Search results for: high relative accuracy

Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan

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Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.

Keywords: ADAS, home zone parking pilot, object detection, visual SLAM

Procedia PDF Downloads 67

Authors: John Farr Rothrock

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Although migraine traditionally has been assumed to convey a relative decrease in libido, sexual activity and sexual performance, recent data have suggested that the female migraine population is far from homogenous in this regard. We sought to determine the levels of libido, sexual activity and sexual performance in the female migraine patient population both generally and according to clinical phenotype. In this single-blind study, a consecutive series of sexually active new female patients ages 25-55 initially presenting to a university-based headache clinic and having a >1 year history of migraine were asked to complete anonymously a survey assessing their sexual histories generally and as they related to their headache disorder and the 19-item Female Sexual Function Index (FSFI). To serve as 2 separate control groups, 100 sexually active females with no history of migraine and 100 female migraineurs from the general (non-clinic) population but matched for age, marital status, educational background and socioeconomic status completed a similar survey. Over a period of 3 months, 188 consecutive migraine patients were invited to participate. Twenty declined, and 28 of the remaining 160 potential subjects failed to meet the inclusion criterion utilized for “sexually active” (ie, heterosexual intercourse at a frequency of > once per month in each of the preceding 6 months). In all groups younger age (p<.005), higher educational level attained (p<.05) and higher socioeconomic status (p<.025) correlated with a higher monthly frequency of intercourse and a higher likelihood of intercourse resulting in orgasm. Relative to the 100 control subjects with no history of migraine, the two migraine groups (total n=232) reported a lower monthly frequency of intercourse and recorded a lower FSFI score (both p<.025), but the contribution to this difference came primarily from the chronic migraine (CM) subgroup (n=92). Patients with low frequency episodic migraine (LFEM) and mid frequency episodic migraine (MFEM) reported a higher FSFI score, higher monthly frequency of intercourse, higher likelihood of intercourse resulting in orgasm and higher likelihood of multiple active sex partners than controls. All migraine subgroups reported a decreased likelihood of engaging in intercourse during an active migraine attack, but relative to the CM subgroup (8/92=9%), a higher proportion of patients in the LFEM (12/49=25%), MFEM (14/67=21%) and high frequency episodic migraine (HFEM: 6/14=43%) subgroups reported utilizing intercourse - and orgasm specifically - as a means of potentially terminating a migraine attack. In the clinic vs no-clinic groups there were no significant differences in the dependent variables assessed. Research subjects with LFEM and MFEM may report a level of libido, frequency of intercourse and likelihood of orgasm-associated intercourse that exceeds what is reported by age-matched controls free of migraine. Many patients with LFEM, MFEM and HFEM appear to utilize intercourse/orgasm as a means to potentially terminate an acute migraine attack.

Keywords: migraine, female, libido, sexual activity, phenotype

Procedia PDF Downloads 77

Authors: Sonu Gandhi, Neena Capalash, Prince Sharma, C. Raman Suri

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A sensitive chemiluminescence immunoassay (CIA) for heroin and its major metabolites is reported. The method is based on the competitive reaction of horseradish peroxidase (HRP)-labeled anti-MAM antibody and free drug in spiked urine samples. A hapten-protein conjugate was synthesized by using acidic derivative of monoacetyl morphine (MAM) coupled to carrier protein BSA and was used as an immunogen for the generation of anti-MAM (monoacetyl morphine) antibody. A high titer of antibody (1:64,0000) was obtained and the relative affinity constant (Kaff) of antibody was 3.1×107 l/mol. Under the optimal conditions, linear range and reactivity for heroin, mono acetyl morphine (MAM), morphine and codeine were 0.08, 0.09, 0.095 and 0.092 ng/mL respectively. The developed chemiluminescence inhibition assay could detect heroin and its metabolites in standard and urine samples up to 0.01 ng/ml.

Keywords: heroin, metabolites, chemiluminescence immunoassay, horse radish peroxidase

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Authors: H. Vahdani

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By the adsorption of polar compounds and/or the deposition of organic material, the wettability of originally water-wet reservoir rock can be altered. The degree of alteration is determined by the interaction of the oil constituents, the mineral surface, and the brine chemistry. Recently improving oil recovery by tuning wettability alteration is believed as a new recovery method. Various researchers have demonstrated that low salt water injection has a significant impact on oil recovery. It has been shown, for instance, that additional oil can be produced from reservoir rock by managing the injection water. Large wettability sensitivity has been observed, indicating that the oil/water capillary pressure profiles play a major role during low saline water injection simulation. Although the exact physics on how this alteration occurs is still a research topic; however, it has been reported that some of its effect can be captured by a relative permeability shift from an oil-wet system to a water-wet system. Modeling of low salt water injection mainly is based on the theory of wettability alteration and is hence strongly dependent on the wettability of the reservoir. In this article, combination of different wettabilities has been simulated and it is observed that the highest recoveries were from the cases were the reservoir initially was water-wet, and the lowest recoveries was from the cases were the reservoir initially was considered oil-wet. However for the cases where the reservoir initially was oil-wet, the effect of low-salinity waterflooding was the largest.

Keywords: low salt water injection, wettability alteration, modelling, relative permeability

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Authors: J. Constanzo, B. Paquette, G. Charest, L. Masson-Côté, M. Guillot

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Targeted and whole-brain irradiation in humans can result in significant side effects causing decreased patient quality of life. To adequately investigate structural and functional alterations after stereotactic radiosurgery, preclinical studies are needed. The first step is to establish a robust standardized method of targeted irradiation on small regions of the rat brain. Eleven euthanized male Fischer rats were imaged in a stereotactic bed, by computed tomographic (CT), to estimate positioning variations regarding to the bregma skull reference point. Using a rat brain atlas and the stereotactic bregma coordinates assessed from CT images, various regions of the brain were delimited and a treatment plan was generated. A dose of 37 Gy at 30% isodose which corresponds to 100 Gy in 100% of the target volume (X = 98.1; Y = 109.1; Z = 100.0) was set by Leksell Gamma Plan using sectors number 4, 5, 7, and 8 of the Gamma Knife unit with the 4-mm diameter collimators. Effects of positioning accuracy of the rat brain on the dose deposition were simulated by Gamma Plan and validated with dosimetric measurements. Our results showed that 90% of the target volume received 110 ± 4.7 Gy and the maximum of deposited dose was 124 ± 0.6 Gy, which corresponds to an excellent relative standard deviation of 0.5%. This dose deposition calculated with the Gamma Plan was validated with the dosimetric films resulting in a dose-profile agreement within 2%, both in X- and Z-axis,. Our results demonstrate the feasibility to standardize the irradiation procedure of a small volume in the rat brain using a Gamma Knife.

Keywords: brain irradiation, dosimetry, gamma knife, small-animal irradiation, stereotactic radiosurgery (SRS)

Procedia PDF Downloads 407

Authors: X. Cervantes-López, C. Arriaga-Canon, L. Contreras Espinosa

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The goal of this study is to validate the overexpression of the lncRNA GATA3-AS1 associated with resistance to neoadjuvant chemotherapy of female patients with locally advanced mammary adenocarcinoma of luminal B subtype This study involved a cohort of one hundred thirty-seven samples for which total RNA was isolated from formalin fixed paraffin embedded (FFPE) tissue. Samples were cut using a Microtome Hyrax M25 Zeiss and RNA was isolated using the RNeasy FFPE kit and a deparaffinization solution, the next step consisted in the analysis of RNA concentration and quality, then 18 µg of RNA was treated with DNase I, and cDNA was synthesized from 50 ng total RNA, finally real-time PCR was performed with SYBR Green/ROX qPCR Master Mix in order to determined relative gene expression using RPS28 as a housekeeping gene to normalize in a fold calculation ΔCt. As a result, we validated by real-time PCR that the overexpression of the lncRNA GATA3-AS1 is associated with resistance to neoadjuvant chemotherapy in locally advanced breast cancer patients of luminal B subtype.

Keywords: breast cancer, biomarkers, genomics, neoadjuvant chemotherapy, lncRNAS

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Chen Jun-Hong, He Pu, Tao Wen-Quan

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Proton exchange membrane fuel cell (PEMFC) is the most promising future energy source owing to its low operating temperature, high energy efficiency, high power density, and environmental friendliness. In this paper, a comprehensive PEMFC system control-oriented model is developed in the Matlab/Simulink environment, which includes the hydrogen supply subsystem, air supply subsystem, and thermal management subsystem. Besides, Improved Artificial Bee Colony (IABC) is used in the parameter identification of PEMFC semi-empirical equations, making the maximum relative error between simulation data and the experimental data less than 0.4%. Operation temperature is essential for PEMFC, both high and low temperatures are disadvantageous. In the thermal management subsystem, water pump and fan are both controlled with the PID controller to maintain the appreciate operation temperature of PEMFC for the requirements of safe and efficient operation. To improve the control effect further, fuzzy control is introduced to optimize the PID controller of the pump, and the Radial Basis Function (RBF) neural network is introduced to optimize the PID controller of the fan. The results demonstrate that Fuzzy-PID and RBF-PID can achieve a better control effect with 22.66% decrease in Integral Absolute Error Criterion (IAE) of T_st (Temperature of PEMFC) and 77.56% decrease in IAE of T_in (Temperature of inlet cooling water) compared with traditional PID. In the end, a novel thermal management structure is proposed, which uses the cooling air passing through the main radiator to continue cooling the secondary radiator. In this thermal management structure, the parasitic power dissipation can be reduced by 69.94%, and the control effect can be improved with a 52.88% decrease in IAE of T_in under the same controller.

Keywords: PEMFC system, parameter identification, temperature control, Fuzzy-PID, RBF-PID, parasitic power

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Authors: Sidney A. Lima, Hermann J. H. Kux, Elcio H. Shiguemori

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The Unmanned Aircraft Systems (UAS) usually navigate through the Global Navigation Satellite System (GNSS) associated with an Inertial Navigation System (INS). However, GNSS can have its accuracy degraded at any time or even turn off the signal of GNSS. In addition, there is the possibility of malicious interferences, known as jamming. Therefore, the image navigation system can solve the autonomy problem, because if the GNSS is disabled or degraded, the image navigation system would continue to provide coordinate information for the INS, allowing the autonomy of the system. This work aims to evaluate the accuracy of the positioning though photogrammetry concepts. The methodology uses orthophotos and Digital Surface Models (DSM) as a reference to represent the object space and photograph obtained during the flight to represent the image space. For the calculation of the coordinates of the perspective center and camera attitudes, it is necessary to know the coordinates of homologous points in the object space (orthophoto coordinates and DSM altitude) and image space (column and line of the photograph). So if it is possible to automatically identify in real time the homologous points the coordinates and attitudes can be calculated whit their respective accuracies. With the methodology applied in this work, it is possible to verify maximum errors in the order of 0.5 m in the positioning and 0.6º in the attitude of the camera, so the navigation through the image can reach values equal to or higher than the GNSS receivers without differential correction. Therefore, navigating through the image is a good alternative to enable autonomous navigation.

Keywords: autonomy, navigation, security, photogrammetry, remote sensing, spatial resection, UAS

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Authors: R. Stevens, W.L. Bryden

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The objective of this study was to determine the efficacy of zeolite in preventing the adverse effects of aflatoxin B1 (AFB1) in broilers. A total of 540 one-day-old Ross chicks were randomly divided into nine treatments, with four replicate pens per treatment and 15 chicks per pen. The treatments included 3 Levels of AFB1 (0,1and 2 mg/kg diet) and 3 levels of zeolite (0, 1.5 and 3 %) in a 3 ×3 factorial arrangement. The experimental treatments commenced on d 7 post-hatch. A starter diet was provided from d 1 to 14, a grower diet from d 15 to 28 and a finisher diet from d 29 to d 49. Diets were based on corn and soybeans and formulated to meet the bird's requirements. The evaluated parameters were as follows: Bodyweight, daily gain, feed intake (FI), feed conversion (FC), relative weights of organs (carcass, liver, heart and abdominal fat) and clinical biochemistry parameters: alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Bodyweight, daily gain and FC were significantly (P<0.05) impaired by aflatoxin. Relative weights of the liver and heart were also affected. The addition of zeolite (1.5 and 3 %) to the contaminated diets ameliorated the effects of aflatoxin, especially at the higher level of inclusion. These data demonstrate that this specific sorbent (zeolite) can protect against the toxicity of AFB1in young broiler chicks.

Keywords: aflatoxin, broiler, toxicity, zeolite

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Authors: Fahad Alanazi, Andrew Jones

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Digital forensics seeks to achieve the successful investigation of digital crimes through obtaining acceptable evidence from digital devices that can be presented in a court of law. Thus, the digital forensics investigation is normally performed through a number of phases in order to achieve the required level of accuracy in the investigation processes. Since 1984 there have been a number of models and frameworks developed to support the digital investigation processes. In this paper, we review a number of the investigation processes that have been produced throughout the years and introduce a proposed digital forensic model which is based on the scope of the Saudi Arabia investigation process. The proposed model has been integrated with existing models for the investigation processes and produced a new phase to deal with a situation where there is initially insufficient evidence.

Keywords: digital forensics, process, metadata, Traceback, Sauid Arabia

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Authors: Sohaib Z. Khan, Farrukh Mustahsan, Essam R. I. Mahmoud, S. H. Masood

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Materials or structures that contract laterally on the application of a compressive load and vice versa are said to be Auxetic materials which exhibit Negative Poisson’s Ratio (NPR). Numerous auxetic structures are proposed in the literature. One of the most studied periodic auxetic structure is the re-entrant honeycomb model. In this paper, a modified re-entrant model is proposed to enhance the auxetic behavior. The paper aimed to investigate the elastic behaviour of the proposed model to improve Young’s modulus and NPR by evaluating the analytical model. Finite Element Analysis (FEA) is also conducted to support the analytical results. A significant increment in Young’s modulus and NPR can be achieved in one of the two orthogonal directions of the loading at the cost of compromising these values in other direction. The proposed modification resulted in lower relative densities when compared to the existing re-entrant honeycomb structure. A trade-off in the elastic properties in one direction at low relative density makes the proposed model suitable for uni-direction applications where higher stiffness and NPR is required, and strength to weight ratio is important.

Keywords: 2D model, auxetic materials, re-entrant honeycomb, negative Poisson's ratio

Procedia PDF Downloads 139

Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

Procedia PDF Downloads 307

Authors: Mohamed Hassan Gundu, Jaeseung Lee, Muhammad Faizan Chinannai, Hyunchul Ju

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In this study, we present the effects of bipolar plate design to control the temperature of the cell and ensure effective water management under an excessive amount of air flow and low humidification conditions in the proton exchange membrane fuel cell (PEMFC). The PEMFC model developed and applied to consider a three type of bipolar plate that is defined by ratio of inlet channel width to outlet channel width. Simulation results show that the design which has narrow gas inlet channel and wide gas outlet channel width (wide coolant inlet channel and narrow coolant outlet channel width) make the relative humidity and water concentration increase in the channel and the catalyst layer. Therefore, this study clearly demonstrates that the dehydration phenomenon can be decreased by using design of bipolar plate with narrow gas inlet channel and wide gas outlet channel width (wide coolant inlet channel and narrow coolant outlet channel width).

Keywords: PEMFC, air-cooling, relative humidity, water management, water concentration, oxygen concentration

Procedia PDF Downloads 296

Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: Anjalika Fernando, Banuka Athuraliya

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This paper presents a comprehensive approach for converting Sinhala Sign Language (SSL) into grammatically correct sentences using Natural Language Processing (NLP) techniques in real-time. While previous studies have explored various aspects of SSL translation, the research gap lies in the absence of grammar checking for SSL. This work aims to bridge this gap by proposing a two-stage methodology that leverages deep learning models to detect signs and translate them into coherent sentences, ensuring grammatical accuracy. The first stage of the approach involves the utilization of a Long Short-Term Memory (LSTM) deep learning model to recognize and interpret SSL signs. By training the LSTM model on a dataset of SSL gestures, it learns to accurately classify and translate these signs into textual representations. The LSTM model achieves a commendable accuracy rate of 94%, demonstrating its effectiveness in accurately recognizing and translating SSL gestures. Building upon the successful recognition and translation of SSL signs, the second stage of the methodology focuses on improving the grammatical correctness of the translated sentences. The project employs a Neural Machine Translation (NMT) architecture, consisting of an encoder and decoder with LSTM components, to enhance the syntactical structure of the generated sentences. By training the NMT model on a parallel corpus of Sinhala wrong sentences and their corresponding grammatically correct translations, it learns to generate coherent and grammatically accurate sentences. The NMT model achieves an impressive accuracy rate of 98%, affirming its capability to produce linguistically sound translations. The proposed approach offers significant contributions to the field of SSL translation and grammar correction. Addressing the critical issue of grammar checking, it enhances the usability and reliability of SSL translation systems, facilitating effective communication between hearing-impaired and non-sign language users. Furthermore, the integration of deep learning techniques, such as LSTM and NMT, ensures the accuracy and robustness of the translation process. This research holds great potential for practical applications, including educational platforms, accessibility tools, and communication aids for the hearing-impaired. Furthermore, it lays the foundation for future advancements in SSL translation systems, fostering inclusive and equal opportunities for the deaf community. Future work includes expanding the existing datasets to further improve the accuracy and generalization of the SSL translation system. Additionally, the development of a dedicated mobile application would enhance the accessibility and convenience of SSL translation on handheld devices. Furthermore, efforts will be made to enhance the current application for educational purposes, enabling individuals to learn and practice SSL more effectively. Another area of future exploration involves enabling two-way communication, allowing seamless interaction between sign-language users and non-sign-language users.In conclusion, this paper presents a novel approach for converting Sinhala Sign Language gestures into grammatically correct sentences using NLP techniques in real time. The two-stage methodology, comprising an LSTM model for sign detection and translation and an NMT model for grammar correction, achieves high accuracy rates of 94% and 98%, respectively. By addressing the lack of grammar checking in existing SSL translation research, this work contributes significantly to the development of more accurate and reliable SSL translation systems, thereby fostering effective communication and inclusivity for the hearing-impaired community

Keywords: Sinhala sign language, sign Language, NLP, LSTM, NMT

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Authors: Bo Wang

Abstract:

The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

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Authors: Aliakbar Hafezi, Jalal Taherian, Jamshid Abedi, Mahsa Elahi

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Background: Bones are commonly affected by renal cell carcinoma (RCC) (primarily or secondary), and this condition causes bone fragility. The aim of this study was to evaluate the diagnostic value of noninvasive methods for the diagnosis of ROD in RCC patients on hemodialysis (HD) in northern Iran. Methods: In this cross-sectional study, 50 RCC patients with ESRD referred to dialysis units in northern Iran during 2021-2024 were randomly selected and investigated. The biochemical and radiographic evaluation of ROD and its subtypes was performed, and then all patients underwent bone biopsy and histopathological study, and finally, the diagnostic value of the noninvasive methods was assessed. Results: The mean age of patients was 58.9 ± 11.7 years, and 27 cases (54.0%) were female. 38 (76.0%) of RCC patients with ESRD had ROD, and 12 patients (24.0%) had no evidence of bone disorders. The sensitivity, specificity, positive and predictive values and accuracy of the noninvasive methods for the diagnosis of ROD were 92%, 82%, 95%, 75% and 90%, respectively. Conclusion: This study showed that the frequency of ROD in RCC patients with ESRD in northern Iran was high and the biochemical and radiographic markers have a high diagnostic value for ROD as well as histopathological assessment.

Keywords: renal cell carcinoma, renal osteodystrophy, hemodialysis, non-metastatic

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Authors: Saman Ghaffarian, Ilgin Gökaşar

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This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

Procedia PDF Downloads 379

Authors: Cher Rong Matthew Kong, Edmund Tian, Seng Poon Ong, Chee Sian Gan

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Gamma-aminobutyric acid (GABA) is a non-protein amino acid that is a functional constituent of certain rice varieties. When consumed, it decreases blood pressure and reduces the risk of hypertension-related diseases. This has led to more research dedicated towards the development of functional food products (e.g. germinated brown rice) with enhanced GABA content, and the development of these functional food products has led to increased demand for instrument-based methods that can efficiently and effectively determine GABA content. Current analytical methods require analyte derivatisation, and have significant disadvantages such as being labour intensive and time-consuming, and being subject to analyte loss due to the increased complexity of the sample preparation process. To address this, an LC-MS-MS method for the determination of GABA in rice products has been developed and validated. This developed method involves a relatively simple sample preparation process before analysis using HILIC LC-MS-MS. This method eliminates the need for derivatisation, thereby significantly reducing the labour and time associated with such an analysis. Using LC-MS-MS also allows for better differentiation of GABA from any potential co-eluting compounds in the sample matrix. Results obtained from the developed method demonstrated high linearity, accuracy, and precision for the determination of GABA (1ng/L to 8ng/L) in a variety of brown rice products. The method can significantly simplify sample preparation steps, improve the accuracy of quantitation, and increase the throughput of analyses, thereby providing a quick but effective alternative to established instrumental analysis methods for GABA in rice.

Keywords: functional food, gamma-aminobutyric acid, germinated brown rice, method development

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Authors: Jie Liu, Wei Wei, Chun-Ping Chang

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Using a sample of 110 countries over the period 1984-2013, this paper examines the impacts of country risks on choosing a specific exchange rate regime (first by utilizing the Levy-Yeyati and Sturzenegger de facto classification and then robusting it by the IMF de jure measurement) relative to other regimes via the panel multinomial logit approach. Empirical findings are as follows. First, in the full samples case we provide evidence that government is more likely to implement a flexible regime, but less likely to adopt a fixed regime, under a low level of composite and financial risk. Second, we find that Eurozone countries are more likely to choose a fixed exchange rate regime with a decrease in the level of country risk and favor a flexible regime in response to a shock from an increase of risk, which is opposite to non-Eurozone countries. Third, we note that high-risk countries are more likely to choose a fixed regime with a low level of composite and political risk in the government, but do not adjust the exchange rate regime as a shock absorber when facing economic and financial risks. It is interesting to see that those countries with relatively low risk display almost opposite results versus high-risk economies. Overall, we believe that it is critically important to account for political economy variables in a government’s exchange rate policy decisions, especially for country risks. All results are robust to the panel ordered probit model.

Keywords: country risk, political economy, exchange rate regimes, shock absorber

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Authors: Jali Octariady, Ida Herliningsih, Ade K. Mulyana, Annisa Fitria, Diaz C. K. Yuwana

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Awareness of the importance large-scale maps for development of a country is growing in all walks of life, especially for governments in Indonesia. Various parties, especially local governments throughout Indonesia demanded for immediate availability the large-scale maps of 1:5000 for regional development. But in fact, the large-scale maps of 1:5000 is only available less than 5% of the entire territory of Indonesia. Unavailability precise GCP at the entire territory of Indonesia is one of causes of slow availability the large scale maps of 1:5000. This research was conducted to find an alternative solution to this problem. This study was conducted to assess the accuracy of ArcGIS base maps coordinate when it shall be used as GCP for creating a map scale of 1:5000. The study was conducted by comparing the GCP coordinate from Field survey using GPS Geodetic than the coordinate from ArcGIS basemaps in various locations in Indonesia. Some areas are used as a study area are Lombok Island, Kupang City, Surabaya City and Kediri District. The differences value of the coordinates serve as the basis for assessing the accuracy of ArcGIS basemaps coordinates. The results of the study at various study area show the variation of the amount of the coordinates value given. Differences coordinate value in the range of millimeters (mm) to meters (m) in the entire study area. This is shown the inconsistency quality of ArcGIS base maps coordinates. This inconsistency shows that the coordinate value from ArcGIS Basemaps is careless. The Careless coordinate from ArcGIS Basemaps indicates that its cannot be used as GCP for large-scale mapping on the entire territory of Indonesia.

Keywords: accuracy, ArcGIS base maps, GCP, large scale maps

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Authors: Suriya

Abstract:

Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

Abstract:

Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

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Authors: Razan Salem

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Gender plays a vital role in the stock markets because men and women differ in their behavior when investing in stocks. Accordingly, the role of gender differences in investment behavior is an increasingly important strand in the field of behavioral finance research. The investment behaviors of women relative to men have been examined in the behavioral finance literature, mainly for comparison purposes. Women's roles in the stock market have not been examined in the behavioral finance literature, however, particularly with respect to the Arab region. This study aims to contribute towards a better understanding of the investment behavior of Arab women (in regards to their risk tolerance, investment confidence, and investment literacy levels) relative to Arab men; using a sample from Arab women and men investors living in Saudi Arabia and Jordan. In order to achieve the study's main aim, the researcher used non-parametric tests, as Mann-Whitney U test, along with frequency distribution analysis to analyze the study’s primary data. The researcher distributed close-ended online questionnaires to a sample of 550 Arab male and female individuals investing in stocks in both Saudi Arabia and Jordan. The results confirm that the sample Arab women invest less in stocks compared to Arab men due to their risk-averse behaviors and limited confidence levels. The results also reveal that due to Arab women’s very low investment literacy levels, they fear from taking the risk and invest often in stocks relative to Arab men. Overall, the study’s main variables (risk tolerance, investment confidence, and investment literacy levels) have a combined effect on the investment behavior of Arab women and their limited participation in the stock market. Hence, this study is one of the very first studies that indicate the combined effect of the three main variables (which are usually studied separately in the existing literature) on the investment behavior of women, particularly Arab women. This study makes three important contributions to the growing literature on gender differences in investment behavior. First, while the behavioral finance literature documents evidence on gender differences in investment behaviors in many developed countries, there are very limited studies that investigate such differences in Arab countries. Arab women investors, generally, are ignored from the behavioral finance literature due probably to cultural barriers and data collection difficulties. Thus, this study extends the literature to include Arab women and their investment behaviors when trading stock relative to Arab men. Moreover, the study associates women investment literacy and confidence levels with their financial risk behaviors and participation in the stock market. This study provides direct evidence on Arab women's investment behaviors when trading stocks. Overall, studying Arab women investors is important to investigate whether the investment behavior identified for Western women investors are also found in Arab women investors.

Keywords: Arab women, gender differences, investment behavior, stock markets

Procedia PDF Downloads 181

Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

Procedia PDF Downloads 575

Authors: Yael Wolff Sagy, Yiska Loewenberg Weisband, Vered Kaufman Shriqui, Michal Krieger, Arie Ben Yehuda, Ronit Calderon Margalit

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Background: Obesity is both a risk factor and common comorbidity of diabetes. Obesity impedes the achievement of glycemic control, and enhances damage caused by hyperglycemia to blood vessels; thus it increases diabetes-related complications. This study assessed the prevalence of obesity and morbid obesity among Israeli adults with diabetes, and estimated disparities associated with sex and socioeconomic position (SEP). Methods: A cross-sectional study was conducted in the setting of the Israeli National Program for Quality Indicators in Community Healthcare. Data on all the Israeli population is retrieved from electronic medical records of the four health maintenance organizations (HMOs). The study population included all Israeli patients with diabetes aged 20-64 with documented body mass index (BMI) in 2016 (N=180,451). Diabetes was defined as the existence of one or more of the following criteria: (a) Plasma glucose level >200 mg% in at least two tests conducted at least one month apart in the previous year; (b) HbA1c>6.5% at least once in the previous year (c) at least three prescriptions of diabetes medications were dispensed during the previous year. Two measures were included: the prevalence of obesity (defined as last BMI≥ 30 kg/m2 and <35 kg/m2) and the prevalence of morbid obesity (defined as last BMI≥ 35 kg/m2) in individuals aged 20-64 with diabetes. The cut-off value for morbid obesity was set in accordance with the eligibility criteria for bariatric surgery in diabetics. Data were collected by the HMOs and aggregated by age, sex and SEP. SEP was based on statistical areas ranking by the Israeli Central Bureau of Statistics and divided into 4 categories, ranking from 1 (lowest) to 4 (highest). Results: BMI documentation among adults with diabetes was 84.9% in 2016. The prevalence of obesity in the study population was 30.5%. Although the overall rate was similar in both sexes (30.8% in females, 30.3% in males), SEP disparities were stronger in females (32.7% in SEP level 1 vs. 27.7% in SEP level 4; 18.1% relative difference) compared to males (30.6% in SEP level 1 vs. 29.3% in SEP level 4; 4.4% relative difference). The overall prevalence of morbid obesity in this population was 20.8% in 2016. The rate among females was almost double compared to the rate in males (28.1% and 14.6%, respectively). In both sexes, the prevalence of morbid obesity was strongly associated with lower SEP. However, in females, disparities between SEP levels were much stronger (34.3% in SEP level 1 vs. 18.7% in SEP level 4; 83.4% relative difference) compared to SEP-disparities in males (15.7% in SEP level 1 vs. 12.3% in SEP level 4; 27.6% relative difference). Conclusions: The overall prevalence of BMI≥ 30 kg/m2 among adults with diabetes in Israel exceeds 50%; and the prevalence of morbid obesity suggests that 20% meet the BMI-criteria for bariatric surgery. Prevalence rates show major SEP- and sex-disparities; especially strong SEP disparities in morbid obesity among females. These findings highlight the need for greater consideration of different population groups when implementing interventions.

Keywords: diabetes, health disparities, health policy, obesity, socio-economic position

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Authors: Adam Chlapecka, Anna Kagstrom, Pavla Cermakova

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Background: Increasing educational attainment (EA) could decrease the occurrence of depression. We investigated the relationship between EA and depressive symptoms in older individuals across four European regions. Methods: We studied 108 315 Europeans (54 % women, median age 63 years old) from the Survey on Health, Ageing and Retirement in Europe assessing EA (7 educational levels based on ISCED classification); and depressive symptoms (≥ 4 points on EURO-D scale). Logistic regression estimated the association between EA and depressive symptoms, adjusting for sociodemographic and health-related factors; testing for sex/age/region and education interactions. Results: Higher EA was associated with lower odds of depressive symptoms, independent of sociodemographic and health-related factors. A threshold of the lowest odds of depressive symptoms was detected at the first stage of tertiary education (OR 0.60; 95% CI 0.55-0.65; p<0.001; relative to no education). Central and Eastern Europe showed the strongest association (OR for high vs. low education 0.37; 95% CI 0.33-0.40; p<0.001) and Scandinavia the weakest (OR for high vs. low education 0.69; 95% CI 0.60-0.80; p<0.001). The association was strongest amongst younger individuals. There was a sex and education interaction only within Central and Eastern Europe. Conclusion: The level of EA is reflected in later-life depressive symptoms, suggesting that supporting individuals in achieving EA, and considering those with lower EA at increased risk for depression, could lead to the decreased burden of depression across the life course. Further educational support in Central and Eastern Europe may decrease the higher burden of depressive symptoms in women.

Keywords: depression, education, epidemiology, Europe

Procedia PDF Downloads 205

Authors: T. Kobayashi, K. Shiba, T. Kaburagi, Y. Kurihara

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For the elderly living alone, falls can be a serious problem encountered in daily life. Some elderly people are unable to stand up without the assistance of a caregiver. They may become unconscious after a fall, which can lead to serious aftereffects such as hypothermia, dehydration, and sometimes even death. We treat the subject as an inverted pendulum and model its angle from the equilibrium position and its angular velocity. As the model is non-linear, we implement the filtering method with a particle filter which can estimate true states of the non-linear model. In order to evaluate the accuracy of the particle filter estimation results, we calculate the root mean square error (RMSE) between the estimated angle/angular velocity and the true values generated by the simulation. The experimental results give the highest accuracy RMSE of 0.0141 rad and 0.1311 rad/s for the angle and angular velocity, respectively.

Keywords: fall, microwave Doppler sensor, non-linear dynamics model, particle filter

Procedia PDF Downloads 217