabilities as compared to conventional home cleaning robots. The mechanical design and system architecture are introduced first, followed by a detailed description of system modelling and controller design efforts in sTetro. An autonomy algorithm is also proposed for self-reconfiguration, locomotion, and autonomous navigation of sTetro in the controlled environment, for example, in homes/offices with a flat floor and a straight staircase. A staircase recognition algorithm is presented to distinguish between the surrounding environment and the stairs. The misalignment detection technique of the robot with a front staircase riser is also given, and a feedback from the IMU sensor for misalignment corrective measures is provided. The experiments performed with the sTetro robot demonstrated the efficacy and validity of the developed system models, control, and autonomy approaches.