Search results for: gravity gradient sensor

Authors: Qihang Zeng, Wei Xu, Ying Shen, Changyuan Yu

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In this paper, a highly sensitive fiber-optic curvature sensor based on four mode fiber (FMF) is presented and investigated. The proposed sensing structure is constructed by fusing a section of FMF into two standard single mode fibers (SMFs) concatenated with two no core fiber (NCF), i.e., SMF-NCF-FMF-NCF-SMF structure is fabricated. The length of the NCF is very short about 1 millimeter acting as exciting/recoupling the light from/into the core of the SMF, while the FMF is with 3 centimeters long supporting four eigenmodes including LP₀₁, LP₁₁, LP₂₁ and LP₀₂. High core modes in FMF can be effectively stimulated owing to mismatched mode field distribution and the mainly sensing principle is based on modal interferometer spectrum analysis. Different curvatures induce different strains on the FMF such that affecting the modal excitation, resulting spectrum shifts. One can get the curvature value by tracking the wavelength shifting. Experiments have been done to address the sensing performance, which is about 7.8 nm/m⁻¹ within a range of 1.90 m⁻¹~3.18 m⁻¹.

Keywords: curvature, four mode fiber, highly sensitive, modal interferometer

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of liver and spleen from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of ROI from CT images is usually a difficult task. This difficulty is the gray’s level of which is similar to the other organ also the ROI are connected to the ribs, heart, kidneys, etc. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the surrounding and connected organs and tissues by applying morphological filters. This first step makes the extraction of interest regions easier. The second step consists of improving the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce these deficiencies by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: Phornpat Chewasoonthorn, Surat Kwanmuang

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Indoor positioning technologies have been evolved rapidly. They augment the Global Positioning System (GPS) which requires line-of-sight to the sky to track the location of people or objects. This study developed an error correction method for an indoor real-time location system (RTLS) based on an ultra-wideband (UWB) sensor from Decawave. Multiple stationary nodes (anchor) were installed throughout the workspace. The distance between stationary and moving nodes (tag) can be measured using a two-way-ranging (TWR) scheme. The result has shown that the uncorrected ranging error from the sensor system can be as large as 1 m. To reduce ranging error and thus increase positioning accuracy, This study purposes an online correction algorithm using the Kalman filter. The results from experiments have shown that the system can reduce ranging error down to 5 cm.

Keywords: indoor positioning, ultra-wideband, error correction, Kalman filter

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Authors: Annu Sheokand, Vinay Kumar

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Hydrogen sulfide (H₂S) detection is essential for environmental monitoring and industrial safety due to its high toxicity, even at low concentrations. This study explores the H₂S gas sensing properties of Cu-doped Fe₂O₃ materials derived from metal-organic frameworks (MOFs), which offer high surface area and controlled porosity for optimized gas sensing. The structural and morphological characteristics of the synthesized material were thoroughly analyzed using techniques such as X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), and UV-Vis Spectroscopy. The resulting sensor exhibited remarkable sensitivity and selectivity, achieving a detection limit at the ppb level for H₂S. The study indicates that Cu doping significantly enhances the gas sensing performance of Fe₂O₃ by introducing abundant active sites within the material. These enhanced sensing properties emphasize the potential of MOF-derived Cu-doped Fe₂O₃ as a highly effective material for H₂S gas sensors in various applications.

Keywords: detection limit, doping, MOF, sensitivity, sensor

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Authors: Nidhi Gupta, Omita Nanda, Rakhi Grover, Kanchan Saxena

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Hybrid perovskite is new class of material which has gained much attention due to their different crystal structure and interesting optical and electrical properties. Easy fabrication, high absorption coefficient, and photoluminescence properties make them a strong candidate for various applications such as sensors, photovoltaics, photodetectors, etc. In perovskites, ions arrange themselves in a special type of crystal structure with chemical formula ABX3, where A is organic species like CH3NH3+, B is metal ion (e.g., Pb, Sn, etc.) and X is halide (Cl-, Br-, I-). In crystal structure, A is present at corner position, B at center of the crystal lattice and halide ions at the face centers. High stability and sensitivity of nanostructured perovskite make them suitable for chemical sensors. Researchers have studied sensing properties of perovskites for number of analytes such as 2,4,6-trinitrophenol, ethanol and other hazardous chemical compounds. Ammonia being highly toxic agent makes it a reason of concern for the environment. Thus the detection of ammonia is extremely important. Our present investigation deals with organic inorganic hybrid perovskite based ammonia sensor. Various methods like sol-gel, solid state synthesis, thermal vapor deposition etc can be used to synthesize Different hybrid perovskites. In the present work, a novel hybrid perovskite has been synthesized by a single step method. Ethylenediammnedihalide and lead halide were used as precursor. Formation of hybrid perovskite was confirmed by FT-IR and XRD. Morphological characterization of the synthesized material was performed using scanning electron microscopy (SEM). SEM analysis revealed the formation of one dimensional nanowire perovskite with mean diameter of 200 nm. Measurements for sensing properties of halide perovskite for ammonia vapor were carried out. Perovskite thin films showed a color change from yellow to orange on exposure of ammonia vapor. Electro-optical measurements show that sensor based on lead halide perovskite has high sensitivity towards ammonia with effective selectivity and reversibility. Sensor exhibited rapid response time of less than 20 seconds.

Keywords: hybrid perovskite, ammonia, sensor, nanostructure, thin film

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Authors: Lifar M. S., Tereshchenko A. A., Bulgakov A. N., Guda S. A., Guda A. A., Soldatov A. V.

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Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes - adsorption, activation, reaction, and desorption. These processes, in turn, depend on the inlet feed concentrations, temperature, and pressure. At stationary conditions, the active surface sites may be poisoned by reaction byproducts or blocked by thermodynamically adsorbed gaseous reagents. Thus, the yield of reaction products can significantly drop. On the contrary, the dynamic control accounts for the changes in the surface properties and adjusts reaction parameters accordingly. Therefore dynamic control may be more efficient than stationary control. In this work, a reinforcement learning algorithm has been applied to control the simulation of CO oxidation on a catalyst. The policy gradient algorithm is learned to maximize the CO₂ production rate based on the CO and O₂ flows at a given time step. Nonstationary solutions were found for the regime with surface deactivation. The maximal product yield was achieved for periodic variations of the gas flows, ensuring a balance between available adsorption sites and the concentration of activated intermediates. This methodology opens a perspective for the optimization of catalytic reactions under nonstationary conditions.

Keywords: artificial intelligence, catalyst, co oxidation, reinforcement learning, dynamic control

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Authors: Bo-Bin Xiaoa, Chia-Yi Liua

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In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better.

Keywords: double Q learning, dueling network, NoisyNet, source searching

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Authors: Francesco Morgan Bono, Simone Cinquemani

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This research aims to enhance bridge monitoring by employing innovative techniques that incorporate exogenous factors into the modeling of sensor signals, thereby improving long-term predictability beyond traditional static methods. Using real datasets from two different bridges equipped with Linear Variable Displacement Transducer (LVDT) sensors, the study investigates the fundamental principles governing sensor behavior for more precise long-term forecasts. Additionally, the research evaluates performance on noisy and synthetically damaged data, proposing a residual-based alarm system to detect anomalies in the bridge. In summary, this novel approach combines advanced modeling, exogenous factors, and anomaly detection to extend prediction horizons and improve preemptive damage recognition, significantly advancing structural health monitoring practices.

Keywords: structural health monitoring, dynamic models, sindy, railway bridges

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Authors: Daniel Mueller, Melanie Breitbach, Stefan Cornelius, Sarah Pakulla-Dickel, Margaretha Koenig, Anke Prochnow, Mario Scherer

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The European STR standard set (ESS) of loci as well as the new expanded CODIS core loci set as recommended by the CODIS Core Loci Working Group, has led to a higher standardization and harmonization in STR analysis across borders. Various multiplex PCRs assays have since been developed for the analysis of these 17 ESS or 23 CODIS expansion STR markers that all meet high technical demands. However, forensic analysts are often faced with difficult STR results and the questions thereupon. What is the reason that no peaks are visible in the electropherogram? Did the PCR fail? Was the DNA concentration too low? QIAGEN’s newest Investigator STR kits contain a novel Quality Sensor (QS) that acts as internal performance control and gives useful information for evaluating the amplification efficiency of the PCR. QS indicates if the reaction has worked in general and furthermore allows discriminating between the presence of inhibitors or DNA degradation as a cause for the typical ski slope effect observed in STR profiles of such challenging samples. This information can be used to choose the most appropriate rework strategy.Based on the latest PCR chemistry called FRM 2.0, QIAGEN now provides the next technological generation for STR analysis, the Investigator ESSplex SE QS and Investigator 24plex QS Kits. The new PCR chemistry ensures robust and fast PCR amplification with improved inhibitor resistance and easy handling for a manual or automated setup. The short cycling time of 60 min reduces the duration of the total PCR analysis to make a whole workflow analysis in one day more likely. To facilitate the interpretation of STR results a smart primer design was applied for best possible marker distribution, highest concordance rates and a robust gender typing.

Keywords: PCR, QIAGEN, quality sensor, STR

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Authors: R. Gadea, J. M. Monzó, F. J. Puertas, M. Castro, A. Tebar, P. J. Fito, R. J. Colom

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There exists a wide array of ailments impacting the structural soundness of the esophageal walls, predominantly linked to digestive issues. Presently, the techniques employed for identifying esophageal tract complications are excessively invasive and discomforting, subjecting patients to prolonged discomfort in order to achieve an accurate diagnosis. This study proposes the creation of a sensor with profound measuring capabilities designed to detect fluids coursing through the esophageal tract. The multi-sensor detection system relies on radiofrequency photospectrometry. During experimentation, individuals representing diverse demographics in terms of gender and age were utilized, positioning the sensors amidst the trachea and diaphragm and assessing measurements in vacuum conditions, water, orange juice, and saline solutions. The findings garnered enabled the identification of various liquid mediums within the esophagus, segregating them based on their ionic composition.

Keywords: radiofrequency spectrophotometry, medical device, gastroesophageal disease, photonics

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: B. Atli-Veltin, A. Vosteen, D. Megan, A. Jedynska, L. K. Cheng

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Composite materials combine the advantages of being lightweight and possessing high strength. This is in particular of interest for the development of large constructions, e.g., aircraft, space applications, wind turbines, etc. One of the shortcomings of using composite materials is the complex nature of the failure mechanisms which makes it difficult to predict the remaining lifetime. Therefore, condition and health monitoring are essential for using composite material for critical parts of a construction. Different types of sensors are used/developed to monitor composite structures. These include ultrasonic, thermography, shearography and fiber optic. The first 3 technologies are complex and mostly used for measurement in laboratory or during maintenance of the construction. Optical fiber sensor can be surface mounted or embedded in the composite construction to provide the unique advantage of in-operation measurement of mechanical strain and other parameters of interest. This is identified to be a promising technology for Structural Health Monitoring (SHM) or Prognostic Health Monitoring (PHM) of composite constructions. Among the different fiber optic sensing technologies, Fiber Bragg Grating (FBG) sensor is the most mature and widely used. FBG sensors can be realized in an array configuration with many FBGs in a single optical fiber. In the current project, different aspects of using embedded FBG for composite wind turbine monitoring are investigated. The activities are divided into two parts. Firstly, FBG embedded carbon composite laminate is subjected to tensile and bending loading to investigate the response of FBG which are placed in different orientations with respect to the fiber. Secondly, the demonstration of using FBG sensor array for temperature and strain sensing and monitoring of a 5 m long scale model of a glass fiber mono-pile is investigated. Two different FBG types are used; special in-house fibers and off-the-shelf ones. The results from the first part of the study are showing that the FBG sensors survive the conditions during the production of the laminate. The test results from the tensile and the bending experiments are indicating that the sensors successfully response to the change of strain. The measurements from the sensors will be correlated with the strain gauges that are placed on the surface of the laminates.

Keywords: Fiber Bragg Gratings, embedded sensors, health monitoring, wind turbine towers

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Authors: Ahmed Elaksher, Islam Omar

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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.

Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Jonathan Sohn

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Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods.

Keywords: deep neural network, adversarial attack, adversarial defense, adversarial machine learning

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Authors: Max Sardou, Patricia Sardou

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Now that weight saving is mandatory, to author best knowledge composite springs, that we have invented, are best choice for automotive suspensions, against steel. So, we have created a Joint Ventures called S.ARA, in order to mass produce composite coils springs. Start of Production of composite coils springs was in 2014 for AUDI. As we have demonstrated, on the road, that composite springs are not a sweet dream. The present paper describes all the benefits of ‘C’ springs and ‘S’ springs for high performance vehicles suspension, for rocket stage separation, and for satellite injection into orbit. Developing rocket stage separation, we have developed for CNES (Centre National d’Etudes Spatiales) the following concept. If we call ‘line of action’ a line going from one end of a spring to the other. Our concept is to use for instance two springs inclined. In such a way that their line of action cross together and create at this crossing point a virtual center well above the springs. This virtual center, is pulling from above the top stage and is offering a guidance, perfectly stable and straight. About buildings, our solution is to transfer this rocket technology, creating a ‘virtual center’ of pendulation positioned above the building center of gravity. This is achieved by using tilted composite springs benches oriented in such a way that their line of action converges creating the ‘virtual center’. Thanks to the ‘virtual center’ position, the building behaves as a pendulum, hanged from above. When earthquake happen then the building will oscillate around its ‘virtual center’ and will go back safely to equilibrium after the tremor. ‘C’ springs, offering anti-rust, anti-settlement, fail-safe suspension, plus virtual center solution is the must for long-lasting, perfect protection of buildings against earthquakes.

Keywords: virtual center of tilt, composite springs, fail safe springs, antiseismic suspention

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Authors: Ajith Kumar S., Sankaran Namboothiri, Sankrish J., SarathKumar S., S. Anil Lal

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A numerical analysis of flow over a heated circular cylinder is done in this paper. The governing equations, Navier-Stokes, and energy equation within the Boussinesq approximation along with continuity equation are solved using hybrid FEM-FVM technique. The density gradient created due to the heating of the cylinder will induce buoyancy force, opposite to the direction of action of acceleration due to gravity, g. In the present work, the flow direction and the direction of buoyancy force are taken as same (vertical flow configuration), so that the buoyancy force accelerates the mean flow past the cylinder. The relative dominance of the buoyancy force over the inertia force is characterized by the Richardson number (Ri), which is one of the parameter that governs the flow dynamics and heat transfer in this analysis. It is well known that above a certain value of Reynolds number, Re (ratio of inertia force over the viscous forces), the unsteady Von Karman vortices can be seen shedding behind the cylinder. The shedding wake patterns could be seriously altered by heating/cooling the cylinder. The non-dimensional shedding frequency called the Strouhal number is found to be increasing as Ri increases. The aerodynamic force coefficients CL and CD are observed to change its value. In the present vertical configuration of flow over the cylinder, as Ri increases, shedding frequency gets increased and suddenly drops down to zero at a critical value of Richardson number. The unsteady vortices turn to steady standing recirculation bubbles behind the cylinder after this critical Richardson number. This phenomenon is well known in literature as "Breakdown of the Karman Vortex Street". It is interesting to see the flow structures on further increase in the Richardson number. On further heating of the cylinder surface, the size of the recirculation bubble decreases without loosing its symmetry about the horizontal axis passing through the center of the cylinder. The separation angle is found to be decreasing with Ri. Finally, we observed a second critical Richardson number, after which the the flow will be attached to the cylinder surface without any wake behind it. The flow structures will be symmetrical not only about the horizontal axis, but also with the vertical axis passing through the center of the cylinder. At this stage, there will be a "single plume" emanating from the rear stagnation point of the cylinder. We also observed the transition of the plume is a strong function of the Richardson number.

Keywords: drag reduction, flow over circular cylinder, flow control, mixed convection flow, vortex shedding, vortex breakdown

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Authors: Yuqing Chen, Ying Xu, Renfa Li

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The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

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Authors: Bartlomiej S. Witkowski, Lukasz Wachnicki, Sylwia Gieraltowska, Rafal Pietruszka, Marek Godlewski

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Zinc oxide is extensively studied II-VI semiconductor with a direct energy gap of about 3.37 eV at room temperature and high transparency in visible light spectral region. Due to these properties, ZnO is an attractive material for applications in photovoltaic, electronic and optoelectronic devices. ZnO nanorods, due to a well-developed surface, have potential of applications in sensor technology and photovoltaics. In this work we present a new inexpensive method of the ultra-fast growth of ZnO nanorods from the aqueous solution. This environment friendly and fully reproducible method allows growth of nanorods in few minutes time on various substrates, without any catalyst or complexing agent. Growth temperature does not exceed 50ºC and growth can be performed at atmospheric pressure. The method is characterized by simplicity and allows regulation of size of the ZnO nanorods in a large extent. Moreover the method is also very safe, it requires organic, non-toxic and low-price precursors. The growth can be performed on almost any type of substrate through the homo-nucleation as well as hetero-nucleation. Moreover, received nanorods are characterized by a very high quality - they are monocrystalline as confirmed by XRD and transmission electron microscopy. Importantly oxygen vacancies are not found in the photoluminescence measurements. First results for obtained by us ZnO nanorods in sensor applications are very promising. Resistance UV sensor, based on ZnO nanorods grown on a quartz substrates shows high sensitivity of 20 mW/m2 (2 μW/cm2) for point contacts, especially that the results are obtained for the nanorods array, not for a single nanorod. UV light (below 400 nm of wavelength) generates electron-hole pairs, which results in a removal from the surfaces of the water vapor and hydroxyl groups. This reduces the depletion layer in nanorods, and thus lowers the resistance of the structure. The so-obtained sensor works at room temperature and does not need the annealing to reset to initial state. Details of the technology and the first sensors results will be presented. The obtained ZnO nanorods are also applied in simple-architecture photovoltaic cells (efficiency over 12%) in conjunction with low-price Si substrates and high-sensitive photoresistors. Details informations about technology and applications will be presented.

Keywords: hydrothermal method, photoresistor, photovoltaic cells, ZnO nanorods

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

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Authors: Seyed Mahdi Jameii, Arash Nikdel, Seyed Mohsen Jameii

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Energy efficiency is an important issue in the field of Wireless Sensor Networks (WSNs). So, minimizing the energy consumption in this kind of networks should be an essential consideration. Sleep/wake scheduling mechanism is an efficient approach to handling this issue. In this paper, we propose a Genetic Algorithm-based Sleep-Wake up Area Coverage protocol called GA-SWAC. The proposed protocol puts the minimum of nodes in active mode and adjusts the sensing radius of each active node to decrease the energy consumption while maintaining the network’s coverage. The proposed protocol is simulated. The results demonstrate the efficiency of the proposed protocol in terms of coverage ratio, number of active nodes and energy consumption.

Keywords: wireless sensor networks, genetic algorithm, coverage, connectivity

Procedia PDF Downloads 519

Authors: Leyla Esfandiari

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Rapid, sensitive detection of nucleic acid (NA) molecules of specific sequence is of interest for a range of diverse health-related applications such as screening for genetic diseases, detecting pathogenic microbes in food and water, and identifying biological warfare agents in homeland security. Sequence-specific nucleic acid detection platforms rely on base pairing interaction between two complementary single stranded NAs, which can be detected by the optical, mechanical, or electrochemical readout. However, many of the existing platforms require amplification by polymerase chain reaction (PCR), fluorescent or enzymatic labels, and expensive or bulky instrumentation. In an effort to address these shortcomings, our research is focused on utilizing the cutting edge nanotechnology and microfluidics along with resistive pulse electrical measurements to design and develop a cost-effective, handheld and highly-sensitive nanopore-based sensor for point-of-care medical diagnostics.

Keywords: diagnostics, nanopore, nucleic acids, sensor

Procedia PDF Downloads 462

Authors: Apurva Patil, Ashay Aswale, Akshay Kulkarni, Shubham Bharadiya

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The potential benefit of gravity compensation of linkages in mechanisms using springs to reduce actuator requirements is well recognized, but practical applications have been elusive. Although existing methods provide exact spring balance, they require additional masses or auxiliary links, or all the springs used originate from the ground, which makes the resulting device bulky and space-inefficient. This paper uses a method of static balancing of mechanisms with conservative loads such as gravity and spring loads using non-zero-free-length springs with child–parent connections and no auxiliary links. Application of this method to the developed arm of a humanoid robot is presented here. Spring balancing is particularly important in this case because the serial chain of linkages has to work against gravity.This work involves approximate spring balancing of the open-loop chain of linkages using minimization of potential energy variance. It uses the approach of flattening the potential energy distribution over the workspace and fuses it with numerical optimization. The results show the considerable reduction in actuator torque requirement with practical spring design and arrangement. Reduced actuator torque facilitates the use of lower end actuators which are generally smaller in weight and volume thereby lowering the space requirements and the total weight of the arm. This is particularly important for humanoid robots where the parent actuator has to handle the weight of the subsequent actuators as well. Actuators with lower actuation requirements are more energy efficient, thereby reduce the energy consumption of the mechanism. Lower end actuators are lower in cost and facilitate the development of low-cost devices. Although the method provides only an approximate balancing, it is versatile, flexible in choosing appropriate control variables that are relevant to the design problem and easy to implement. The true potential of this technique lies in the fact that it uses a very simple optimization to find the spring constant, free-length of the spring and the optimal attachment points subject to the optimization constraints. Also, it uses physically realizable non-zero-free-length springs directly, thereby reducing the complexity involved in simulating zero-free-length springs from non-zero-free-length springs. This method allows springs to be attached to the preceding parent link, which makes the implementation of spring balancing practical. Because auxiliary linkages can be avoided, the resultant arm of the humanoid robot is compact. The cost benefits and reduced complexity can be significant advantages in the development of this arm of the humanoid robot.

Keywords: actuator torque, child-parent connections, spring balancing, the arm of a humanoid robot

Procedia PDF Downloads 242

Authors: Łukasz Ambroziak

Abstract:

Although the impact of the production fragmentation on trade flows has been examined many times since the 1990s, the research was not comprehensive because of the limitations in traditional trade statistics. Early 2010s the complex databases containing world input-output tables (or indicators calculated on their basis) has made available. It increased the possibilities of examining the production sharing in the world. The trade statistic in value-added terms enables us better to estimate trade changes resulted from the internationalisation and globalisation as well as benefits of the countries from international trade. In the literature, there are many research studies on this topic. Unfortunately, trade in value added of the Central and Eastern European Countries (CEECs) has been so far insufficiently studied. Thus, the aim of the paper is to present changes in value added trade of the CEECs (Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia) in the period of 1995-2011. The concept 'trade in value added' or 'value added trade' is defined as the value added of a country which is directly and indirectly embodied in final consumption of another country. The typical question would be: 'How much value added is created in a country due to final consumption in the other countries?' The data will be downloaded from the World Input-Output Database (WIOD). The structure of this paper is as follows. First, theoretical and methodological aspects related to the application of the input-output tables in the trade analysis will be studied. Second, a brief survey of the empirical literature on this topic will be presented. Third, changes in exports and imports in value added of the CEECs will be analysed. A special attention will be paid to the differences in bilateral trade balances using traditional trade statistics (in gross terms) on one side, and value added statistics on the other. Next, in order to identify factors influencing value added exports and value added imports of the CEECs the generalised gravity model, based on panel data, will be used. The dependent variables will be value added exports and imports. The independent variables will be, among others, the level of GDP of trading partners, the level of GDP per capita of trading partners, the differences in GDP per capita, the level of the FDI inward stock, the geographical distance, the existence (or non-existence) of common border, the membership (or not) in preferential trade agreements or in the EU. For comparison, an estimation will also be made based on exports and imports in gross terms. The initial research results show that the gravity model better explained determinants of trade in value added than gross trade (R2 in the former is higher). The independent variables had the same direction of impact both on value added exports/imports and gross exports/imports. Only value of coefficients differs. The most difference concerned geographical distance. It had smaller impact on trade in value added than gross trade.

Keywords: central and eastern European countries, gravity model, input-output tables, trade in value added

Procedia PDF Downloads 238

Authors: Shuba Anastasiia, Kuchmenko Tatiana, Umarkhanov Ruslan

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The development of new sensitive coatings for sensors is one of the key directions in the development of sensor technologies. Recently, there has been a trend towards the creation of multicomponent coatings for sensors, which make it possible to increase the sensitivity, and specificity, and improve the performance properties of sensors. When analyzing samples with a complex matrix of biological origin, the inclusion of micelles of bioactive substances (amino and nucleic acids, peptides, proteins) in the composition of the sensor coating can also increase useful analytical information. The purpose of this work is to evaluate the analytical characteristics of composite coatings of piezoelectric quartz sensors based on medium-molecular viscous sorbents with incorporated micellar casein concentrate during the sorption of vapors of volatile organic compounds. The sorption properties of the coatings were studied by piezoelectric quartz microbalance. Macromolecular compounds (dicyclohexyl-18-crown-6, triton X-100, lanolin, micellar casein concentrate) were used as sorbents. Highly volatile organic compounds of various classes (alcohols, acids, aldehydes, esters) and water were selected as test substances. It has been established that composite coatings of sensors with the inclusion of micellar casein are more stable and selective to vapors of highly volatile compounds than to water vapors. The method and technique of forming a composite coating using molecular viscous sorbents do not affect the kinetic features of VOC sorption. When casein micelles are used, the features of kinetic sorption depend on the matrix of the coating.

Keywords: piezoquartz sensor, viscous sorbents, micellar casein, coating, volatile compounds

Procedia PDF Downloads 120

Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

Procedia PDF Downloads 122

Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

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In the current paper, an indoor positioning system is proposed with consideration of measurement delay. Firstly, an estimation filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least square criterion, which utilizes only finite measurements on the most recent window. The proposed estimation filtering based scheme gives the filtered estimates for position, velocity and acceleration of moving target in real-time, while removing undesired noisy effects and preserving desired moving positions. Secondly, the proposed scheme is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Finally, computer simulations shows that the performance of the proposed estimation filtering based scheme can outperform to the existing infinite memory filtering based mechanism.

Keywords: indoor positioning system, wireless sensor networks, measurement delay

Procedia PDF Downloads 481

Authors: Akber Oumer Abdurezak

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Road traffic accident is a major problem in Ethiopia, resulting in the deaths of many people and potential injuries and crash every year and loss of properties. According to the Federal Transport Authority, one of the main causes of traffic accident and crash in Ethiopia is over speeding. Implementation of different technologies is used to monitor the speed of vehicles in order to minimize accidents and crashes. This research aimed at designing a speed monitoring system to monitor the speed of travelling vehicles and movements, reporting illegal speeds or overspeeding vehicles to the concerned bodies. The implementation of the system is through a wireless sensor network. The proposed system can sense and detect the movement of vehicles, process, and analysis the data obtained from the sensor and the cloud system. The data is sent to the central controlling server. The system contains accelerometer and gyroscope sensors to sense and collect the data of the vehicle. Arduino to process the data and Global System for Mobile Communication (GSM) module for communication purposes to send the data to the concerned body. When the speed of the vehicle exceeds the allowable speed limit, the system sends a message to database as “over speeding”. Both accelerometer and gyroscope sensors are used to collect acceleration data. The acceleration data then convert to speed, and the corresponding speed is checked with the speed limit, and those above the speed limit are reported to the concerned authorities to avoid frequent accidents. The proposed system decreases the occurrence of accidents and crashes due to overspeeding and can be used as an eye opener for the implementation of other intelligent transport system technologies. This system can also integrate with other technologies like GPS and Google Maps to obtain better output.

Keywords: accelerometer, IOT, GSM, gyroscope

Procedia PDF Downloads 74

Authors: Lukasz Grabowski, Konrad Pietrykowski, Michal Bialy

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This paper presents the results of the research to verify the combustion in aircraft piston engine Asz62-IR. This engine was modernized and a type of ignition system was developed. Due to the high costs of experiments of a nine-cylinder 1,000 hp aircraft engine, a simulation technique should be applied. Therefore, computational fluid dynamics to simulate the combustion process is a reasonable solution. Accordingly, the tests for varied ignition advance angles were carried out and the optimal value to be tested on a real engine was specified. The CFD model was created with the AVL Fire software. The engine in the research had two spark plugs for each cylinder and ignition advance angles had to be set up separately for each spark. The results of the simulation were verified by comparing the pressure in the cylinder. The courses of the indicated pressure of the engine mounted on a test stand were compared. The real course of pressure was measured with an optical sensor, mounted in a specially drilled hole between the valves. It was the OPTRAND pressure sensor, which was designed especially to engine combustion process research. The indicated pressure was measured in cylinder no 3. The engine was running at take-off power. The engine was loaded by a propeller at a special test bench. The verification of the CFD simulation results was based on the results of the test bench studies. The course of the simulated pressure obtained is within the measurement error of the optical sensor. This error is 1% and reflects the hysteresis and nonlinearity of the sensor. The real indicated pressure measured in the cylinder and the pressure taken from the simulation were compared. It can be claimed that the verification of CFD simulations based on the pressure is a success. The next step was to research on the impact of changing the ignition advance timing of spark plugs 1 and 2 on a combustion process. Moving ignition timing between 1 and 2 spark plug results in a longer and uneven firing of a mixture. The most optimal point in terms of indicated power occurs when ignition is simultaneous for both spark plugs, but so severely separated ignitions are assured that ignition will occur at all speeds and loads of engine. It should be confirmed by a bench experiment of the engine. However, this simulation research enabled us to determine the optimal ignition advance angle to be implemented into the ignition control system. This knowledge allows us to set up the ignition point with two spark plugs to achieve as large power as possible.

Keywords: CFD model, combustion, engine, simulation

Procedia PDF Downloads 359