Showing papers in "Journal of Sensors in 2015"
TL;DR: Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
Abstract: Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
1,316 citations
TL;DR: Smartphones have become a useful tool in agriculture because their mobility matches the nature of farming, the cost of the device is highly accessible, and their computing power allows a variety of practical applications to be created.
Abstract: Smartphones have become a useful tool in agriculture because their mobility matches the nature of farming, the cost of the device is highly accessible, and their computing power allows a variety of practical applications to be created. Moreover, smartphones are nowadays equipped with various types of physical sensors which make them a promising tool to assist diverse farming tasks. This paper systematically reviews smartphone applications mentioned in research literature that utilize smartphone built-in sensors to provide agricultural solutions. The initial 1,500 articles identified through database search were screened based on exclusion criteria and then reviewed thoroughly in full text, resulting in 22 articles included in this review. The applications are categorized according to their agricultural functions. Those articles reviewed describe 12 farming applications, 6 farm management applications, 3 information system applications, and 4 extension service applications. GPS and cameras are the most popular sensors used in the reviewed papers. This shows an opportunity for future applications to utilize other sensors such as accelerometer to provide advanced agricultural solutions.
151 citations
TL;DR: It is found that transducers and materials such as piezoresistive and polymer, respectively, are used in order to improve the sensing sensitivity for grasping mechanisms in future.
Abstract: We survey the state of the art in a variety of force sensors for designing and application of robotic hand. Most of the force sensors are examined based on tactile sensing. For a decade, many papers have widely discussed various sensor technologies and transducer methods which are based on microelectromechanical system (MEMS) and silicon used for improving the accuracy and performance measurement of tactile sensing capabilities especially for robotic hand applications. We found that transducers and materials such as piezoresistive and polymer, respectively, are used in order to improve the sensing sensitivity for grasping mechanisms in future. This predicted growth in such applications will explode into high risk tasks which requires very precise purposes. It shows considerable potential and significant levels of research attention.
128 citations
TL;DR: Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively.
Abstract: Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling). Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.
105 citations
TL;DR: Experimental results show that the DBN-based method outperforms three other approaches and produces homogenous mapping results with preserved shape details.
Abstract: Land use and land cover (LULC) mapping in urban areas is one of the core applications in remote sensing, and it plays an important role in modern urban planning and management. Deep learning is springing up in the field of machine learning recently. By mimicking the hierarchical structure of the human brain, deep learning can gradually extract features from lower level to higher level. The Deep Belief Networks (DBN) model is a widely investigated and deployed deep learning architecture. It combines the advantages of unsupervised and supervised learning and can archive good classification performance. This study proposes a classification approach based on the DBN model for detailed urban mapping using polarimetric synthetic aperture radar (PolSAR) data. Through the DBN model, effective contextual mapping features can be automatically extracted from the PolSAR data to improve the classification performance. Two-date high-resolution RADARSAT-2 PolSAR data over the Great Toronto Area were used for evaluation. Comparisons with the support vector machine (SVM), conventional neural networks (NN), and stochastic Expectation-Maximization (SEM) were conducted to assess the potential of the DBN-based classification approach. Experimental results show that the DBN-based method outperforms three other approaches and produces homogenous mapping results with preserved shape details.
103 citations
TL;DR: This paper describes a scalable and distributed architecture for sensor data collection, storage, and analysis that uses GPS sensors as data source and run machine-learning algorithms for data analysis.
Abstract: Sensors are becoming ubiquitous. From almost any type of industrial applications to intelligent vehicles, smart city applications, and healthcare applications, we see a steady growth of the usage of various types of sensors. The rate of increase in the amount of data produced by these sensors is much more dramatic since sensors usually continuously produce data. It becomes crucial for these data to be stored for future reference and to be analyzed for finding valuable information, such as fault diagnosis information. In this paper we describe a scalable and distributed architecture for sensor data collection, storage, and analysis. The system uses several open source technologies and runs on a cluster of virtual servers. We use GPS sensors as data source and run machine-learning algorithms for data analysis.
99 citations
TL;DR: Comprehensive advantageous features regarding microsizing and ultralow power consumption, high linearity without any hysteresis for the magnetic field detection, high sensitivity for magnetic field Detection with a Pico-Tesla resolution, and quick response for detection of magnetic field are based on the magneto-impedance effect in the amorphous wires.
Abstract: We analyzed and organized the reasons why the amorphous wire CMOS IC magneto-impedance sensor (MI sensor) has rapidly been mass-produced as the electronic compass chips for the smart phones, mobile phones, and the wrist watches. Comprehensive advantageous features regarding six terms of (1) microsizing and ultralow power consumption, (2) high linearity without any hysteresis for the magnetic field detection, (3) high sensitivity for magnetic field detection with a Pico-Tesla resolution, (4) quick response for detection of magnetic field, (5) high temperature stability, and (6) high reversibility against large disturbance magnetic field shock are based on the magneto-impedance effect in the amorphous wires. We have detected the biomagnetic field using the Pico-Tesla resolution MI sensor at the room temperature such as the magneto-cardiogram (MCG), the magneto-encephalogram (MEG), and the self-oscillatory magnetic field of guinea-pig stomach smooth muscles (in vitro) that suggest the origin of the biomagnetic field is probably pulsive flow of Ca2
78 citations
TL;DR: A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed to consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors.
Abstract: A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.
75 citations
TL;DR: This review focuses on the current status of research in the use of NPs within coatings in optical fiber sensing, and most used sensing principles in fiber optics are briefly described and classified into several groups.
Abstract: The use of nanoparticles (NPs) in scientific applications has attracted the attention of many researchers in the last few years. The use of NPs can help researchers to tune the physical characteristics of the sensing coating (thickness, roughness, specific area, refractive index, etc.) leading to enhanced sensors with response time or sensitivity better than traditional sensing coatings. Additionally, NPs also offer other special properties that depend on their nanometric size, and this is also a source of new sensing applications. This review focuses on the current status of research in the use of NPs within coatings in optical fiber sensing. Most used sensing principles in fiber optics are briefly described and classified into several groups: absorbance-based sensors, interferometric sensors, fluorescence-based sensors, fiber grating sensors, and resonance-based sensors, among others. For each sensor group, specific examples of the utilization of NP-embedded coatings in their sensing structure are reported.
69 citations
TL;DR: This work demonstrates that SWCNTs-Nafion film can improve the sensitivity, selectivity, reproducibility, and stability, making it an ideal candidate for electrochemical detection of 8-OHdG.
Abstract: 8-Hydroxy-2′-deoxyguanosine (8-OHdG) is a typical biomarker of oxidative DNA damage and has attracted much attention in recent years since the level of 8-OHdG in body fluids is typically associated with various diseases. In this work, a simple and highly sensitive electrochemical sensor for the determination of 8-OHdG was fabricated incorporating single wall carbon nanotubes- (SWCNTs-) Nafion composite film coated on glassy carbon electrode. Nafion was chosen as an optimal adhesive agent from a series of adhesive agents and acted as a binder, enrichment, and exclusion film. Due to the strong cation-exchange ability of Nafion and the outstanding electronic properties of SWCNTs, the prepared SWCNTs-Nafion film can strongly enhance the electrochemical response to oxidation of 8-OHdG and efficiently alleviate the interferences from uric acid and ascorbic acid. The oxidation peak currents are linear with the concentration of 8-OHdG in the range of 0.03 to 1.25 μM with a detection limit of 8.0 nM (S/N = 3). This work demonstrates that SWCNTs-Nafion film can improve the sensitivity, selectivity, reproducibility, and stability, making it an ideal candidate for electrochemical detection of 8-OHdG.
64 citations
TL;DR: A new clustering algorithm, Genetic Algorithm based Energy efficient Clustering Hierarchy (GAECH) algorithm, is proposed to increase FND, HND, and LND with a novel fitness function, which increases both the stability period and lifetime of the network.
Abstract: Clustering the Wireless Sensor Networks (WSNs) is the major issue which determines the lifetime of the network. The parameters chosen for clustering should be appropriate to form the clusters according to the need of the applications. Some of the well-known clustering techniques in WSN are designed only to reduce overall energy consumption in the network and increase the network lifetime. These algorithms achieve increased lifetime, but at the cost of overloading individual sensor nodes. Load balancing among the nodes in the network is also equally important in achieving increased lifetime. First Node Die (FND), Half Node Die (HND), and Last Node Die (LND) are the different metrics for analysing lifetime of the network. In this paper, a new clustering algorithm, Genetic Algorithm based Energy efficient Clustering Hierarchy (GAECH) algorithm, is proposed to increase FND, HND, and LND with a novel fitness function. The fitness function in GAECH forms well-balanced clusters considering the core parameters of a cluster, which again increases both the stability period and lifetime of the network. The experimental results also clearly indicate better performance of GAECH over other algorithms in all the necessary aspects.
TL;DR: Experimental results show that the algorithm can effectively isolate the malicious node in the network and reduce the consumption of energy of the whole network.
Abstract: Wireless sensor network (WSN) is a kind of distributed and self-organizing networks, in which the sensor nodes have limited communication bandwidth, memory, and limited energy. The topology construction of this network is usually vulnerable when attacked by malicious nodes. Besides, excessive energy consumption is a problem that can not be ignored. Therefore, this paper proposes a secure topology protocol of WSN which is trust-aware and of low energy consumption, called TLES. The TLES considers the trust value as an important factor affecting the behavior of node. In detail, the TLES would take trust value, residual energy of the nodes, and node density into consideration when selecting cluster head nodes. Then, TLES constructs these cluster head nodes by choosing the next hop node according to distance to base station (BS), nodes’ degrees, and residual energy, so as to establish a safe, reliable, and energy saving network. Experimental results show that the algorithm can effectively isolate the malicious node in the network and reduce the consumption of energy of the whole network.
TL;DR: Simulation results show that, in comparison to previously proposed routing protocols, namely, AODV-EHA and LTB-AODV (Light-Weight Trust-Based Routing Protocol), the proposed ETARP can keep the same security level while achieving more energy efficiency for data packet delivery.
Abstract: This paper presents a new routing protocol called Secure and Energy Aware Routing Protocol (ETARP) designed for energy efficiency and security for wireless sensor networks (WSNs). ETARP attempts to deal with WSN applications operating in extreme environments such as the battlefield. The key part of the routing protocol is route selection based on utility theory. The concept of utility is a novel approach to simultaneously factor energy efficiency and trustworthiness of routes in the routing protocol. ETARP discovers and selects routes on the basis of maximum utility with incurring additional cost in overhead compared to the common AODV (Ad Hoc On Demand Distance Vector) routing protocol. Simulation results show that, in comparison to previously proposed routing protocols, namely, AODV-EHA and LTB-AODV (Light-Weight Trust-Based Routing Protocol), the proposed ETARP can keep the same security level while achieving more energy efficiency for data packet delivery.
TL;DR: An in situ wireless SHM system based on an acoustic emission (AE) technique that localization of acoustic sources which could emulate impact damage or audible cracks caused by different objects, such as tools, bird strikes, or strong hail are proposed.
Abstract: Structural health monitoring (SHM) is important for reducing the maintenance and operation cost of safety-critical components and systems in offshore wind turbines. This paper proposes an in situ wireless SHM system based on an acoustic emission (AE) technique. By using this technique a number of challenges are introduced due to high sampling rate requirements, limitations in the communication bandwidth, memory space, and power resources. To overcome these challenges, this paper focused on two elements: (1) the use of an in situ wireless SHM technique in conjunction with the utilization of low sampling rates; (2) localization of acoustic sources which could emulate impact damage or audible cracks caused by different objects, such as tools, bird strikes, or strong hail, all of which represent abrupt AE events and could affect the structural health of a monitored wind turbine blade. The localization process is performed using features extracted from aliased AE signals based on a developed constraint localization model. To validate the performance of these elements, the proposed system was tested by testing the localization of the emulated AE sources acquired in the field.
TL;DR: A low cost, easy to fabricate biosensor, which can quickly and accurately detect Salmonella typhimurium and shows that the microfluidic biosensor has 10-fold increased sensitivity.
Abstract: We present a low cost, easy to fabricate biosensor, which can quickly and accurately detect Salmonella typhimurium This study also compares the advantages of the microfluidic biosensor over a nonmicrofluidic biosensor High density interdigitated electrode array was used to detect Salmonella cells inside a microfluidic chip Monoclonal anti-Salmonella antibodies were allowed to be immobilized on the surface of the electrode array for selective detection of Salmonella typhimurium An impedance analyzer was used to measure and record the response signal from the biosensor The biosensor provides qualitative and quantitative results in 3 hours without any enrichment steps The microfluidic biosensor’s lower detection limit was found to be CFU/mL compared to the CFU/mL of the nonmicrofluidic biosensor, which shows that the microfluidic biosensor has 10-fold increased sensitivity The impedance response of microfluidic biosensor was also significantly higher (2 to 29 times) compared to the nonmicrofluidic biosensor
TL;DR: Compared with genetic algorithm simulations, the comparison confirms that TS-PSO optimizes the multiobjective function of the identified problem better than what is offered by GA solution.
Abstract: A multiobjective dynamic vehicle routing problem (M-DVRP) has been identified and a time seed based solution using particle swarm optimization (TS-PSO) for M-DVRP has been proposed. M-DVRP considers five objectives, namely, geographical ranking of the request, customer ranking, service time, expected reachability time, and satisfaction level of the customers. The multiobjective function of M-DVRP has four components, namely, number of vehicles, expected reachability time, and profit and satisfaction level. Three constraints of the objective function are vehicle, capacity, and reachability. In TS-PSO, first of all, the problem is partitioned into smaller size DVRPs. Secondly, the time horizon of each smaller size DVRP is divided into time seeds and the problem is solved in each time seed using particle swarm optimization. The proposed solution has been simulated in ns-2 considering real road network of New Delhi, India, and results are compared with those obtained from genetic algorithm (GA) simulations. The comparison confirms that TS-PSO optimizes the multiobjective function of the identified problem better than what is offered by GA solution.
TL;DR: IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control, primarily considers real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing.
Abstract: We describe IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control. A device connected to IoTCloud can communicate with real time data analysis frameworks deployed in the cloud via messaging. The platform design is scalable in connecting devices as well as transferring and processing data. With IoTCloud, a user can develop real time data processing algorithms in an abstract framework without concern for the underlying details of how the data is distributed and transferred. For this platform, we primarily consider real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing. To demonstrate the effectiveness of the system, a robotic application is developed on top of the framework. The system and the robotics application characteristics are measured to show that data processing in central servers is feasible for real time sensor applications.
TL;DR: An improved strategy and practical system to detect driving fatigue based on machine vision and Adaboost algorithm and planted into portable smart device, which could be widely used for driving fatigue detection in daily life.
Abstract: Driving fatigue is one of the most important factors in traffic accidents. In this paper, we proposed an improved strategy and practical system to detect driving fatigue based on machine vision and Adaboost algorithm. Kinds of face and eye classifiers are well trained by Adaboost algorithm in advance. The proposed strategy firstly detects face efficiently by classifiers of front face and deflected face. Then, candidate region of eye is determined according to geometric distribution of facial organs. Finally, trained classifiers of open eyes and closed eyes are used to detect eyes in the candidate region quickly and accurately. The indexes which consist of PERCLOS and duration of closed-state are extracted in video frames real time. Moreover, the system is transplanted into smart device, that is, smartphone or tablet, due to its own camera and powerful calculation performance. Practical tests demonstrated that the proposed system can detect driver fatigue with real time and high accuracy. As the system has been planted into portable smart device, it could be widely used for driving fatigue detection in daily life.
TL;DR: The present technology with additional annealing improves the performance of the graphene based sensor and makes it suitable for the environmental nitrogen dioxide gas monitoring.
Abstract: We report about technology of fabrication and optimization of a gas sensor based on epitaxial graphene. Optimized graphene/metal contact configuration exhibited low contact resistance. Complementary annealing of graphene sensor after each gas exposure led to significant improvement in the sensing performance. The response of the annealed sensor to the nitrogen dioxide (NO2) was tenfold higher than that of an as-fabricated graphene sensor. NO2 concentration as low as 0.2 parts per billion (ppb) was easily detectable. Devices have high signal-to-noise ratio. The detection limit of the graphene sensor was estimated to be 0.6 ppt (parts per trillion). The present technology with additional annealing improves the performance of the graphene based sensor and makes it suitable for the environmental nitrogen dioxide gas monitoring.
TL;DR: The wireless sensor network consists of small limited energy sensors which are connected to one or more sinks and the network lifetime optimization is achieved by restructuring the network by modifying the neighbor nodes of a sink.
Abstract: The wireless sensor network consists of small limited energy sensors which are connected to one or more sinks. The maximum energy consumption takes place in communicating the data from the nodes to the sink. Multiple sink WSN has an edge over the single sink WSN where very less energy is utilized in sending the data to the sink, as the number of hops is reduced. If the energy consumed by a node is balanced between the other nodes, the lifetime of the network is considerably increased. The network lifetime optimization is achieved by restructuring the network by modifying the neighbor nodes of a sink. Only those nodes are connected to a sink which makes the total energy of the sink less than the threshold. This energy balancing through network restructuring optimizes the network lifetime. This paper depicts this fact through simulations done in MATLAB.
TL;DR: This paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure to solve the invalidation problem of Dempster-Shafer theory of evidence with high conflict in multisensor data fusion.
Abstract: To solve the invalidation problem of Dempster-Shafer theory of evidence (DS) with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs) to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs) but also the divergence degree of the hypothesis that two BBAs support. Finally, the weighting factors used to reassign conflicts on BBAs are developed and Dempster’s rule is chosen to combine the discounted sources. Simple numerical examples are employed to demonstrate the merit of the proposed method. Through analysis and comparison of the results, the new combination approach can effectively solve the problem of conflict management with better convergence performance and robustness.
TL;DR: The feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation of the respiratory function conducted in a laboratory environment is demonstrated.
Abstract: Real-time respiratory measurement with Doppler Radar has an important advantage in the monitoring of certain conditions such as sleep apnoea, sudden infant death syndrome (SIDS), and many other general clinical uses requiring fast nonwearable and non-contact measurement of the respiratory function. In this paper, we demonstrate the feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation-slow exhalation conducted in a laboratory environment. A high correlation factor was achieved between the Doppler Radar-based measurements and the conventional measurement device, a respiration strap. We also extended this work from basic signal acquisition to extracting detailed features of breathing function (I : E ratio). This facilitated additional insights into breathing activity and is likely to trigger a number of new applications in respiratory medicine.
TL;DR: A wireless passive temperature sensor designed on the basis of a resonant circuit, fabricated on multilayer high temperature cofired ceramic (HTCC) tapes, and measured with an antenna in the wireless coupling way demonstrates good high-temperature characteristics and wide temperature range.
Abstract: A wireless passive temperature sensor is designed on the basis of a resonant circuit, fabricated on multilayer high temperature cofired ceramic (HTCC) tapes, and measured with an antenna in the wireless coupling way. Alumina ceramic used as the substrate of the sensor is fabricated by lamination and sintering techniques, and the passive resonant circuit composed of a planar spiral inductor and a parallel plate capacitor is printed and formed on the substrate by screen-printing and postfiring processes. Since the permittivity of the ceramic becomes higher as temperature rises, the resonant frequency of the sensor decreases due to the increasing capacitance of the circuit. Measurements on the input impedance versus the resonant frequency of the sensor are achieved based on the principle, and discussions are made according to the exacted relative permittivity of the ceramic and quality factor () of the sensor within the temperature range from 19°C (room temperature) to 900°C. The results show that the sensor demonstrates good high-temperature characteristics and wide temperature range. The average sensitivity of the sensor with good repeatability and reliability is up to 5.22 KHz/°C. It can be applied to detect high temperature in harsh environment.
TL;DR: The characterization of a commercial variable orifice meters intended for application in respiratory monitoring finds the good static and dynamic properties and the low influence of condensation on sensor’s response make this VOM suitable for applications in respiratory function monitoring.
Abstract: Accurate measurement of gas exchanges is essential in mechanical ventilation and in respiratory monitoring. Among the large number of commercial flowmeters, only few kinds of sensors are used in these fields. Among them, variable orifice meters (VOMs) show some valuable characteristics, such as linearity, good dynamic response, and low cost.
This paper presents the characterization of a commercial VOM intended for application in respiratory monitoring. Firstly, two nominally identical VOMs were calibrated within ±10 L·min−1, to assess their metrological properties. Furthermore, experiments were performed by humidifying the air, to evaluate the influence of vapor condensation on sensor’s performances. The condensation influence was investigated during two long lasting trials (i.e., 4 hours) by delivering 4 L·min−1 and 8 L·min−1. Data show that the two VOMs’ responses are linear and their response is comparable (sensitivity difference of 1.4%, RMSE of 1.50 Pa); their discrimination threshold is <0.5 L·min−1, and the settling time is about 66 ms.
The condensation within the VOM causes a negligible change in sensor sensitivity and a very slight deterioration of precision. The good static and dynamic properties and the low influence of condensation on sensor’s response make this VOM suitable for applications in respiratory function monitoring.
TL;DR: The results showed that the open source sensor circuit performed as designed and could be constructed for <$100 in components representing a significant potential value through lateral scaling and replication in the 3D printing community.
Abstract: Arduino open-source microcontrollers are well known in sensor applications for scientific equipment and for controlling RepRap 3D printers Recently low-cost open-source gas metal arc weld (GMAW) RepRap 3D printers have been developed The entry-level welders used have minimal controls and therefore lack any real-time measurement of welder voltage or current The preliminary work on process optimization of GMAW 3D printers requires a low-cost sensor and data logger system to measure welder current and voltage This paper reports on the development of a low-cost open-source power measurement sensor system based on Arduino architecture The sensor system was designed, built, and tested with two entry-level MIG welders The full bill of materials and open source designs are provided Voltage and current were measured while making stepwise adjustments to the manual voltage setting on the welder Three conditions were tested while welding with steel and aluminum wire on steel substrates to assess the role of electrode material, shield gas, and welding velocity The results showed that the open source sensor circuit performed as designed and could be constructed for <$100 in components representing a significant potential value through lateral scaling and replication in the 3D printing community
TL;DR: This paper presents an overview on mobile sensor networks in robotics and vice versa and robotic sensor network applications and enables new applications.
Abstract: The interaction of distributed robotics and wireless sensor networks has led to the creation of mobile sensor networks. There has been an increasing interest in building mobile sensor networks and they are the favored class of WSNs in which mobility plays a key role in the execution of an application. More and more researches focus on development of mobile wireless sensor networks (MWSNs) due to its favorable advantages and applications. In WSNs robotics can play a crucial role, and integrating static nodes with mobile robots enhances the capabilities of both types of devices and enables new applications. In this paper we present an overview on mobile sensor networks in robotics and vice versa and robotic sensor network applications.
TL;DR: This review briefly describes the improvement of probe design, excitation light sources, detectors, and calibrations of in situ fluorometers, and the development of electronic technology to meet and improve in situ measurement.
Abstract: Chlorophyll fluorescence measurement is a sensitive and effective method to quantify and analyze freshwater and sea water phytoplankton in situ. Major improvements in optical design, electronic technology, and calibration protocol have increased the accuracy and reliability of the fluorometer. This review briefly describes the improvement of probe design, excitation light sources, detectors, and calibrations of in situ fluorometers. Firstly, various optical designs for increasing the efficiency of fluorescence measurement are discussed. Next, the development of electronic technology to meet and improve in situ measurement, including various light sources, detectors, and corresponding measurement protocols, is described. In addition, various calibration materials, procedures, and methods are recommended for different kinds of water. The conclusion discusses key trends and future perspectives for in situ fluorescence sensors.
TL;DR: This review paper is focused on the design of microwave sensors using symmetry properties of transmission lines loaded with symmetric resonators, and the main advantage is the robustness against changing environmental conditions.
Abstract: This review paper is focused on the design of microwave sensors using symmetry properties of transmission lines loaded with symmetric resonators. The operating principle of these sensors is presented and then several prototype devices are reported, including linear and angular displacement sensors and rotation speed sensors. The main advantage of the proposed sensors is the robustness against changing environmental conditions.
TL;DR: Subcutaneous measurements of EEG with the test device showed high quality as measured by both quantitative and more subjective qualitative methods and provides a method of ultra-long term EEG recording in situations where this is required and where a small number of EEG electrodes are sufficient.
Abstract: Purpose. We provide a comprehensive verification of a new subcutaneous EEG recording device which promises robust and unobtrusive measurements over ultra-long time periods. The approach is evaluated against a state-of-the-art surface EEG electrode technology. Materials and Methods. An electrode powered by an inductive link was subcutaneously implanted on five subjects. Surface electrodes were placed at sites corresponding to the subcutaneous electrodes, and the EEG signals were evaluated with both quantitative (power spectral density and coherence analysis) and qualitative (blinded subjective scoring by neurophysiologists) analysis. Results. The power spectral density and coherence analysis were very similar during measurements of resting EEG. The scoring by neurophysiologists showed a higher EEG quality for the implanted system for different subject states (eyes open and eyes closed). This was most likely due to higher amplitude of the subcutaneous signals. During periods with artifacts, such as chewing, blinking, and eye movement, the two systems performed equally well. Conclusions. Subcutaneous measurements of EEG with the test device showed high quality as measured by both quantitative and more subjective qualitative methods. The signal might be superior to surface EEG in some aspects and provides a method of ultra-long term EEG recording in situations where this is required and where a small number of EEG electrodes are sufficient.
TL;DR: It is concluded that the volatile composition of commercial mushrooms could benefit a finger spectrum by e-nose to identify the species of edible fungi.
Abstract: Volatile profiles of eight mushrooms were characterized by gas chromatography-mass spectrometry and electronic nose analysis Volatile compounds including 11 alcohols, 11 ketones, 15 aldehydes, 3 sulfur compounds and alkenes, 8 terpenes, 7 acid and esters, 5 heterocyclic compounds, 20 aromatic compounds, and 4 other compounds were identified The overall aroma properties of the mushrooms were analyzed by the electronic nose Results indicated that the e-nose sensors have the ability to accurately respond to different mushrooms with similar fingerprint chromatograms The relationship between the GC-MS data and e-nose responses of different mushrooms was modeled by principal component analysis and partial least squares regression This combination for the volatile analysis with chemometric methods can be applied to distinguish different mushrooms successfully Furthermore, it is concluded that the volatile composition of commercial mushrooms could benefit a finger spectrum by e-nose to identify the species of edible fungi