Search results for: sensor array

Authors: Aravind Ravichandran, Marc Ramuz, Sylvain Blayac

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With the rapid development of wearable electronics and sensor networks, batteries cannot meet the sustainable energy requirement due to their limited lifetime, size and degradation. Ambient energies such as wind have been considered as an attractive energy source due to its copious, ubiquity, and feasibility in nature. With miniaturization leading to high-power and robustness, triboelectric nanogenerator (TENG) have been conceived as a promising technology by harvesting mechanical energy for powering small electronics. TENG integration in large-scale applications is still unexplored considering its attractive properties. In this work, a state of the art design TENG based on wind venturi system is demonstrated for use in any complex environment. When wind introduces into the air gap of the homemade TENG venturi system, a thin flexible polymer repeatedly contacts with and separates from electrodes. This device structure makes the TENG suitable for large scale harvesting without massive volume. Multiple stacking not only amplifies the output power but also enables multi-directional wind utilization. The system converts ambient mechanical energy to electricity with 400V peak voltage by charging of a 1000mF super capacitor super rapidly. Its future implementation in an array of applications aids in environment friendly clean energy production in large scale medium and the proposed design performs with an exhaustive material testing. The relation between the interfacial micro-and nano structures and the electrical performance enhancement is comparatively studied. Nanostructures are more beneficial for the effective contact area, but they are not suitable for the anti-adhesion property due to the smaller restoring force. Considering these issues, the nano-patterning is proposed for further enhancement of the effective contact area. By considering these merits of simple fabrication, outstanding performance, robust characteristic and low-cost technology, we believe that TENG can open up great opportunities not only for powering small electronics, but can contribute to large-scale energy harvesting through engineering design being complementary to solar energy in remote areas.

Keywords: triboelectric nanogenerator, wind energy, vortex design, large scale energy

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Authors: Raana Babadi Fathipour

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Tea, second only to water in global consumption rates, holds a significant place as the beverage of choice for many around the world. The process of fermenting tea leaves plays a crucial role in determining its ultimate quality, traditionally assessed through meticulous observation by tea tasters and laboratory analysis. However, advancements in technology have paved the way for innovative electronic sensing platforms like the electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye). These cutting-edge tools, coupled with sophisticated data processing algorithms, not only expedite the assessment of tea's sensory qualities based on consumer preferences but also establish new benchmarks for this esteemed bioactive product to meet burgeoning market demands worldwide. By harnessing intricate data sets derived from electronic signals and deploying multivariate statistical techniques, these technological marvels can enhance accuracy in predicting and distinguishing tea quality with unparalleled precision. In this contemporary exploration, a comprehensive overview is provided of the most recent breakthroughs and viable solutions aimed at addressing forthcoming challenges in the realm of tea analysis. Utilizing bio-mimicking Electronic Sensory Perception systems (ESPs), researchers have developed innovative technologies that enable precise and instantaneous evaluation of the sensory-chemical attributes inherent in tea and its derivatives. These sophisticated sensing mechanisms are adept at deciphering key elements such as aroma, taste, and color profiles, transitioning valuable data into intricate mathematical algorithms for classification purposes. Through their adept capabilities, these cutting-edge devices exhibit remarkable proficiency in discerning various teas with respect to their distinct pricing structures, geographic origins, harvest epochs, fermentation processes, storage durations, quality classifications, and potential adulteration levels. While voltammetric and fluorescent sensor arrays have emerged as promising tools for constructing electronic tongue systems proficient in scrutinizing tea compositions, potentiometric electrodes continue to serve as reliable instruments for meticulously monitoring taste dynamics within different tea varieties. By implementing a feature-level fusion strategy within predictive models, marked enhancements can be achieved regarding efficiency and accuracy levels. Moreover, by establishing intrinsic linkages through pattern recognition methodologies between sensory traits and biochemical makeup found within tea samples, further strides are made toward enhancing our understanding of this venerable beverage's complex nature.

Keywords: classifier system, tea, polyphenol, sensor, taste sensor

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Authors: Natalia V. Beloglazova, Pieterjan Lenain, Martin Hedstrom, Dietmar Knopp, Sarah De Saeger

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In recent years polycyclic aromatic hydrocarbons (PAHs), which represent a hazard to humans and entire ecosystem, have been receiving an increased interest due to their mutagenic, carcinogenic and endocrine disrupting properties. They are formed in all incomplete combustion processes of organic matter and, as a consequence, ubiquitous in the environment. Benzo(a)pyrene (BaP) is on the priority list published by the Environmental Agency (US EPA) as the first PAH to be identified as a carcinogen and has often been used as a marker for PAHs contamination in general. It can be found in different types of water samples, therefore, the European Commission set up a limit value of 10 ng L–1 (10 ppt) for BAP in water intended for human consumption. Generally, different chromatographic techniques are used for PAHs determination, but these assays require pre-concentration of analyte, create large amounts of solvent waste, and are relatively time consuming and difficult to perform on-site. An alternative robust, stand-alone, and preferably cheap solution is needed. For example, a sensing unit which can be submerged in a river to monitor and continuously sample BaP. An affinity sensor based on capacitive transduction was developed. Natural antibodies or their synthetic analogues can be used as ligands. Ideally the sensor should operate independently over a longer period of time, e.g. several weeks or months, therefore the use of molecularly imprinted polymers (MIPs) was discussed. MIPs are synthetic antibodies which are selective for a chosen target molecule. Their robustness allows application in environments for which biological recognition elements are unsuitable or denature. They can be reused multiple times, which is essential to meet the stand-alone requirement. BaP is a highly lipophilic compound and does not contain any functional groups in its structure, thus excluding non-covalent imprinting methods based on ionic interactions. Instead, the MIPs syntheses were based on non-covalent hydrophobic and π-π interactions. Different polymerization strategies were compared and the best results were demonstrated by the MIPs produced using electropolymerization. 4-vinylpyridin (VP) and divinylbenzene (DVB) were used as monomer and cross-linker in the polymerization reaction. The selectivity and recovery of the MIP were compared to a non-imprinted polymer (NIP). Electrodes were functionalized with natural receptor (monoclonal anti-BaP antibody) and with MIPs selective towards BaP. Different sets of electrodes were evaluated and their properties such as sensitivity, selectivity and linear range were determined and compared. It was found that both receptor can reach the cut-off level comparable to the established ML, and despite the fact that the antibody showed the better cross-reactivity and affinity, MIPs were more convenient receptor due to their ability to regenerate and stability in river till 7 days.

Keywords: antibody, benzo(a)pyrene, capacitive sensor, MIPs, river water

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Authors: H. C. Woo, F. Bouanis, C. S. Cojocaur

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Graphene, which consists of a single layer of carbon atoms in a honeycomb lattice, is an interesting potential optoelectronic material because of graphene’s high carrier mobility, zero bandgap, and electron–hole symmetry. Graphene can absorb light and convert it into a photocurrent over a wide range of the electromagnetic spectrum, from the ultraviolet to visible and infrared regimes. Over the last several years, a variety of graphene-based photodetectors have been reported, such as graphene transistors, graphene-semiconductor heterojunction photodetectors, graphene based bolometers. It is also reported that there are several physical mechanisms enabling photodetection: photovoltaic effect, photo-thermoelectric effect, bolometric effect, photogating effect, and so on. In this work, we report a simple approach for the realization of graphene based resistive photo-detection devices and the measurements of their photoelectrical response. The graphene were synthesized directly on the glass substrate by novel growth method patented in our lab. Then, the metal electrodes were deposited by thermal evaporation on it, with an electrode length and width of 1.5 mm and 300 μm respectively, using Co to fabricate simple graphene based resistive photosensor. The measurements show that the graphene resistive devices exhibit a photoresponse to the illumination of visible light. The observed re-sistance response was reproducible and similar after many cycles of on and off operations. This photoelectrical response may be attributed not only to the direct photocurrent process but also to the desorption of oxygen. Our work shows that the simple graphene resistive devices have potential in photodetection applications.

Keywords: graphene, resistive sensor, optoelectronics, photoresponse

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Authors: Yasin Ghasemi-Zadeh, Yousef Kavian

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Wireless body area networks (WBANs) technology has achieved lots of popularity over the last decade with a wide range of medical applications. This paper presents an asynchronous media access control (MAC) protocol based on B-MAC protocol by giving an application for medical issues. In WBAN applications, there are some serious problems such as energy, latency, link reliability (quality of wireless link) and throughput which are mainly due to size of sensor networks and human body specifications. To overcome these problems and improving link reliability, we concentrated on MAC layer that supports mobility models for medical applications. In the presented protocol, preamble frames are divided into some sub-frames considering the threshold level. Actually, the main reason for creating shorter preambles is the link reliability where due to some reasons such as water, the body signals are affected on some frequency bands and causes fading and shadowing on signals, therefore by increasing the link reliability, these effects are reduced. In case of mobility model, we use MoBAN model and modify that for some more areas. The presented asynchronous MAC protocol is modeled by OMNeT++ simulator. The results demonstrate increasing the link reliability comparing to B-MAC protocol where the packet reception ratio (PRR) is 92% also covers more mobility areas than MoBAN protocol.

Keywords: wireless body area networks (WBANs), MAC protocol, link reliability, mobility, biomedical

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Authors: Amal E. Ahmed, Levent Trabzon

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Due to the low concentration of the analytes in biological samples, the use of Biological Microelectromechanical System (Bio-MEMS) biosensors for biomolecules detection results in a minuscule output signal that is not good enough for practical applications. In response to this, a need has arisen for an optimized biosensor capable of giving high output signal in response the detection of few analytes in the sample; the ultimate goal is being able to convert the attachment of a single biomolecule into a measurable quantity. For this purpose, MEMS microcantilevers based biosensors emerged as a promising sensing solution because it is simple, cheap, very sensitive and more importantly does not need analytes optical labeling (Label-free). Among the different microcantilever transducing techniques, piezoresistive based microcantilever biosensors became more prominent because it works well in liquid environments and has an integrated readout system. However, the design of piezoresistive microcantilevers is not a straightforward problem due to coupling between the design parameters, constraints, process conditions, and performance. It was found that the parameters that can be optimized to enhance the sensitivity of Piezoresistive microcantilever-based sensors are: cantilever dimensions, cantilever material, cantilever shape, piezoresistor material, piezoresistor doping level, piezoresistor dimensions, piezoresistor position, Stress Concentration Region's (SCR) shape and position. After a systematic analyzation of the effect of each design and process parameters on the sensitivity, a step-wise optimization approach was developed in which almost all these parameters were variated one at each step while fixing the others to get the maximum possible sensitivity at the end. At each step, the goal was to optimize the parameter in a way that it maximizes and concentrates the stress in the piezoresistor region for the same applied force thus get the higher sensitivity. Using this approach, an optimized sensor that has 73.5x times higher electrical sensitivity (ΔR⁄R) than the starting sensor was obtained. In addition to that, this piezoresistive microcantilever biosensor it is more sensitive than the other similar sensors previously reported in the open literature. The mechanical sensitivity of the final senior is -1.5×10-8 Ω/Ω ⁄pN; which means that for each 1pN (10-10 g) biomolecules attach to this biosensor; the piezoresistor resistivity will decrease by 1.5×10-8 Ω. Throughout this work COMSOL Multiphysics 5.0, a commercial Finite Element Analysis (FEA) tool, has been used to simulate the sensor performance.

Keywords: biosensor, microcantilever, piezoresistive, stress concentration region (SCR)

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Authors: Md. Asif Ullah, M. A. R. Sarkar

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This paper illustrates 2-D and 3-D simulations of sub-channels of a Pressurized Water Reactor (PWR) having hexagonal array of fuel rods. At a steady state, the temperature of outer surface of the cladding of fuel rod is kept about 1200°C. The temperature of this isothermal surface is taken as boundary condition for simulation. Water with temperature of 290°C is given as a coolant inlet to the primary water circuit which is pressurized upto 157 bar. Turbulent flow of pressurized water is used for heat removal. In 2-D model, temperature, velocity, pressure and Nusselt number distributions are simulated in a vertical sectional plane through the sub-channels of a hexagonal fuel rod assembly. Temperature, Nusselt number and Y-component of convective heat flux along a line in this plane near the end of fuel rods are plotted for different Reynold’s number. A comparison between X-component and Y-component of convective heat flux in this vertical plane is analyzed. Hexagonal fuel rod assembly has three types of sub-channels according to geometrical shape whose boundary conditions are different too. In 3-D model, temperature, velocity, pressure, Nusselt number, total heat flux magnitude distributions for all the three sub-channels are studied for a suitable Reynold’s number. A horizontal sectional plane is taken from each of the three sub-channels to study temperature, velocity, pressure, Nusselt number and convective heat flux distribution in it. Greater values of temperature, Nusselt number and Y-component of convective heat flux are found for greater Reynold’s number. X-component of convective heat flux is found to be non-zero near the bottom of fuel rod and zero near the end of fuel rod. This indicates that the convective heat transfer occurs totally along the direction of flow near the outlet. As, length to radius ratio of sub-channels is very high, simulation for a short length of the sub-channels are done for graphical interface advantage. For the simulations, Turbulent Flow (K-Є ) module and Heat Transfer in Fluids (ht) module of COMSOL MULTIPHYSICS 5.0 are used.

Keywords: sub-channels, Reynold’s number, Nusselt number, convective heat transfer

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Authors: Branislav Timotijevic, Yves Petremand, Markus Luetzelschwab, Dara Bayat, Laurent Aebi

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Optical miniaturized sensors with remote readout are required devices for the monitoring in harsh electromagnetic environments. As an example, in turbo and hydro generators, excessively high vibrations of the end-windings can lead to dramatic damages, imposing very high, additional service costs. A significant change of the generator temperature can also be an indicator of the system failure. Continuous monitoring of vibrations, temperature, humidity, and gases is therefore mandatory. The high electromagnetic fields in the generators impose the use of non-conductive devices in order to prevent electromagnetic interferences and to electrically isolate the sensing element to the electronic readout. Metal-free sensors are good candidates for such systems since they are immune to very strong electromagnetic fields and given the fact that they are non-conductive. We have realized miniature optical accelerometer and temperature sensors for a remote sensing of the harsh environments using the common, inexpensive silicon Micro Electro-Mechanical System (MEMS) platform. Both devices show highly linear response. The accelerometer has a deviation within 1% from the linear fit when tested in a range 0 – 40 g. The temperature sensor can provide the measurement accuracy better than 1 °C in a range 20 – 150 °C. The design of other type of sensors for the environments with high electromagnetic interferences has also been discussed.

Keywords: optical MEMS, temperature sensor, accelerometer, remote sensing, harsh environment

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Authors: Feng Tao, Han Ye, Shaoyi Liao

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City constructions collapse after earthquake and people will be buried under ruins. Search and rescue should be conducted as soon as possible to save them. Therefore, according to the complicated environment, irregular aftershocks and rescue allow of no delay, a kind of target localization method based on RSSI (Received Signal Strength Indication) is proposed in this article. The target localization technology based on RSSI with the features of low cost and low complexity has been widely applied to nodes localization in WSN (Wireless Sensor Networks). Based on the theory of RSSI transmission and the environment impact to RSSI, this article conducts the experiments in five scenes, and multiple filtering algorithms are applied to original RSSI value in order to establish the signal propagation model with minimum test error respectively. Target location can be calculated from the distance, which can be estimated from signal propagation model, through improved centroid algorithm. Result shows that the localization technology based on RSSI is suitable for large-scale nodes localization. Among filtering algorithms, mixed filtering algorithm (average of average, median and Gaussian filtering) performs better than any other single filtering algorithm, and by using the signal propagation model, the minimum error of distance between known nodes and target node in the five scene is about 3.06m.

Keywords: signal propagation model, centroid algorithm, localization, mixed filtering, RSSI

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

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Authors: Bivek Bhusal, Ana Legrand

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Detection of crop stress is one of the major applications of remote sensing in agriculture. Multiple studies have demonstrated the capability of remote sensing using Unmanned Aerial Vehicle (UAV)-based multispectral imagery for detection of plant stress, but none so far on Nitrogen (N) stress and PLH feeding stress on green beans. In view of its wide host range, geographical distribution, and damage potential, Potato leafhopper- Empoasca fabae (Harris) has been emerging as a key pest in several countries. Monitoring methods for potato leafhopper (PLH) damage, as well as the laboratory techniques for detecting Nitrogen deficiency, are time-consuming and not always easily affordable. A study was initiated to demonstrate if the multispectral sensor attached to a drone can detect PLH stress and N deficiency in beans. Small-plot trials were conducted in the summer of 2023, where cages were used to manipulate PLH infestation in green beans (Provider cultivar) at their first-trifoliate stage. Half of the bean plots were introduced with PLH, and the others were kept insect-free. Half of these plots were grown with the recommended amount of N, and the others were grown without N. Canopy reflectance was captured using a five-band multispectral sensor. Our findings indicate that drone imagery could detect stress due to a lack of N and PLH damage in beans.

Keywords: potato leafhopper, nitrogen, remote sensing, spectral reflectance, beans

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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Authors: Amita Singha

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The project describes the scope of magnetic braking. The potential applications of the braking system can be a de-accelerating system to increase the safety of an elevator or any guided rail transportation system.

Keywords: boost and buck converter, electromagnet, elevator, ferromagnetic material, sensor, solenoid, timer

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Authors: Arturo Daag

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The Philippine Institute of Volcanology and Seismology (PHIVOLCS) is enhancing its liquefaction hazard map towards a detailed probabilistic approach using SDS and geophysical data. Target sites for liquefaction assessment are public schools in Metro Manila. Since target sites are in highly urbanized-setting, the objective of the project is to conduct both non-destructive geotechnical studies using Screw Driving Testing (SDFS) combined with geophysical data such as refraction microtremor array (ReMi), 3 component microtremor Horizontal to Vertical Spectral Ratio (HVSR), and ground penetrating RADAR (GPR). Initial test data was conducted in liquefaction impacted areas from the Mw 6.1 earthquake in Central Luzon last April 22, 2019 Province of Pampanga. Numerous accounts of liquefaction events were documented areas underlain by quaternary alluvium and mostly covered by recent lahar deposits. SDS estimated values showed a good correlation to actual SPT values obtained from available borehole data. Thus, confirming that SDS can be an alternative tool for liquefaction assessment and more efficient in terms of cost and time compared to SPT and CPT. Conducting borehole may limit its access in highly urbanized areas. In order to extend or extrapolate the SPT borehole data, non-destructive geophysical equipment was used. A 3-component microtremor obtains a subsurface velocity model in 1-D seismic shear wave velocity of the upper 30 meters of the profile (Vs30). For the ReMi, 12 geophone array with 6 to 8-meter spacing surveys were conducted. Microtremor data were computed through the Factor of Safety, which is the quotient of Cyclic Resistance Ratio (CRR) and Cyclic Stress Ratio (CSR). Complementary GPR was used to study the subsurface structure and used to inferred subsurface structures and groundwater conditions.

Keywords: screw drive testing, microtremor, ground penetrating RADAR, liquefaction

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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

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: John E. Foley, Martin Miele, Raul Fonda, Jon Jacobson

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This paper presents development and implementation of new and innovative data collection and analysis methodologies based on deployment of total field magnetometer arrays. Our research has focused on the development of a vertically-integrated suite of platforms all utilizing common data acquisition, data processing and analysis tools. These survey platforms include low-altitude helicopters and ground-based vehicles, including robots, for terrestrial mapping applications. For marine settings the sensor arrays are deployed from either a hydrodynamic bottom-following wing towed from a surface vessel or from a towed floating platform for shallow-water settings. Additionally, sensor arrays are deployed from tethered remotely operated vehicles (ROVs) for underwater settings where high maneuverability is required. While the primary application of these systems is the detection and mapping of unexploded ordnance (UXO), these system are also used for various infrastructure mapping and geologic investigations. For each application, success is driven by the integration of magnetometer arrays, accurate geo-positioning, system noise mitigation, and stable deployment of the system in appropriate proximity of expected targets or features. Each of the systems collects geo-registered data compatible with a web-enabled data management system providing immediate access of data and meta-data for remote processing, analysis and delivery of results. This approach allows highly sophisticated magnetic processing methods, including classification based on dipole modeling and remanent magnetization, to be efficiently applied to many projects. This paper also briefly describes the initial development of magnetometer-based detection systems deployed from low-altitude helicopter platforms and the subsequent successful transition of this technology to the marine environment. Additionally, we present examples from a range of terrestrial and marine settings as well as ongoing research efforts related to sensor miniaturization for unmanned aerial vehicle (UAV) magnetic field mapping applications.

Keywords: dipole modeling, magnetometer mapping systems, sub-surface infrastructure mapping, unexploded ordnance detection

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Authors: Kirandeep Kaur, Rohit Mehra, Pargin Bangotra

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Radon and thoron are pervasive radioactive gases and so are their progenies. The progenies of radon and thoron are present in the indoor atmosphere as attached/unattached fractions. In the present work, seasonal variation of concentration of attached and total (attached + unattached) nanosized decay products of indoor radon and thoron has been studied in the dwellings of Bathinda District of Punjab using Deposition based progeny sensors over long integrated times, which are independent of air turbulence. The preliminary results of these measurements are reported particularly regarding DTPS (Direct Thoron Progeny Sensor) and DRPS (Direct Radon Progeny Sensor) for the first time in Bathinda. It has been observed that there is a strong linear relationship in total EERC (Equilibrium Equivalent Radon Concentration) and EETC (Equilibrium Equivalent Thoron Concentration) in rainy season (R2 = 0.83). Further a strong linear relation between total indoor radon concentration and attached fraction has also been observed for the same rainy season (R2= 0.91). The concentration of attached progeny of radon (EERCatt) is 76.3 % of the total Equilibrium Equivalent Radon Concentration (EERC).

Keywords: radon, thoron, progeny, DTPS/DRPS, EERC, EETC, seasonal variation

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Authors: Vishal Kumar, Soumitra Satapathi

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Föster Resonance Energy Transfer (FRET) is a powerful technique used to probe close-range molecular interactions. Physically, the FRET phenomenon manifests as a dipole–dipole interaction between closely juxtaposed fluorescent molecules (10–100 Å). Our effort is to employ this FRET technique to make a prototype device for highly sensitive detection of environment pollutant. Among the most common environmental pollutants, nitroaromatic compounds (NACs) are of particular interest because of their durability and toxicity. That’s why, sensitive and selective detection of small amounts of nitroaromatic explosives, in particular, 2,4,6-trinitrophenol (TNP), 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT) has been a critical challenge due to the increasing threat of explosive-based terrorism and the need of environmental monitoring of drinking and waste water. In addition, the excessive utilization of TNP in several other areas such as burn ointment, pesticides, glass and the leather industry resulted in environmental accumulation, and is eventually contaminating the soil and aquatic systems. To the date, high number of elegant methods, including fluorimetry, gas chromatography, mass, ion-mobility and Raman spectrometry have been successfully applied for explosive detection. Among these efforts, fluorescence-quenching methods based on the mechanism of FRET show good assembly flexibility, high selectivity and sensitivity. Here, we report a FRET-based sensor system for the highly selective detection of NACs, such as TNP, DNT and TNT. The sensor system is composed of a copolymer Poly [(N,N-dimethylacrylamide)-co-(Boc-Trp-EMA)] (RP) bearing tryptophan derivative in the side chain as donor and dansyl tagged copolymer P(MMA-co-Dansyl-Ala-HEMA) (DCP) as an acceptor. Initially, the inherent fluorescence of RP copolymer is quenched by non-radiative energy transfer to DCP which only happens once the two molecules are within Förster critical distance (R0). The excellent spectral overlap (Jλ= 6.08×10¹⁴ nm⁴M⁻¹cm⁻¹) between donors’ (RP) emission profile and acceptors’ (DCP) absorption profile makes them an exciting and efficient FRET pair i.e. further confirmed by the high rate of energy transfer from RP to DCP i.e. 0.87 ns⁻¹ and lifetime measurement by time correlated single photon counting (TCSPC) to validate the 64% FRET efficiency. This FRET pair exhibited a specific fluorescence response to NACs such as DNT, TNT and TNP with 5.4, 2.3 and 0.4 µM LODs, respectively. The detection of NACs occurs with high sensitivity by photoluminescence quenching of FRET signal induced by photo-induced electron transfer (PET) from electron-rich FRET pair to electron-deficient NAC molecules. The estimated stern-volmer constant (KSV) values for DNT, TNT and TNP are 6.9 × 10³, 7.0 × 10³ and 1.6 × 104 M⁻¹, respectively. The mechanistic details of molecular interactions are established by time-resolved fluorescence, steady-state fluorescence and absorption spectroscopy confirmed that the sensing process is of mixed type, i.e. both dynamic and static quenching as lifetime of FRET system (0.73 ns) is reduced to 0.55, 0.57 and 0.61 ns DNT, TNT and TNP, respectively. In summary, the simplicity and sensitivity of this novel FRET sensor opens up the possibility of designing optical sensor of various NACs in one single platform for developing multimodal sensor for environmental monitoring and future field based study.

Keywords: FRET, nitroaromatic, stern-Volmer constant, tryptophan and dansyl tagged copolymer

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Authors: Gurcharan Singh Buttar

Abstract:

It has been discovered that although these two areas, statistics, and information theory, are independent in their nature, they can be combined to create applications in multidisciplinary mathematics. This is due to the fact that where in the field of statistics, statistical parameters (measures) play an essential role in reference to the population (distribution) under investigation. Information measure is crucial in the study of ambiguity, assortment, and unpredictability present in an array of phenomena. The following communication is a link between the two, and it has been demonstrated that the well-known conventional statistical measures can be used as a measure of information.

Keywords: probability distribution, entropy, concavity, symmetry, variance, central tendency

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

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Authors: Ian O'Loughlin

Abstract:

Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

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Authors: Anusha Jain, Santosh Vishwanathan, Praveen K. Gupta, Shwetha S., Kavitha S. N.

Abstract:

Agriculture has a major impact on the Indian economy, with the highest employment ratio than any sector of the country. Currently, most of the traditional agricultural practices and farming methods are manual, which results in farmers not realizing their maximum productivity often due to increasing in labour cost, inefficient use of water sources leading to wastage of water, inadequate soil moisture content, subsequently leading to food insecurity of the country. This research paper aims to solve this problem by developing a full-fledged web application-based platform that has the capacity to associate itself with a Microcontroller-based Automated Irrigation System which schedules the irrigation of crops based on real-time soil moisture content employing soil moisture sensors centric to the crop’s requirements using WSN (Wireless Sensor Networks) and M2M (Machine To Machine Communication) concepts, thus optimizing the use of the available limited water resource, thereby maximizing the crop yield. This robust automated irrigation system provides end-to-end automation of Irrigation of crops at any circumstances such as droughts, irregular rainfall patterns, extreme weather conditions, etc. This platform will also be capable of achieving a nationwide united farming community and ensuring the welfare of farmers. This platform is designed to equip farmers with prerequisite knowledge on tech and the latest farming practices in general. In order to achieve this, the MailChimp mailing service is used through which interested farmers/individuals' email id will be recorded and curated articles on innovations in the world of agriculture will be provided to the farmers via e-mail. In this proposed system, service is enabled on the platform where nearby crop vendors will be able to enter their pickup locations, accepted prices and other relevant information. This will enable farmers to choose their vendors wisely. Along with this, we have created a blogging service that will enable farmers and agricultural enthusiasts to share experiences, helpful knowledge, hardships, etc., with the entire farming community. These are some of the many features that the platform has to offer.

Keywords: WSN (wireless sensor networks), M2M (M/C to M/C communication), automation, irrigation system, sustainability, SAAS (software as a service), soil moisture sensor

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Authors: Marlon Gallo, Eduardo Miguel, Laura Oca, Eneko Gonzalez, Unai Iraola

Abstract:

The heat generation of an energy storage system is an essential topic when designing a battery pack and its cooling system. Heat generation estimation is used together with thermal models to predict battery temperature in operation and adapt the design of the battery pack and the cooling system to these thermal needs guaranteeing its safety and correct operation. In the present work, a comparison between the use of a heat flux sensor (HFS) for indirect measurement of heat losses in a cell and the widely used and simplified version of Bernardi’s equation for estimation is presented. First, a Li-ion cell is thermally characterized with an HFS to measure the thermal parameters that are used in a first-order lumped thermal model. These parameters are the equivalent thermal capacity and the thermal equivalent resistance of a single Li-ion cell. Static (when no current is flowing through the cell) and dynamic (making current flow through the cell) tests are conducted in which HFS is used to measure heat between the cell and the ambient, so thermal capacity and resistances respectively can be calculated. An experimental platform records current, voltage, ambient temperature, surface temperature, and HFS output voltage. Second, an equivalent circuit model is built in a Matlab-Simulink environment. This allows the comparison between the generated heat predicted by Bernardi’s equation and the HFS measurements. Data post-processing is required to extrapolate the heat generation from the HFS measurements, as the sensor records the heat released to the ambient and not the one generated within the cell. Finally, the cell temperature evolution is estimated with the lumped thermal model (using both HFS and Bernardi’s equation total heat generation) and compared towards experimental temperature data (measured with a T-type thermocouple). At the end of this work, a critical review of the results obtained and the possible mismatch reasons are reported. The results show that indirectly measuring the heat generation with HFS gives a more precise estimation than Bernardi’s simplified equation. On the one hand, when using Bernardi’s simplified equation, estimated heat generation differs from cell temperature measurements during charges at high current rates. Additionally, for low capacity cells where a small change in capacity has a great influence on the terminal voltage, the estimated heat generation shows high dependency on the State of Charge (SoC) estimation, and therefore open circuit voltage calculation (as it is SoC dependent). On the other hand, with indirect measuring the heat generation with HFS, the resulting error is a maximum of 0.28ºC in the temperature prediction, in contrast with 1.38ºC with Bernardi’s simplified equation. This illustrates the limitations of Bernardi’s simplified equation for applications where precise heat monitoring is required. For higher current rates, Bernardi’s equation estimates more heat generation and consequently, a higher predicted temperature. Bernardi´s equation accounts for no losses after cutting the charging or discharging current. However, HFS measurement shows that after cutting the current the cell continues generating heat for some time, increasing the error of Bernardi´s equation.

Keywords: lithium-ion battery, heat flux sensor, heat generation, thermal characterization

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Authors: Gopalasingam Daisan

Abstract:

Landmines and ammunitions have been creating a significant threat to the people and animals, after the war, the landmines remain in the land and it plays a vital role in civilian’s security. Especially the Children are at the highest risk because they are curious. After all, an unexploded bomb can look like a tempting toy to an inquisitive child. The initial step of designing the UAS (Unmanned Aircraft Systems) for landmine detection is to choose an appropriate and effective sensor to locate the landmines and other unexploded ammunitions. The sensor weight and other components related to the sensor supporting device’s weight are taken as a payload weight. The mission requirement is to find the landmines in a particular area by making a proper path that will cover all the vicinity in the desired area. The weight estimation of the UAV (Unmanned Aerial Vehicle) can be estimated by various techniques discovered previously with good accuracy at the first phase of the design. The next crucial part of the design is to calculate the power requirement and the wing loading calculations. The matching plot techniques are used to determine the thrust-to-weight ratio, and this technique makes this process not only easiest but also precisely. The wing loading can be calculated easily from the stall equation. After these calculations, the wing area is determined from the wing loading equation and the required power is calculated from the thrust to weight ratio calculations. According to the power requirement, an appropriate engine can be selected from the available engine from the market. And the wing geometric parameter is chosen based on the conceptual sketch. The important steps in the wing design to choose proper aerofoil and which will ensure to create sufficient lift coefficient to satisfy the requirements. The next component is the tail; the tail area and other related parameters can be estimated or calculated to counteract the effect of the wing pitching moment. As the vertical tail design depends on many parameters, the initial sizing only can be done in this phase. The fuselage is another major component, which is selected based on the slenderness ratio, and also the shape is determined on the sensor size to fit it under the fuselage. The landing gear is one of the important components which is selected based on the controllability and stability requirements. The minimum and maximum wheel track and wheelbase can be determined based on the crosswind and overturn angle requirements. The minor components of the landing gear design and estimation are not the focus of this project. Another important task is to calculate the weight of the major components and it is going to be estimated using empirical relations and also the mass is added to each such component. The CG and moment of inertia are also determined to each component separately. The sensitivity of the weight calculation is taken into consideration to avoid extra material requirements and also reduce the cost of the design. Finally, the aircraft performance is calculated, especially the V-n (velocity and load factor) diagram for different flight conditions such as not disturbed and with gust velocity.

Keywords: landmine, UAS, matching plot, optimization

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Authors: Raz Flieshman, Adam Michael Altenbuchner, Jörg Krüger

Abstract:

This paper presents an approach to optimizing the Fused Deposition Modeling (FDM) 3D printing process through a preprocess calibration routine of printing parameters. The core of this method involves the use of a low-cost thermal sensor capable of measuring tempera-tures within the range of -20 to 500 degrees Celsius for detailed process observation. The calibration process is conducted by printing a predetermined path while varying the process parameters through machine instructions (g-code). This enables the extraction of critical thermal, dimensional, and surface properties along the printed path. The calibration routine utilizes computer vision models to extract features and metrics from the thermal images, in-cluding temperature distribution, layer adhesion quality, surface roughness, and dimension-al accuracy and consistency. These extracted properties are then analyzed to optimize the process parameters to achieve the desired qualities of the printed material. A significant benefit of this calibration method is its potential to create printing parameter profiles for new polymer and composite materials, thereby enhancing the versatility and application range of FDM 3D printing. The proposed method demonstrates significant potential in enhancing the precision and reliability of FDM 3D printing, making it a valuable contribution to the field of additive manufacturing.

Keywords: FDM 3D printing, preprocess calibration, thermal sensor, process optimization, additive manufacturing, computer vision, material profiles

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Authors: Hamed Ahari, Behrouz Akbari Adreghani, Vadood Razavilar, Amirali Anvar, Sima Moradi, Hourieh Shalchi

Abstract:

Considering the ever increasing population and industrialization of the developmental trend of humankind's life, we are no longer able to detect the toxins produced in food products using the traditional techniques. This is due to the fact that the isolation time for food products is not cost-effective and even in most of the cases, the precision in the practical techniques like the bacterial cultivation and other techniques suffer from operator errors or the errors of the mixtures used. Hence with the advent of nanotechnology, the design of selective and smart sensors is one of the greatest industrial revelations of the quality control of food products that in few minutes time, and with a very high precision can identify the volume and toxicity of the bacteria. Methods and Materials: In this technique, based on the bacterial antibody connection to nanoparticle, a sensor was used. In this part of the research, as the basis for absorption for the recognition of bacterial toxin, medium sized silica nanoparticles of 10 nanometer in form of solid powder were utilized with Notrino brand. Then the suspension produced from agent-linked nanosilica which was connected to bacterial antibody was positioned near the samples of distilled water, which were contaminated with Staphylococcus aureus bacterial toxin with the density of 10-3, so that in case any toxin exists in the sample, a connection between toxin antigen and antibody would be formed. Finally, the light absorption related to the connection of antigen to the particle attached antibody was measured using spectrophotometry. The gene of 23S rRNA that is conserved in all Staphylococcus spp., also used as control. The accuracy of the test was monitored by using serial dilution (l0-6) of overnight cell culture of Staphylococcus spp., bacteria (OD600: 0.02 = 107 cell). It showed that the sensitivity of PCR is 10 bacteria per ml of cells within few hours. Result: The results indicate that the sensor detects up to 10-4 density. Additionally, the sensitivity of the sensors was examined after 60 days, the sensor by the 56 days had confirmatory results and started to decrease after those time periods. Conclusions: Comparing practical nano biosensory to conventional methods like that culture and biotechnology methods(such as polymerase chain reaction) is accuracy, sensitiveness and being unique. In the other way, they reduce the time from the hours to the 30 minutes.

Keywords: exotoxin, nanobiosensor, recognition, Staphylococcus aureus

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Authors: Nour Hassan Ismail, Abdelmonem Nassar, Khaled Baz

Abstract:

Semiconductor crystals smaller than about 10 nm, known as quantum dots, have properties that differ from large samples, including a band gap that becomes larger for smaller particles. These properties create several applications for quantum dots. In this paper, new shapes of quantum dot arrays are used to enhance the photo physical properties of gold nano-particles. This paper presents a study of the effect of nano-particles shape, array, and size on their absorption characteristics.

Keywords: quantum dots, nano-particles, LSPR

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Authors: Hui Zhang, Sherif Beskhyroun

Abstract:

In this study, a highly flexible LoRa-Based wireless sensing system was used to assess the strain state performance of building structures. The system was developed to address the local damage limitation of structural health monitoring (SHM) systems. The system is part of an intelligent SHM system designed to monitor, collect and transmit strain changes in key structural components. The main purpose of the wireless sensor system is to reduce the development and installation costs, and reduce the power consumption of the system, so as to achieve long-time monitoring. The highly stretchable flexible strain gauge is mounted on the surface of the structure and is waterproof, heat resistant, and low temperature resistant, greatly reducing the installation and maintenance costs of the sensor. The system was also developed with the aim of using LoRa wireless communication technology to achieve both low power consumption and long-distance transmission, therefore solving the problem of large-scale deployment of sensors to cover more areas in large structures. In the long-term monitoring of the building structure, the system shows very high performance, very low actual power consumption, and wireless transmission stability. The results show that the developed system has a high resolution, sensitivity, and high possibility of long-term monitoring.

Keywords: LoRa, SHM system, strain measurement, civil structures, flexible sensing system

Procedia PDF Downloads 106