Search results for: autonomous sensors

Authors: Eu Tteum Ha, Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks.

Keywords: Bayesian network, IoT, learning, situation -awareness, smart home

Procedia PDF Downloads 523

Authors: M. A. Riurean, S. Heijnen, C. A. Knott, J. Makinde, D. Gotti, J. VD. Kamp

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Objectives: The current study is done in preparation for a randomized controlled study investigating the effects of an implicit motor learning protocol implemented using an extension-supporting glove. It will explore different protocols to find out which is preferred when studying motor learn-ing in the chronic stroke population that struggles with hand spasticity. Design: This exploratory study will follow 24 individuals who have a chronic stroke (> 6 months) during their usual care journey. We will record the results of two 9-Hole Peg Tests (9HPT) done during their therapy ses-sions with a physiotherapist or in their home before and after 4 weeks of them wearing an exten-sion-supporting glove used to employ the to-be-studied protocols. The participants will wear the glove 3 times/week for one hour while performing their activities of daily living and record the times they wore it in a diary. Their experience will be monitored through telecommunication once every week. Subjects: Individuals that have had a stroke at least 6 months prior to participation, hand spasticity measured on the modified Ashworth Scale of maximum 3, and finger flexion motor control measured on the Motricity Index of at least 19/33. Exclusion criteria: extreme hemi-neglect. Methods: The participants will be randomly divided into 3 groups: one group using the glove in a pre-set way of decreasing support (implicit motor learning), one group using the glove in a self-controlled way of decreasing support (autonomous motor learning), and the third using the glove with constant support (as control). Before and after the 4-week period, there will be an intake session and a post-assessment session. Analysis: We will compare the results of the two 9HPTs to check whether the protocols were effective. Furthermore, we will compare the results between the three groups to find the preferred one. A qualitative analysis will be run of the experience of participants throughout the 4-week period. Expected results: We expect that the group using the implicit learning protocol will show superior results.

Keywords: implicit learning, hand spasticity, stroke, error minimization, motor task

Procedia PDF Downloads 59

Authors: Armanul Hoque Shaon, Md Baizid Mahmud, Askander Nobi, Md. Raju Ahmed, Md. Jiabul Hoque

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In the Internet of Things (IoT), devices are linked to the internet through a wireless network, allowing them to collect and transmit data without the need for a human operator. Agriculture relies heavily on wireless sensors, which are a vital component of the Internet of Things (IoT). This kind of wireless sensor network monitors physical or environmental variables like temperatures, sound, vibration, pressure, or motion without relying on a central location or sink and collaboratively passes its data across the network to be analyzed. As the primary source of plant nutrients, the soil is critical to the agricultural industry's continued growth. We're excited about the prospect of developing an Internet of Things (IoT) solution. To arrange the network, the sink node collects groundwater levels and sends them to the Gateway, which centralizes the data and forwards it to the sensor nodes. The sink node gathers soil moisture data, transmits the mean to the Gateways, and then forwards it to the website for dissemination. The web server is in charge of storing and presenting the moisture in the soil data to the web application's users. Soil characteristics may be collected using a networked method that we developed to improve rice production. Paddy land is running out as the population of our nation grows. The success of this project will be dependent on the appropriate use of the existing land base.

Keywords: IoT based agriculture monitoring, intelligent irrigation, communicating network, rice production

Procedia PDF Downloads 154

Authors: Rob van den Dam

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Today’s Internet of Things (IoT) comprises more than a billion intelligent devices, connected via wired/wireless communications. The expected proliferation of hundreds of billions more places us at the threshold of a transformation sweeping across the communications industry. Yet, we found that the IoT architecture and solutions that currently work for billions of devices won’t necessarily scale to tomorrow’s hundreds of billions of devices because of high cost, lack of privacy, not future-proof, lack of functional value and broken business models. As the IoT scales exponentially, decentralized networks have the potential to reduce infrastructure and maintenance costs to manufacturers. Decentralization also promises increased robustness by removing single points of failure that could exist in traditional centralized networks. By shifting the power in the network from the center to the edges, devices gain greater autonomy and can become points of transactions and economic value creation for owners and users. To validate the underlying technology vision, IBM jointly developed with Samsung Electronics the autonomous decentralized peer-to- peer proof-of-concept (PoC). The primary objective of this PoC was to establish a foundation on which to demonstrate several capabilities that are fundamental to building a decentralized IoT. Though many commercial systems in the future will exist as hybrid centralized-decentralized models, the PoC demonstrated a fully distributed proof. The PoC (a) validated the future vision for decentralized systems to extensively augment today’s centralized solutions, (b) demonstrated foundational IoT tasks without the use of centralized control, (c) proved that empowered devices can engage autonomously in marketplace transactions. The PoC opens the door for the communications and electronics industry to further explore the challenges and opportunities of potential hybrid models that can address the complexity and variety of requirements posed by the internet that continues to scale. Contents: (a) The new approach for an IoT that will be secure and scalable, (b) The three foundational technologies that are key for the future IoT, (c) The related business models and user experiences, (d) How such an IoT will create an 'Economy of Things', (e) The role of users, devices, and industries in the IoT future, (f) The winners in the IoT economy.

Keywords: IoT, internet, wired, wireless

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Authors: Debadi Chakraborty, John E. Sader

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Nanomechanical devices have emerged as a versatile platform for a host of applications due to their extreme sensitivity to environmental conditions. For example, mass measurements with sensitivity at the atomic level have recently been demonstrated. Ultrafast laser spectroscopy coherently excite the vibrational modes of metal nanoparticles and permits precise measurement of the vibration characteristics as a function of nanoparticle shape, size and surrounding environment. This study reports that the vibration of metal nanoparticles in simple liquids, like water and glycerol are not described by conventional fluid mechanics, i.e., Navier Stokes equations. The intrinsic molecular relaxation processes in the surrounding liquid are found to have a profound effect on the fluid-structure interaction of mechanical devices at nanometre scales. Theoretical models have been developed based on the non-Newtonian viscoelastic fluid-structure interaction theory to investigate the vibration of nanoparticles immersed in simple fluids. The utility of this theoretical framework is demonstrated by comparison to measurements on single nanowires and ensembles of metal rods. This study provides a rigorous foundation for the use of metal nanoparticles as ultrasensitive mechanical sensors in fluid and opens a new paradigm for understanding extremely high frequency fluid mechanics, nanoscale sensing technologies, and biophysical processes.

Keywords: fluid-structure interaction, nanoparticle vibration, ultrafast laser spectroscopy, viscoelastic damping

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Authors: J. Bahrawi, M. Elhag

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Estimation of evapotranspiration is always a major component in water resources management. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are thus used to overcome difficulties in obtaining daily evapotranspiration measurements on regional scale. The Surface Energy Balance System (SEBS) model was adopted to estimate daily evapotranspiration and relative evaporation along with other land surface energy fluxes. The model requires agro-climatic data that improve the model outputs. Advance Along Track Scanning Radiometer (AATSR) and Medium Spectral Resolution Imaging Spectrometer (MERIS) imageries were used to estimate the daily evapotranspiration and relative evaporation over the entire Nile Delta region in Egypt supported by meteorological data collected from six different weather stations located within the study area. Daily evapotranspiration maps derived from SEBS model show a strong agreement with actual ground-truth data taken from 92 points uniformly distributed all over the study area. Moreover, daily evapotranspiration and relative evaporation are strongly correlated. The reliable estimation of daily evapotranspiration supports the decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes.

Keywords: daily evapotranspiration, relative evaporation, SEBS, AATSR, MERIS, Nile Delta

Procedia PDF Downloads 259

Authors: Lingling Shui, Shuting Xie

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As alternatives to electronic devices, optically active structures from responsive nanomaterials offer great opportunity buildup smart functional sensors. Hereby, we report on droplet microfluidics enabled construction and application of photonic microcapsules (PMCs) for colorimetric temperature microsensors, enabling miniaturization for injectable local micro-area sensing and integration for large-area sensing. Monodispersed PMCs are produced by in-situ photopolymerization of hydrogel shells of cholesteric liquid crystal (CLC)-in-water-in-oil double emulsion droplets prepared using microfluidic devices, with controllable physical structures and chemical compositions. Constructed PMCs exhibit thermal responsive structural color according to the selective Bragg reflection of CLC’s periodical helical structures within the microdroplet’s spherical confinement. Constructed PMCs with tunable size and composition have been successfully applied for monitoring the living cell extracellular temperature via co-incubation with cell suspension, and for detecting human body temperature via a flexible device from assembled PMCs. These PMCs could be flexibly applied in either micro-environment or large-area surface, enabling wide applications for precision temperature monitoring biological activities (e.g. cells or organs), optoelectronic devices working conditions (e.g. temperature indicators under extreme conditions), and etc.

Keywords: droplet, microfluidics, assembly, soft materials, microsensor

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Authors: Bulcha Belay Etana

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Research shows that several challenges are to be resolved for textile sensors and wearable smart textiles systems to make it accurate and reproducible minimizing variability issues when tested. To achieve this, we developed stimulating embroidery electrode with three different filling textiles such as 3Dknit, microfiber, and nonwoven fabric, and tested with FTT for high recoverability on compression. Hence The impedance characteristics of wetted electrodes were caried out after 1hr of wetting under normal environmental conditions. The wetted 3D knit (W-3D knit), Wetted nonwoven (W-nonwoven), and wetted microfiber (W-microfiber) developed using Satin stitch performed better than a dry standard stitch or dry Satin stitch electrodes. Its performance was almost the same as that of the gel electrode (Ag/AgCl) as shown by the impedance result in figure 2 .The impedance characteristics of Dry and wetted 3D knit based Embroidered electrodes are better than that of the microfiber, and nonwoven filling textile. This is due to the fact that 3D knit fabric has high recoverability on compression to retain electrolyte gel than microfiber, and nonwoven. However,The non-woven fabric held the electrolyte for longer time without releasing it to the skin when needed, thus making its impedance characteristics poor as observed from the results. Whereas the dry Satin stitch performs better than the standard stitch based developed electrode. The inter electrode distance of all types of the electrode was 25mm, with the area of the electrode being 20mm by 20mm. Detail evaluation and further analysis is in progress for EMG monitoring application

Keywords: impedance, moisture retention, 3D knit fabric, microfiber, nonwoven

Procedia PDF Downloads 140

Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Walid Abdallah, Noureddine Boudriga

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Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

Procedia PDF Downloads 659

Authors: Kamélia Bouderbala, Hichem Nouira, Etienne Videcoq, Manuel Girault, Daniel Petit

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Thermal drifts caused by the power dissipated by the mechanical guiding systems constitute the main limit to enhance the accuracy of an ultra-high precision cylindricity measuring machine. For this reason, a high precision compact prototype has been designed to simulate the behaviour of the instrument. It ensures in situ calibration of four capacitive displacement probes by comparison with four laser interferometers. The set-up includes three heating wires for simulating the powers dissipated by the mechanical guiding systems, four additional heating wires located between each laser interferometer head and its respective holder, 19 Platinum resistance thermometers (Pt100) to observe the temperature evolution inside the set-up and four Pt100 sensors to monitor the ambient temperature. Both a Reduced Model (RM), based on the Modal Identification Method (MIM) was developed and optimized by comparison with the experimental results. Thereafter, time dependent tests were performed under several conditions to measure the temperature variation at 19 fixed positions in the system and compared to the calculated RM results. The RM results show good agreement with experiment and reproduce as well the temperature variations, revealing the importance of the RM proposed for the evaluation of the thermal behaviour of the system.

Keywords: modal identification method (MIM), thermal behavior and drift, dimensional metrology, measurement

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Jisun Lee, Jay Hyoun Kwon

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As an alternative way to compensate the INS (inertial navigation system) error in non-GNSS (Global Navigation Satellite System) environment, geophysical database referenced navigation is being studied. In this study, both gravity gradient and terrain data were combined to complement the weakness of sole geophysical data as well as to improve the stability of the positioning. The main process to compensate the INS error using geophysical database was constructed on the basis of the EKF (Extended Kalman Filter). In detail, two type of combination method, centralized and decentralized filter, were applied to check the pros and cons of its algorithm and to find more robust results. The performance of each navigation algorithm was evaluated based on the simulation by supposing that the aircraft flies with precise geophysical DB and sensors above nine different trajectories. Especially, the results were compared to the ones from sole geophysical database referenced navigation to check the improvement due to a combination of the heterogeneous geophysical database. It was found that the overall navigation performance was improved, but not all trajectories generated better navigation result by the combination of gravity gradient with terrain data. Also, it was found that the centralized filter generally showed more stable results. It is because that the way to allocate the weight for the decentralized filter could not be optimized due to the local inconsistency of geophysical data. In the future, switching of geophysical data or combining different navigation algorithm are necessary to obtain more robust navigation results.

Keywords: Extended Kalman Filter, geophysical database referenced navigation, gravity gradient, terrain

Procedia PDF Downloads 349

Authors: Shivani Verma, Amit Agrawal, Pankaj Kumar Meena, Ashish B. Deoghare

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Over the decade robotics has been a major area of interest among the researchers and scientists in reducing human efforts. The need for robots to replace human work in different dangerous fields such as underground mining, nuclear power station and war against terrorist attack has gained huge attention. Most of the robot design is based on human structure popularly known as humanoid robots. However, the problems encountered in humanoid robots includes low speed of movement, misbalancing in structure, poor load carrying capacity, etc. The simplification and adaptation of the fundamental design principles seen in animals have led to the creation of bio-inspired robots. But the major challenges observed in naturally inspired robot include complexity in structure, several degrees of freedom and energy storage problem. The present work focuses on design and fabrication of a bionic quadruped walking robot which is based on different joint of quadruped mammals like a dog, cheetah, etc. The design focuses on the structure of the robot body which consists of four legs having three degrees of freedom per leg and the electronics system involved in it. The robot is built using readily available plastics and metals. The proposed robot is simple in construction and is able to move through uneven terrain, detect and locate obstacles and take images while carrying additional loads which may include hardware and sensors. The robot will find possible application in the artificial intelligence sector.

Keywords: artificial intelligence, bionic, quadruped robot, degree of freedom

Procedia PDF Downloads 215

Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

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Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

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Authors: Jing Hu, Dong Yang

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With the increasingly difficult development and operating environment of exploration, there are many new challenges and difficulties in developing and exploiting oil and gas resources. These include the ability to dynamically monitor wells and provide data and assurance for the completion and production of high-cost and complex wells. A key technology in providing these assurances and maximizing oilfield profitability is real-time permanent reservoir monitoring. The emergence of optical fiber sensing systems has gradually begun to replace traditional electronic systems. Traditional temperature sensors can only achieve single-point temperature monitoring, but fiber optic sensing systems based on the Bragg grating principle have a high level of reliability, accuracy, stability, and resolution, enabling cost-effective monitoring, which can be done in real-time, anytime, and without well intervention. Continuous data acquisition is performed along the entire wellbore. The integrated package with the downhole pressure gauge, packer, and surface system can also realize real-time dynamic monitoring of the pressure in some sections of the downhole, avoiding oil well intervention and eliminating the production delay and operational risks of conventional surveys. Real-time information obtained through permanent optical fibers can also provide critical reservoir monitoring data for production and recovery optimization.

Keywords: PDHM, optical fiber, coiled tubing, photoelectric composite cable, digital-oilfield

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Authors: Ahmad Umar, Sheikh Akbar, Ahmed A. Ibrahim, Mohsen A. Alhamami

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The development of advanced materials for energy storage and gas sensing applications has gained significant attention in recent years. In this study, we synthesized and characterized PANI@MnO2@rGO ternary nanocomposites (NCs) to explore their potential in supercapacitors and gas sensing devices. The ternary NCs were synthesized through a multi-step process involving the hydrothermal synthesis of MnO2 nanoparticles, preparation of PANI@rGO composites and the assembly to the ternary PANI@MnO2@rGO ternary NCs. The structural, morphological, and compositional characteristics of the materials were thoroughly analyzed using techniques such as XRD, FESEM, TEM, FTIR, and Raman spectroscopy. In the realm of gas sensing, the ternary NCs exhibited excellent performance as NH3 gas sensors. The optimized operating temperature of 100 °C yielded a peak response of 15.56 towards 50 ppm NH3. The nanocomposites demonstrated fast response and recovery times of 6 s and 10 s, respectively, and displayed remarkable selectivity for NH3 gas over other tested gases. For supercapacitor applications, the electrochemical performance of the ternary NCs was evaluated using cyclic voltammetry and galvanostatic charge-discharge techniques. The composites exhibited pseudocapacitive behavior, with the capacitance reaching up to 185 F/g at 1 A/g and excellent capacitance retention of approximately 88.54% over 4000 charge-discharge cycles. The unique combination of rGO, PANI, and MnO2 nanoparticles in these ternary NCs offer synergistic advantages, showcasing their potential to address challenges in energy storage and gas sensing technologies.

Keywords: paniI@mnO2@rGO ternary NCs, synergistic effects, supercapacitors, gas sensing, energy storage

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Authors: Jaiprakash Dargad

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CdMnSe dilute semiconductor or semimagnetic semiconductors have become the focus of intense research due to their interesting combination of magnetic and semiconducting properties, and are employed in a variety of devices including solar cells, gas sensors etc. A series of thin films of this material, Cd1−xMnxSe (0 ≤ x ≤ 0.5), were therefore synthesized onto precleaned amorphous glass substrates using a solution growth technique. The sources of cadmium (Cd2+) and manganese (Mn2+) were aqueous solutions of cadmium sulphate and manganese sulphate, and selenium (Se2−) was extracted from a reflux of sodium selenosulphite. The different deposition parameters such as temperature, time of deposition, speed of mechanical churning, pH of the reaction mixture etc were optimized to yield good quality deposits. The as-grown samples were thin, relatively uniform, smooth and tightly adherent to the substrate support. The colour of the deposits changed from deep red-orange to yellowish-orange as the composition parameter, x, was varied from 0 to 0.5. The terminal layer thickness decreased with increasing value of, x. The optical energy gap decreased from 1.84 eV to 1.34 eV for the change of x from 0 to 0.5. The coefficient of optical absorption is of the order of 10-4 - 10-5 cm−1 and the type of transition (m = 0.5) is of the band-to-band direct type. The dc electrical conductivities were measured at room temperature and in the temperature range 300 K - 500 K. It was observed that the room temperature electrical conductivity increased with the composition parameter x up to 0.1, gradually decreasing thereafter. The thermo power measurements showed n-type conduction in these films.

Keywords: dilute semiconductor, reflux, CBD, thin film

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Wanwisa Deenin, Abdulhadee Yakoh, Chahya Kreangkaiwal, Orawon Chailapakul, Kanitha Patarakul, Sudkate Chaiyo

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LipL32 is an outer membrane protein present only on pathogenic Leptospira species, which are the causative agent of leptospirosis. Leptospirosis symptoms are often misdiagnosed with other febrile illnesses as the clinical manifestations are non-specific. Therefore, an accurate diagnostic tool for leptospirosis is indeed critical for proper and prompt treatment. Typical diagnosis via serological assays is generally performed to assess the antibodies produced against Leptospira. However, their delayed antibody response and complicated procedure are undoubtedly limited the practical utilization especially in primary care setting. Here, we demonstrate for the first time an early-stage detection of LipL32 by an integrated lateral-flow immunoassay with electrochemical readout (eLFIA). A ferrocene trace tag was monitored via differential pulse voltammetry operated on a smartphone-based device, thus allowing for on-field testing. Superior performance in terms of the lowest detectable limit of detection (LOD) of 8.53 pg/mL and broad linear dynamic range (5 orders of magnitude) among other sensors available thus far was established. Additionally, the developed test strip provided a straightforward yet sensitive approach for diagnosis of leptospirosis using the collected human sera from patients, in which the results were comparable to the real-time polymerase chain reaction technique.

Keywords: leptospirosis, electrochemical detection, lateral flow immunosensor, point-of-care testing, early-stage detection

Procedia PDF Downloads 93

Authors: Yasmeen Coaxum

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The year 2020 became synonymous with the words “Emergency Remote Teaching,” which was imposed upon educators during the COVID-19 pandemic. Consequently, teachers were compelled to find new and engaging ways to educate their students outside of the face-to-face classroom setting. Now online instruction has become more of the norm rather than a way to manage educational expectations during a crisis. Therefore, implementing a strategic way to create online environments for students to thrive, create, and fully engage in their learning process is essential. The Synchronous Online Flipped Learning Approach or SOFLA® is a distance learning model that most closely replicates actual classroom teaching. SOFLA® includes structured, interactive, multimodal activities in an eight-step learning cycle with both asynchronous and synchronous components that foster autonomous and interactive learning among today’s online learners. The results of a pilot study in an Intensive English Program at a university, using SOFLA® methodology to facilitate podcasting in an online learning environment will be shared. Previous findings on student-produced podcasting projects have shown that students felt they improved their pronunciation, vocabulary, and speaking skills. However, few if any studies have been conducted on using a structured online flipped learning approach to facilitate such projects. Therefore, the purpose of this study is to assess the effect of using the SOFLA® framework to enhance optimum engagement in the online environment while using podcasts as the primary tool of instruction. Through data from interviews, questionnaires, and the results of formative and summative assessments, this study also investigates the affective and academic impact this flipped learning method combined with podcasting has on the students in terms of speaking confidence and vocabulary retention, and production. The steps of SOFLA will be illustrated, a video demonstration of the Anchor podcasting app will be shown, and final student projects and questionnaire responses will be shared. The specific context is a 14-week advanced level conversation and listening class. Participants vary in age but are all adult language learners representing a diverse array of countries.

Keywords: mall online flipped learning, podcasting, productive vocabulary

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Authors: Shalini Menon, K. Girish Kumar

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Norepinephrine (NE) is one of the naturally occurring catecholamines which act both as a neurotransmitter and a hormone. Catecholamine levels are used for the diagnosis and regulation of phaeochromocytoma, a neuroendocrine tumor of the adrenal medulla. The development of simple, rapid and cost-effective sensors for NE still remains a great challenge. Herein, a dual-channel sensor has been developed for the determination of NE. A mixture of AgNO₃, NaOH, NH₃.H₂O and cetrimonium bromide in appropriate concentrations was taken as the working solution. To the thoroughly vortexed mixture, an appropriate volume of NE solution was added. After a particular time, the fluorescence and absorbance were measured. Fluorescence measurements were made by exciting at a wavelength of 400 nm. A dual-channel optical sensor has been developed for the colorimetric as well as the fluorimetric determination of NE. Metal enhanced fluorescence property of nanoparticles forms the basis of the fluorimetric detection of this assay, whereas the appearance of brown color in the presence of NE leads to colorimetric detection. Wide linear ranges and sub-micromolar detection limits were obtained using both the techniques. Moreover, the colorimetric approach was applied for the determination of NE in synthetic blood serum and the results obtained were compared with the classic high-performance liquid chromatography (HPLC) method. Recoveries between 97% and 104% were obtained using the proposed method. Based on five replicate measurements, relative standard deviation (RSD) for NE determination in the examined synthetic blood serum was found to be 2.3%. This indicates the reliability of the proposed sensor for real sample analysis.

Keywords: norepinephrine, colorimetry, fluorescence, silver nanoparticles

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Andrée-Anne Parent, Alain-Steve Comtois

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The Antarctica environment can be a great challenge for human exploration. Explorers need to be focused on the task and require the physical abilities to succeed and survive in complete autonomy in this hostile environment. The aim of this study was to observe cognitive performance and physiological stress with a biomarker (cortisol) and hand grip strength during an expedition in Antarctica. A total of 6 explorers were in complete autonomous exploration on the Forbidden Plateau in Antarctica to reach unknown summits during a 30 day period. The Stroop Test, a simple reaction time, and mood scale (PANAS) tests were performed every week during the expedition. Saliva samples were taken before sailing to Antarctica, the first day on the continent, after the mission on the continent and on the boat return trip. Furthermore, hair samples were taken before and after the expedition. The results were analyzed with SPSS using ANOVA repeated measures. The Stroop and mood scale results are presented in the following order: 1) before sailing to Antarctica, 2) the first day on the continent, 3) after the mission on the continent and 4) on the boat return trip. No significant difference was observed with the Stroop (759±166 ms, 850±114 ms, 772±179 ms and 833±105 ms, respectively) and the PANAS (39.5 ±5.7, 40.5±5, 41.8±6.9, 37.3±5.8 positive emotions, and 17.5±2.3, 18.2±5, 18.3±8.6, 15.8±5.4 negative emotions, respectively) (p>0.05). However, there appears to be an improvement at the end of the second week. Furthermore, the simple reaction time was significantly lower at the end of the second week, a moment where important decisions were taken about the mission, vs the week before (416±39 ms vs 459.8±39 ms respectively; p=0.030). Furthermore, the saliva cortisol was not significantly different (p>0.05) possibly due to important variations and seemed to reach a peak on the first day on the continent. However, the cortisol from the hair pre and post expedition increased significantly (2.4±0.5 pg/mg pre-expedition and 16.7±9.2 pg/mg post-expedition, p=0.013) showing important stress during the expedition. Moreover, no significant difference was observed on the grip strength except between after the mission on the continent and after the boat return trip (91.5±21 kg vs 85±19 kg, p=0.20). In conclusion, the cognitive performance does not seem to be affected during the expedition. Furthermore, it seems to increase for specific important events where the crew seemed to focus on the present task. The physiological stress does not seem to change significantly at specific moments, however, a global pre-post mission measure can be important and for this reason, for long-term missions, a pre-expedition baseline measure is important for crewmembers.

Keywords: Antarctica, cognitive performance, expedition, physiological adaptation, reaction time

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Hung-Yuan Chang, Wen-Lung Chiang, Kuo-Liang Wu, Chen-Tsung Lin

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National Space Organization (NSPO) has completed in 2014 the development of a space-borne GPS receiver, including design, manufacture, comprehensive functional test, environmental qualification test and so on. The main performance of this receiver include 8-meter positioning accuracy, 0.05 m/sec speed-accuracy, the longest 90 seconds of cold start time, and up to 15g high dynamic scenario. The receiver will be integrated in the autonomous FORMOSAT-7 NSPO-Built satellite scheduled to be launched in 2019 to execute pre-defined scientific missions. The flight model of this receiver manufactured in early 2015 will pass comprehensive functional tests and environmental acceptance tests, etc., which are expected to be completed by the end of 2015. The space-borne GPS receiver is a pure software design in which all GPS baseband signal processing are executed by a digital signal processor (DSP), currently only 50% of its throughput being used. In response to the booming global navigation satellite systems, NSPO will gradually expand this receiver to become a multi-mode, multi-band, high-precision navigation receiver, and even a science payload, such as the reflectometry receiver of a global navigation satellite system. The fundamental purpose of this extension study is to port some software algorithms such as signal acquisition and correlation, reused code and large amount of computation load to the FPGA whose processor is responsible for operational control, navigation solution, and orbit propagation and so on. Due to the development and evolution of the FPGA is pretty fast, the new system architecture upgraded via an FPGA should be able to achieve the goal of being a multi-mode, multi-band high-precision navigation receiver, or scientific receiver. Finally, the results of tests show that the new system architecture not only retains the original overall performance, but also sets aside more resources available for future expansion possibility. This paper will explain the detailed DSP/FPGA architecture, development, test results, and the goals of next development stage of this receiver.

Keywords: space-borne, GPS receiver, DSP, FPGA, multi-mode multi-band

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Authors: Haifa Saleh, Kamiran Abduka, Robin Kalfat, Riadh Al-Mahaidi

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The effectiveness of using pre-stressing steel bars for shear strengthening of high strength reinforced concrete (RC) slabs was assessed. Two large-scale RC slabs were tested, one without shear reinforcement and the second strengthened against punching shear failure using pre-stressing steel bars. The two slabs had the same dimensions, flexural reinforcement ratio, loading and support arrangements. The experimental program including the method of strengthening, set up and instrumentation are described in this paper. The experimental results are analyzed and discussed in terms of the structural behavior of the RC slabs, the performance of pre-stressing steel bolts and failure modes. The results confirmed that the shear strengthening technique increased the shear capacity, ductility and yield capacity of the slab by up to 15%, 44%, and 22%, respectively compared to the unstrengthened slab. The strengthening technique also successfully contributed to changing the failure mode from a brittle punching shear mode to ductile flexural failure mode. Vic3D digital image correlation system (photogrammetry) was also used in this research. This technique holds several advantages over traditional contact instrumentations including that it is inexpensive, it produces results that are simple to analyze and it is remote visualization technique. The displacement profile along the span of the slab and rotation has been found and compared with the results obtained from traditional sensors. The performance of the photogrammetry technique was very good and the results of both measurements were in very close agreement.

Keywords: flat slab, photogrammetry, punching shear, strengthening

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Authors: D. Sun, T. F. Lu, A. Zander, M. Trinkle

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This paper investigates the potential use of airborne ultrasonic phased arrays for imaging in outdoor environments as a means of overcoming the limitations experienced by kinect sensors, which may fail to work in the outdoor environments due to the oversaturation of the infrared photo diodes. Ultrasonic phased arrays have been well studied for static media, yet there appears to be no comparable examination in the literature of the impact of a flowing medium on the focusing behaviour of near field focused ultrasonic arrays. This paper presents a method for predicting the sound pressure fields produced by a single ultrasound element or an ultrasonic phased array influenced by airflows. The approach can be used to determine the actual focal point location of an array exposed in a known flow field. From the presented simulation results based upon this model, it can be concluded that uniform flows in the direction orthogonal to the acoustic propagation have a noticeable influence on the sound pressure field, which is reflected in the twisting of the steering angle of the array. Uniform flows in the same direction as the acoustic propagation have negligible influence on the array. For an array impacted by a turbulent flow, determining the location of the focused sound field becomes difficult due to the irregularity and continuously changing direction and the speed of the turbulent flow. In some circumstances, ultrasonic phased arrays impacted by turbulent flows may not be capable of producing a focused sound field.

Keywords: airborne, airflow, focused sound field, ultrasonic phased array

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Authors: Istvan Csarnovics, Attila Bonyar, Miklos Veres, Laszlo Himics, Attila Csik, Judit Kaman, Julia Burunkova, Geza Szanto, Laszlo Balazs, Sandor Kokenyesi

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In this work, the performance of gold nanoparticles were investigated for stimulation of photosensitive materials for photonic applications. It was widely used for surface plasmon resonance experiments, not in the last place because of the manifestation of optical resonances in the visible spectral region. The localized surface plasmon resonance is rather easily observed in nanometer-sized metallic structures and widely used for measurements, sensing, in semiconductor devices and even in optical data storage. Firstly, gold nanoparticles on silica glass substrate satisfy the conditions for surface plasmon resonance in the green-red spectral range, where the chalcogenide glasses have the highest sensitivity. The gold nanostructures influence and enhance the optical, structural and volume changes and promote the exciton generation in gold nanoparticles/chalcogenide layer structure. The experimental results support the importance of localized electric fields in the photo-induced transformation of chalcogenide glasses as well as suggest new approaches to improve the performance of these optical recording media. Results may be utilized for direct, micrometre- or submicron size geometrical and optical pattern formation and used also for further development of the explanations of these effects in chalcogenide glasses. Besides of that, gold nanoparticles could be added to the organic light-sensitive material. The acrylate-based materials are frequently used for optical, holographic recording of optoelectronic elements due to photo-stimulated structural transformations. The holographic recording process and photo-polymerization effect could be enhanced by the localized plasmon field of the created gold nanostructures. Finally, gold nanoparticles widely used for electrochemical and optical sensor applications. Although these NPs can be synthesized in several ways, perhaps one of the simplest methods is the thermal annealing of pre-deposited thin films on glass or silicon surfaces. With this method, the parameters of the annealing process (time, temperature) and the pre-deposited thin film thickness influence and define the resulting size and distribution of the NPs on the surface. Localized surface plasmon resonance (LSPR) is a very sensitive optical phenomenon and can be utilized for a large variety of sensing purposes (chemical sensors, gas sensors, biosensors, etc.). Surface-enhanced Raman spectroscopy (SERS) is an analytical method which can significantly increase the yield of Raman scattering of target molecules adsorbed on the surface of metallic nanoparticles. The sensitivity of LSPR and SERS based devices is strongly depending on the used material and also on the size and geometry of the metallic nanoparticles. By controlling these parameters the plasmon absorption band can be tuned and the sensitivity can be optimized. The technological parameters of the generated gold nanoparticles were investigated and influence on the SERS and on the LSPR sensitivity was established. The LSPR sensitivity were simulated for gold nanocubes and nanospheres with MNPBEM Matlab toolbox. It was found that the enhancement factor (which characterize the increase in the peak shift for multi-particle arrangements compared to single-particle models) depends on the size of the nanoparticles and on the distance between the particles. This work was supported by GINOP- 2.3.2-15-2016-00041 project, which is co-financed by the European Union and European Social Fund. Istvan Csarnovics is grateful for the support through the New National Excellence Program of the Ministry of Human Capacities, supported by the ÚNKP-17-4 Attila Bonyár and Miklós Veres are grateful for the support of the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

Keywords: light sensitive nanocomposites, metallic nanoparticles, photonic application, plasmonic nanostructures

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Authors: Trevor R. Higgins

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The aim of this study was to compare muscle activation of the latissimus dorsi between pin-loaded machine (Lat Pull Down), resistance band (Lat Pull Down) and body-weight (Chin Up) exercises. A convenient sample of male college students with >2 years resistance training experience volunteered for the study. A paired t-test with repeated measures designs was carried out on results from EMG analysis. EMG analysis was conducted with Trigno wireless sensors (Delsys) placed laterally on the latissimus dorsi (left and right) of each participant. By conventional criteria the two-tailed P value suggested that differences between pin-loaded and body-weight was not significantly different (p = 0.93) and differences between pin-loaded and resistance band was not significantly different (p = 0.17) in muscle activity. In relation to conventional criteria the two-tailed P value suggested differences between body-weight and resistance band was not quite significantly different (p = 0.06) in muscle activity. However, effect size trends indicated that both body-weight and pin-loaded exercises where more effective in stimulating muscle electrical activity than a resistance band with male college athletes with >2 years resistance training experience. Although, resistance bands have increased in popularity in health and fitness centres, that for well-trained participants, they may not be effective in stimulating muscles of the latissimus dorsi. Therefore, when considering equipment and exercise selection for experienced resistance training participants pin-loaded machines and body-weight should be prescribed.

Keywords: pin-loaded, resistance bands, body weight, EMG analysis

Procedia PDF Downloads 266