Search results for: radar detection

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

Procedia PDF Downloads 74

Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

Procedia PDF Downloads 96

Authors: Ali Bahkali, Rayan Yousef Booq, Mohammad Khiyami

Abstract:

Soil samples were collected from five different regions in the Kingdom of Saudi Arabia. Microbiological methods included dilution methods and pour plates to isolate and purify bacteria soil. The ability of isolates to develop biopolymer was investigated on petri dishes containing elements and substance concentrations stimulating developing biopolymer. Fluorescent stains, Nile red and Nile blue were used to stain the bacterial cells developing biopolymers. In addition, Sudan black was used to detect biopolymers in bacterial cells. The isolates which developed biopolymers were identified based on their gene sequence of 1 6sRNA and their ability to grow and synthesize PHAs on mineral medium supplemented with 1% dates molasses as the only carbon source under nitrogen limitation. During the study 293 bacterial isolates were isolated and detected. Through the initial survey on the petri dishes, 84 isolates showed the ability to develop biopolymers. These bacterial colonies developed a pink color due to accumulation of the biopolymers in the cells. Twenty-three isolates were able to grow on dates molasses, three strains of which showed the ability to accumulate biopolymers. These strains included Bacillus sp., Ralstonia sp. and Microbacterium sp. They were detected by Nile blue A stain with fluorescence microscopy (OLYMPUS IX 51). Among the isolated strains Ralstonia sp. was selected after its ability to grow on molasses dates in the presence of a limited nitrogen source was detected. The optimum conditions for formation of biopolymers by isolated strains were investigated. Conditions studied included, best incubation duration (2 days), temperature (30°C) and pH (7-8). The maximum PHB production was raised by 1% (v1v) when using concentrations of dates molasses 1, 2, 3, 4 and 5% in MSM. The best inoculated with 1% old inoculum (1= OD). The ideal extraction method of PHA and PHB proved to be 0.4% sodium hypochlorite solution, producing a quantity of polymer 98.79% of the cell's dry weight. The maximum PHB production was 1.79 g/L recorded by Ralstonia sp. after 48 h, while it was 1.40 g/L produced by R.eutropha ATCC 17697 after 48 h.

Keywords: bacteria forming polyhydroxyalkanoate, detection, molecular, Saudi Arabia

Procedia PDF Downloads 349

Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

Abstract:

The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

Procedia PDF Downloads 137

Authors: Jeremy Bost, Matthew Brett, Jacob Flynn, Weihui Li

Abstract:

One response during anaphylaxis is reduced blood pressure due to blood vessels relaxing and dilating. Epinephrine causes the blood vessels to constrict, which raises blood pressure to counteract the symptoms. When going through an allergic reaction, an Epinephrine injector is used to administer a shot of epinephrine intramuscularly. Epinephrine injectors have become an integral part of day-to-day life for people with allergies. Current Epinephrine injectors (EpiPen) are completely mechanical and have no sensors to monitor the vital signs of patients or give suggestions the optimal time for the shot. The EpiPens are also large and inconvenient to carry daily. The current price of an EpiPen is roughly 600$ for a pack of two. This makes carrying an EpiPen very expensive, especially when they need to be switched out when the epinephrine expires. This new design is in the form of a bracelet, which has the ability to inject epinephrine. The bracelet will be equipped with vital signs monitors that can aid the patient to sense the allergic reaction. The vital signs that would be of interest are blood pressure, heart rate and Electrodermal activity (EDA). The heart rate of the patient will be tracked by a photoplethysmograph (PPG) that is incorporated into the sensors. The heart rate is expected to increase during anaphylaxis. Blood pressure will be monitored through a radar sensor, which monitors the phase changes in electromagnetic waves as they reflect off of the blood vessel. EDA is under autonomic control. Allergen-induced anaphylaxis is caused by a release of chemical mediators from mast cells and basophils, thus changes the autonomic activity of the patient. So by measuring EDA, it will give the wearer an alert on how their autonomic nervous system is reacting. After the vital signs are collected, they will be sent to an application on a smartphone to be analyzed, which can then alert an emergency contact if the epinephrine injector on the bracelet is activated. Overall, this design creates a safer system by aiding the user in keeping track of their epinephrine injector, while making it easier to track their vital signs. Also, our design will be more affordable and more convenient to replace. Rather than replacing the entire product, only the needle and drug will be switched out and not the entire design.

Keywords: allergy, anaphylaxis, epinephrine, injector, vital signs monitor

Procedia PDF Downloads 253

Authors: Dhouibi Mohamed, Stirbu Bogdan, Chabotier André, Pirlot Marc

Abstract:

Effects of erosion and wear on the performance of small caliber guns have been analyzed throughout numerical and experimental studies. Mainly, qualitative observations were performed. Correlations between the volume change of the chamber and the maximum pressure are limited. This paper focuses on the development of a numerical model to predict the maximum pressure evolution when the interior shape of the chamber changes in the different weapon’s life phases. To fulfill this goal, an experimental campaign, followed by a numerical simulation study, is carried out. Two test barrels, « 5.56x45mm NATO » and « 7.62x51mm NATO,» are considered. First, a Coordinate Measuring Machine (CMM) with a contact scanning probe is used to measure the interior profile of the barrels after each 300-shots cycle until their worn out. Simultaneously, the EPVAT (Electronic Pressure Velocity and Action Time) method with a special WEIBEL radar are used to measure: (i) the chamber pressure, (ii) the action time, (iii) and the bullet velocity in each barrel. Second, a numerical simulation study is carried out. Thus, a coupled interior ballistic model is developed using the dynamic finite element program LS-DYNA. In this work, two different models are elaborated: (i) coupled Eularien Lagrangian method using fluid-structure interaction (FSI) techniques and a coupled thermo-mechanical finite element using a lumped parameter model (LPM) as a subroutine. Those numerical models are validated and checked through three experimental results, such as (i) the muzzle velocity, (ii) the chamber pressure, and (iii) the surface morphology of fired projectiles. Results show a good agreement between experiments and numerical simulations. Next, a comparison between the two models is conducted. The projectile motions, the dynamic engraving resistances and the maximum pressures are compared and analyzed. Finally, using this obtained database, a statistical correlation between the muzzle velocity, the maximum pressure and the chamber volume is established.

Keywords: engraving process, finite element analysis, gun barrel erosion, interior ballistics, statistical correlation

Procedia PDF Downloads 219

Authors: Tesfaye Mengistu

Abstract:

Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

Procedia PDF Downloads 115

Authors: Ifigeneia V. Mavragani, Zacharenia Nikitaki, George Kalantzis, George Iliakis, Alexandros G. Georgakilas

Abstract:

Complex DNA damage consisting of a combination of DNA lesions, such as Double Strand Breaks (DSBs) and non-DSB base lesions occurring in a small volume is considered as one of the most important biological endpoints regarding ionizing radiation (IR) exposure. Strong theoretical (Monte Carlo simulations) and experimental evidence suggests an increment of the complexity of DNA damage and therefore repair resistance with increasing linear energy transfer (LET). Experimental detection of complex (clustered) DNA damage is often associated with technical deficiencies limiting its measurement, especially in cellular or tissue systems. Our groups have recently made significant improvements towards the identification of key parameters relating to the efficient detection of complex DSBs and non-DSBs in human cellular systems exposed to IR of varying quality (γ-, X-rays 0.3-1 keV/μm, α-particles 116 keV/μm and 36Ar ions 270 keV/μm). The induction and processing of DSB and non-DSB-oxidative clusters were measured using adaptations of immunofluorescence (γH2AX or 53PB1 foci staining as DSB probes and human repair enzymes OGG1 or APE1 as probes for oxidized purines and abasic sites respectively). In the current study, Relative Biological Effectiveness (RBE) values for DSB and non-DSB induction have been measured in different human normal (FEP18-11-T1) and cancerous cell lines (MCF7, HepG2, A549, MO59K/J). The experimental results are compared to simulation data obtained using a validated microdosimetric fast Monte Carlo DNA Damage Simulation code (MCDS). Moreover, this simulation approach is implemented in two realistic clinical cases, i.e. prostate cancer treatment using X-rays generated by a linear accelerator and a pediatric osteosarcoma case using a 200.6 MeV proton pencil beam. RBE values for complex DNA damage induction are calculated for the tumor areas. These results reveal a disparity between theory and experiment and underline the necessity for implementing highly precise and more efficient experimental and simulation approaches.

Keywords: complex DNA damage, DNA damage simulation, protons, radiotherapy

Procedia PDF Downloads 327

Authors: Iván Castelló, Georgiana Stoica, Emilio Palomares, Fernando Bravo

Abstract:

We present herein two colour encoded silica nanospheres (2nanoSi) for the fluorescence quantitative ratiometric determination of trypsin in humans. The system proved to be a faster (minutes) method, with two times higher sensitivity than the state-of-the-art biomarkers based sensors for cystic fibrosis (CF), allowing the quantification of trypsin concentrations in a wide range (0-350 mg/L). Furthermore, as trypsin is directly related to the development of cystic fibrosis, different human genotypes, i.e. healthy homozygotic (> 80 mg/L), CF homozygotic (< 50 mg/L), and heterozygotic (> 50 mg/L), respectively, can be determined using our 2nanoSi nanospheres.

Keywords: cystic fibrosis, trypsin, quantum dots, biomarker, homozygote, heterozygote

Procedia PDF Downloads 487

Authors: R. L. Ujjwal, Abhishek Jain

Abstract:

In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

Procedia PDF Downloads 371

Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

Abstract:

Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

Procedia PDF Downloads 251

Authors: D. Heinemann, S. Schramm, S. Knabner, D. Baumgarten

Abstract:

Purpose: Applying solder paste to printed circuit boards (PCB) with stencils has been the method of choice over the past years. A new method uses a jet printer to deposit tiny droplets of solder paste through an ejector mechanism onto the board. This allows for more flexible PCB layouts with smaller components. Due to the viscosity of the solder paste, air blisters can be trapped in the cartridge. This can lead to missing solder joints or deviations in the applied solder volume. Therefore, a built-in and real-time inspection of the printing process is needed to minimize uncertainties and increase the efficiency of the process by immediate correction. The objective of the current study is the design of an optimal imaging system and the development of an automatic algorithm for the detection of applied solder joints from optical from the captured images. Methods: In a first approach, a camera module connected to a microcomputer and LED strips are employed to capture images of the printed circuit board under four different illuminations (white, red, green and blue). Subsequently, an improved system including a ring light, an objective lens, and a monochromatic camera was set up to acquire higher quality images. The obtained images can be divided into three main components: the PCB itself (i.e., the background), the reflections induced by unsoldered positions or screw holes and the solder joints. Non-uniform illumination is corrected by estimating the background using a morphological opening and subtraction from the input image. Image sharpening is applied in order to prevent error pixels in the subsequent segmentation. The intensity thresholds which divide the main components are obtained from the multimodal histogram using three probability density functions. Determining the intersections delivers proper thresholds for the segmentation. Remaining edge gradients produces small error areas which are removed by another morphological opening. For quantitative analysis of the segmentation results, the dice coefficient is used. Results: The obtained PCB images show a significant gradient in all RGB channels, resulting from ambient light. Using different lightings and color channels 12 images of a single PCB are available. A visual inspection and the investigation of 27 specific points show the best differentiation between those points using a red lighting and a green color channel. Estimating two thresholds from analyzing the multimodal histogram of the corrected images and using them for segmentation precisely extracts the solder joints. The comparison of the results to manually segmented images yield high sensitivity and specificity values. Analyzing the overall result delivers a Dice coefficient of 0.89 which varies for single object segmentations between 0.96 for a good segmented solder joints and 0.25 for single negative outliers. Conclusion: Our results demonstrate that the presented optical imaging system and the developed algorithm can robustly detect solder joints on printed circuit boards. Future work will comprise a modified lighting system which allows for more precise segmentation results using structure analysis.

Keywords: printed circuit board jet-printing, inspection, segmentation, solder paste detection

Procedia PDF Downloads 338

Authors: Young-Min Ko, Dong-Hyun Ha, Chang-Bin Ha, Hyoung-Kyu Song

Abstract:

In recent years, a multi-antenna system is actively used to improve the performance of the communication. A MIMO-OFDM system can provide multiplexing gain or diversity gain. These gains are obtained in proportion to the increase of the number of antennas. In order to provide the optimal gain of the MIMO-OFDM system, various transmission and reception schemes are presented. This paper aims to propose a hybrid scheme that base station provides both diversity gain and multiplexing gain at the same time.

Keywords: DFE, diversity gain, hybrid, MIMO, multiplexing gain.

Procedia PDF Downloads 688

Authors: Yiquan Liao, Quanhong Jiang

Abstract:

Since terahertz was observed by German scientists, we have obtained terahertz through different generation technologies of broadband and narrowband. Then, with the development of semiconductor and other technologies, the imaging technology of terahertz has become increasingly perfect. From the earliest application of nondestructive testing in aviation to the present application of information transmission and human safety detection, the role of terahertz will shine in various fields. The weapons produced by terahertz were epoch-making, which is a crushing deterrent against technologically backward countries. At the same time, terahertz technology in the fields of imaging, medical and livelihood, communication and communication are for the well-being of the country and the people.

Keywords: terahertz, imaging, communication, medical treatment

Procedia PDF Downloads 102

Authors: Mingqian Zhang

Abstract:

Tip-enhanced Raman spectroscopy (TERS) is a scanning probe technique for individual objects and structured surfaces investigation that provides a wealth of enhanced spectral information with nanoscale spatial resolution and high detection sensitivity. It has become a powerful and promising chemical and physical information detection method in the nanometer scale. The TERS technique uses a sharp metallic tip regulated in the near-field of a sample surface, which is illuminated with a certain incident beam meeting the excitation conditions of the wave-vector matching. The local electric field, and, consequently, the Raman scattering, from the sample in the vicinity of the tip apex are both greatly tip-enhanced owning to the excitation of localized surface plasmons and the lightning-rod effect. Typically, a TERS setup is composed of a scanning probe microscope, excitation and collection optical configurations, and a Raman spectroscope. In the illumination configuration, an objective lens or a parabolic mirror is always used as the most important component, in order to focus the incident beam on the tip apex for excitation. In this research, a novel TERS setup was built up by introducing a plasmonic lens to the excitation optics as a focusing device. A plasmonic lens with symmetry breaking semi-annular slits corrugated on gold film was designed for the purpose of generating concentrated sub-wavelength light spots with strong longitudinal electric field. Compared to conventional far-field optical components, the designed plasmonic lens not only focuses an incident beam to a sub-wavelength light spot, but also realizes a strong z-component that dominants the electric field illumination, which is ideal for the excitation of tip-enhancement. Therefore, using a PL in the illumination configuration of TERS contributes to improve the detection sensitivity by both reducing the far-field background and effectively exciting the localized electric field enhancement. The FDTD method was employed to investigate the optical near-field distribution resulting from the light-nanostructure interaction. And the optical field distribution was characterized using an scattering-type scanning near-field optical microscope to demonstrate the focusing performance of the lens. The experimental result is in agreement with the theoretically calculated one. It verifies the focusing performance of the plasmonic lens. The optical field distribution shows a bright elliptic spot in the lens center and several arc-like side-lobes on both sides. After the focusing performance was experimentally verified, the designed plasmonic lens was used as a focusing component in the excitation configuration of TERS setup to concentrate incident energy and generate a longitudinal optical field. A collimated linearly polarized laser beam, with along x-axis polarization, was incident from the bottom glass side on the plasmonic lens. The incident light focused by the plasmonic lens interacted with the silver-coated tip apex and enhanced the Raman signal of the sample locally. The scattered Raman signal was gathered by a parabolic mirror and detected with a Raman spectroscopy. Then, the plasmonic lens based setup was employed to investigate carbon nanotubes and TERS experiment was performed. Experimental results indicate that the Raman signal is considerably enhanced which proves that the novel TERS configuration is feasible and promising.

Keywords: longitudinal electric field, plasmonics, raman spectroscopy, tip-enhancement

Procedia PDF Downloads 376

Authors: M. Marchewka

Abstract:

The article is devoted to the possible optical transitions in double quantum wells system based on HgTe/HgCd(Mn)Te heterostructures. Such structures can find applications as detectors and sources of radiation in the terahertz range. The Double Quantum Wells (DQW) systems consist of two QWs separated by the transparent for electrons barrier. Such systems look promising from the point of view of the additional degrees of freedom. In the case of the topological insulator in about 6.4nm wide HgTe QW or strained 3D HgTe films at the interfaces, the topologically protected surface states appear at the interfaces/surfaces. Electrons in those edge states move along the interfaces/surfaces without backscattering due to time-reversal symmetry. Combination of the topological properties, which was already verified by the experimental way, together with the very well know properties of the DQWs, can be very interesting from the applications point of view, especially in the THz area. It is important that at the present stage, the technology makes it possible to create high-quality structures of this type, and intensive experimental and theoretical studies of their properties are already underway. The idea presented in this paper is based on the eight-band KP model, including the additional terms related to the structural inversion asymmetry, interfaces inversion asymmetry, the influence of the magnetically content, and the uniaxial strain describe the full pictures of the possible real structure. All of this term, together with the external electric field, can be sources of breaking symmetry in investigated materials. Using the 8 band KP model, we investigated the electronic shape structure with and without magnetic field from the application point of view as a THz detector in a small magnetic field (below 2T). We believe that such structures are the way to get the tunable topological insulators and the multilayer topological insulator. Using the one-dimensional electrons at the topologically protected interface states as fast and collision-free signal carriers as charge and signal carriers, the detection of the optical signal should be fast, which is very important in the high-resolution detection of signals in the THz range. The proposed engineering of the investigated structures is now one of the important steps on the way to get the proper structures with predicted properties.

Keywords: topological insulator, THz spectroscopy, KP model, II-VI compounds

Procedia PDF Downloads 124

Authors: Mouna Zitouni, Mariem Tounsi

Abstract:

This research introduces an application of the Riesz mixture model for medical image segmentation for accurate diagnosis and treatment of brain tumors. We propose a pixel classification technique based on the Riesz distribution, derived from an extended Bartlett decomposition. To our knowledge, this is the first study addressing this approach. The Expectation-Maximization algorithm is implemented for parameter estimation. A comparative analysis, using both synthetic and real brain images, demonstrates the superiority of the Riesz model over a recent method based on the Wishart distribution.

Keywords: EM algorithm, segmentation, Riesz probability distribution, Wishart probability distribution

Procedia PDF Downloads 22

Authors: Edna Gnomblerou

Abstract:

Given the current scale of corruption epidemic in West African economies, governments are seeking for immediate and effective measures to reduce the likelihood of the plague within the region. Generally, accountants and auditors are expected to help organizations in detecting illegal practices. However, their role in the fight against corruption is sometimes limited due to the collusive nature of corruption. The Denmark anti-corruption model shows that the implementation of additional controls over public accounts and independent efficient audits improve transparency and increase the probability of detection. This study is aimed at reviewing the existing anti-corruption policies of the Economic Commission of West African States (ECOWAS) as to observe the role attributed to accounting, auditing and other managerial practices in their anti-corruption drive. It further discusses the usefulness of accounting and auditing in helping anti-corruption commissions in controlling misconduct and increasing the perception to detect irregularities within public administration. The purpose of this initiative is to identify and assess the relevance of accounting and auditing in curbing corruption. To meet this purpose, the study was designed to answer the questions of whether accounting and auditing processes were included in the reviewed anti-corruption strategies, and if yes, whether they were effective in the detection process. A descriptive research method was adopted in examining the role of accounting and auditing in West African anti-corruption strategies. The analysis reveals that proper recognition of accounting standards and implementation of financial audits are viewed as strategic mechanisms in tackling corruption. Additionally, codes of conduct, whistle-blowing and information disclosure to the public are among the most common managerial practices used throughout anti-corruption policies to effectively and efficiently address the problem. These observations imply that sound anti-corruption strategies cannot ignore the values of including accounting and auditing processes. On one hand, this suggests that governments should employ all resources possible to improve accounting and auditing practices in the management of public sector organizations. On the other hand, governments must ensure that accounting and auditing practices are not limited to the private sector, but when properly implemented constitute crucial mechanisms to control and reduce corrupt incentives in public sector.

Keywords: accounting, anti-corruption strategy, auditing, ECOWAS

Procedia PDF Downloads 258

Authors: J. Silva, P. Arezes, R. Schierl, N. Costa

Abstract:

In order to study environmental contamination by cytostatic drugs in Portugal hospitals, sampling campaigns were conducted in three hospitals in 2015 (112 samples). Platinum containing drugs and fluorouracil were chosen because both were administered in high amounts. The detection limit was 0.01 pg/cm² for platinum and 0.1 pg/cm² for fluorouracil. The results show that spills occur mainly on the patient`s chair, while the most referenced occurrence is due to an inadequately closed wrapper. Day hospitals facilities were detected as having the largest number of contaminated samples and with higher levels of contamination.

Keywords: cytostatic, contamination, hospital, procedures, handling

Procedia PDF Downloads 297

Authors: Lev Bertovsky

Abstract:

The purpose of this study was to determine the reasons for the formation of false testimony from witnesses and make recommendations on the recognition of such cases. During the studies, which were based on the achievements of professionals in the field of psychology, as well as personal investigative practice, the stages of perception of the information were studied, as well as the process of its reclaim from the memory and transmission to the communicator upon request. Based on the principles of the human brain, kinds of conscientious witness mistakes were systematized. Proposals were formulated for the optimization of investigative actions in cases where the witnesses make an honest mistake with respect to the effects previously observed by them.

Keywords: criminology, eyewitness testimony, honest mistake, information, investigator, investigation, questioning

Procedia PDF Downloads 186

Authors: T. G. Pathrikar, A. D. Urhekar, M. P. Bansal

Abstract:

To find out S. aureus from patient samples on the basis of coagulase test. We have evaluated slide coagulase (n=46 positive), tube coagulase (n=48 positive) and DNase test (n=44, positive) , We have isolated and identified MRSA from various clinical samples and specimens by disc diffusion method determined the incidence of MRSA 50% in patients. Found out the in vitro antimicrobial susceptibility pattern of MRSA isolates and also the MIC of MRSA of oxacillin by E-Test.

Keywords: cefoxitin disc diffusion MRSA detection, e – test, S. aureus devastating pathogen, tube coagulase confirmation

Procedia PDF Downloads 494

Authors: Petr Slobodian, Pavel Riha, Jiri Matyas, Robert Olejnik, Nuri Karakurt

Abstract:

Thermoelectric devices generate an electrical current when there is a temperature gradient between the hot and cold junctions of two dissimilar conductive materials typically n-type and p-type semiconductors. Consequently, also the polymeric semiconductors composed of polymeric matrix filled by different forms of carbon nanotubes with proper structural hierarchy can have thermoelectric properties which temperature difference transfer into electricity. In spite of lower thermoelectric efficiency of polymeric thermoelectrics in terms of the figure of merit, the properties as stretchability, flexibility, lightweight, low thermal conductivity, easy processing, and low manufacturing cost are advantages in many technological and ecological applications. Polyethylene-octene copolymer based highly elastic composites filled with multi-walled carbon nanotubes (MWCTs) were prepared by sonication of nanotube dispersion in a copolymer solution followed by their precipitation pouring into non-solvent. The electronic properties of MWCNTs were moderated by different treatment techniques such as chemical oxidation, decoration by Ag clusters or addition of low molecular dopants. In this concept, for example, the amounts of oxygenated functional groups attached on MWCNT surface by HNO₃ oxidation increase p-type charge carriers. p-type of charge carriers can be further increased by doping with molecules of triphenylphosphine. For partial altering p-type MWCNTs into less p-type ones, Ag nanoparticles were deposited on MWCNT surface and then doped with 7,7,8,8-tetracyanoquino-dimethane. Both types of MWCNTs with the highest difference in generated thermoelectric power were combined to manufacture polymeric based thermoelectric module generating thermoelectric voltage when the temperature difference is applied between hot and cold ends of the module. Moreover, it was found that the generated voltage by the thermoelectric module at constant temperature gradient was significantly affected when exposed to vapors of different volatile organic compounds representing then a self-powered thermoelectric sensor for chemical vapor detection.

Keywords: carbon nanotubes, polymer composites, thermoelectric materials, self-powered gas sensor

Procedia PDF Downloads 155

Authors: Victor Bloch, Eerikki Kaila, Reetta Palva

Abstract:

Field robots are an important tool for enhancing efficiency and decreasing the climatic impact of food production. There exists a number of commercial field robots; however, since this technology is still new, the robot advantages and limitations, as well as methods for optimal using of robots, are still unclear. In this study, the performance of a commercial field robot for seeding and weeding was assessed. A research 2-ha sugar beet field with 0.5m row width was used for testing, which included robotic sowing of sugar beet and weeding five times during the first two months of the growing. About three and five percent of the field were used as untreated and chemically weeded control areas, respectively. The plant detection was based on the exact plant location without image processing. The robot was equipped with six seeding and weeding tools, including passive between-rows harrow hoes and active hoes cutting inside rows between the plants, and it moved with a maximal speed of 0.9 km/h. The robot's performance was assessed by image processing. The field images were collected by an action camera with a height of 2 m and a resolution 27M pixels installed on the robot and by a drone with a 16M pixel camera flying at 4 m height. To detect plants and weeds, the YOLO model was trained with transfer learning from two available datasets. A preliminary analysis of the entire field showed that in the areas treated by the robot, the weed average density varied across the field from 6.8 to 9.1 weeds/m² (compared with 0.8 in the chemically treated area and 24.3 in the untreated area), the weed average density inside rows was 2.0-2.9 weeds / m (compared with 0 on the chemically treated area), and the emergence rate was 90-95%. The information about the robot's performance has high importance for the application of robotics for field tasks. With the help of the developed method, the performance can be assessed several times during the growth according to the robotic weeding frequency. When it’s used by farmers, they can know the field condition and efficiency of the robotic treatment all over the field. Farmers and researchers could develop optimal strategies for using the robot, such as seeding and weeding timing, robot settings, and plant and field parameters and geometry. The robot producers can have quantitative information from an actual working environment and improve the robots accordingly.

Keywords: agricultural robot, field robot, plant detection, robot performance

Procedia PDF Downloads 89

Authors: Shveta Bathla, Preeti Rawat, Sudarshan Kumar, Rubina Baithalu, Jogender Singh Rana, Tushar Kumar Mohanty, Ashok Kumar Mohanty

Abstract:

Pregnancy is a complex process which includes series of events such as fertilization, formation of blastocyst, implantation of embryo, placental formation and development of fetus. The success of these events depends on various interactions which are synchronized by endocrine interaction between a receptive dam and competent embryo. These interactions lead to change in expression of hormones and proteins. But till date no protein biomarker is available which can be used to detect successful completion of these events. We employed quantitative proteomics approach to develop putative serological biomarker which has diagnostic applicability for early detection of pregnancy in cows. For this study, sera were collected from control (non-pregnant, n=6) and pregnant animals on successive days of pregnancy (7, 19, 45, n=6). The sera were subjected to depletion for removal of albumin using Norgen depletion kit. The tryptic peptides were labeled with iTRAQ. The peptides were pooled and fractionated using bRPLC over 80 min gradient. Then 12 fractions were injected to nLC for identification and quantitation in DDA mode using ESI. Identification using Mascot search revealed 2056 proteins out of which 352 proteins were differentially expressed. Twenty proteins were upregulated and twelve proteins were down-regulated with fold change > 1.5 and < 0.6 respectively (p < 0.05). The gene ontology studies of DEPs using Panther software revealed that majority of proteins are actively involved in catalytic activities, binding and enzyme regulatory activities. The DEP'S such as NF2, MAPK, GRIPI, UGT1A1, PARP, CD68 were further subjected to pathway analysis using KEGG and Cytoscape plugin Cluego that showed involvement of proteins in successful implantation, maintenance of pluripotency, regulation of luteal function, differentiation of endometrial macrophages, protection from oxidative stress and developmental pathways such as Hippo. Further efforts are continuing for targeted proteomics, western blot to validate potential biomarkers and development of diagnostic kit for early pregnancy diagnosis in cows.

Keywords: bRPLC, Cluego, ESI, iTRAQ, KEGG, Panther

Procedia PDF Downloads 463

Authors: Somaya A. Abd El Mottaleb, Ahmed Abd El Aziz, Heba A. Fayed, Moustafa H. Aly

Abstract:

In this paper, we present the performance of spectral amplitude coding optical code division multiple access using different detectors at different transmission distances using single photodiode detection technique. Modified double weight codes are used as signature codes. Simulation results show that the system using avalanche photo detector can move distance longer than that using positive intrinsic negative photo detector.

Keywords: avalanche photodiode, modified double weight, multiple access technique, single photodiode.

Procedia PDF Downloads 608

Authors: Amarjit Singh

Abstract:

In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos.

Keywords: video, subtitles, extraction, annotation, frames

Procedia PDF Downloads 605

Authors: Andrey Vinajera-Zamora, Norge Coello-Machado, Elke Glistau

Abstract:

The objective of this research is calculate the added value in operations of electric power meters (EPM) supply chains, specifically the EPM of 220v. The tool used is composed by six steps allowing at same time the identification of calibration of EPM as the bottleneck operation according the net added value being at same time the process of higher added value. On the other hand, this methodology allows calculate the amount of money to buy the raw material. The main conclusions are related to the analyze ‘s way and calculating of added value in supply chain integrated by the echelons procurement, production and distribution or any of these.

Keywords: economic value added, supply chain management, value chain, bottleneck detection

Procedia PDF Downloads 298

Authors: Ambika Selvaraj

Abstract:

Magnetic iron oxide nanoparticles (IO) of < 20nm (superparamagnetic) become promising tool in cancer therapy, and integrated nanodevices for cancer detection and screening. The obstacles include particle heterogeneity and cost. It can be overcome by developing monodispersed nanoparticles in economical approach. We have successfully synthesized < 7 nm IO by low temperature controlled technique, in which Fe0 is sandwiched between stabilizer and Fe2+. Size analysis showed the excellent size control from 31 nm at 33°C to 6.8 nm at 10°C. Resultant monodispersed IO were found to be stable for > 50 reuses, proved its applicability in biomedical applications.

Keywords: low temperature synthesis, hybrid iron nanoparticles, cancer therapy, biomedical applications

Procedia PDF Downloads 346

Authors: Man-Yun Liu, Emily Chia-Yu Su

Abstract:

Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

Procedia PDF Downloads 326

Authors: Akshay Suhas Phalke, Manohar S. Chaudhari

Abstract:

In this paper, we have discussed the multipath routing with its variants. Our purpose is to discuss the different types of the multipath routing mechanism. Here we also put the taxonomy of the multipath routing. Multipath routing is used for the alternate path routing, reliable transmission of data and for better utilization of network resources. We also discussed the multipath routing for topology hiding such as TOHIP. In multipath routing, different parameters such as energy efficiency, packet delivery ratio, shortest path routing, fault tolerance play an important role. We have discussed a number of multipath routing protocol based on different parameters lastly.

Keywords: multi-path routing, WSN, topology, fault detection, trust

Procedia PDF Downloads 356