Search results for: real time imaging

Authors: Andrey V. Timofeev

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The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data off the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as 'signaling parameters' (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of C-OTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as a rule. This report contains describing the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.

Keywords: C-OTDR-system, co-processing of signaling parameters, high-speed objects localization, multichannel monitoring systems

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Authors: Mohammadreza Hedyehzadeh, Mahdi Yousefi

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Colon cancer is one of the most common cancer, which predicted to increase its prevalence due to the bad eating habits of peoples. Nowadays, due to the busyness of people, the use of fast foods is increasing, and therefore, diagnosis of this disease and its treatment are of particular importance. To determine the best treatment approach for each specific colon cancer patients, the oncologist should be known the stage of the tumor. The most common method to determine the tumor stage is TNM staging system. In this system, M indicates the presence of metastasis, N indicates the extent of spread to the lymph nodes, and T indicates the size of the tumor. It is clear that in order to determine all three of these parameters, an imaging method must be used, and the gold standard imaging protocols for this purpose are CT and PET/CT. In CT imaging, due to the use of X-rays, the risk of cancer and the absorbed dose of the patient is high, while in the PET/CT method, there is a lack of access to the device due to its high cost. Therefore, in this study, we aimed to estimate the tumor size and the extent of its spread to the lymph nodes using MR images. More than 1300 MR images collected from the TCIA portal, and in the first step (pre-processing), histogram equalization to improve image qualities and resizing to get the same image size was done. Two expert radiologists, which work more than 21 years on colon cancer cases, segmented the images and extracted the tumor region from the images. The next step is feature extraction from segmented images and then classify the data into three classes: T0N0، T3N1 و T3N2. In this article, the VGG-16 convolutional neural network has been used to perform both of the above-mentioned tasks, i.e., feature extraction and classification. This network has 13 convolution layers for feature extraction and three fully connected layers with the softmax activation function for classification. In order to validate the proposed method, the 10-fold cross validation method used in such a way that the data was randomly divided into three parts: training (70% of data), validation (10% of data) and the rest for testing. It is repeated 10 times, each time, the accuracy, sensitivity and specificity of the model are calculated and the average of ten repetitions is reported as the result. The accuracy, specificity and sensitivity of the proposed method for testing dataset was 89/09%, 95/8% and 96/4%. Compared to previous studies, using a safe imaging technique (MRI) and non-use of predefined hand-crafted imaging features to determine the stage of colon cancer patients are some of the study advantages.

Keywords: colon cancer, VGG-16, magnetic resonance imaging, tumor size, lymph node metastasis

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Authors: Andrey Matyukhin

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Russian official statistics have been showing a steady decline in residential real estate prices for several consecutive years. Price risk in real estate markets is thus affecting various groups of economic agents, namely, individuals, construction companies and financial institutions. Potential use of property derivatives might help mitigate adverse consequences of negative price dynamics. Unless a sustainable price indicator is developed, settlement of such instruments imposes constraints on counterparties involved while imposing restrictions on real estate market development. The study addresses geographical and classification heterogeneity in real estate prices by means of variance analysis in various groups of real estate properties. In conclusion, we determine optimal sample structure of representative real estate assets with sufficient level of price homogeneity. The composite price indicator based on the sample would have a higher level of robustness and reliability and hence improving liquidity in the market for property derivatives through underlying standardization. Unlike the majority of existing real estate price indices, calculated on country-wide basis, the optimal indices for Russian market shall be constructed on the city-level.

Keywords: price homogeneity, property derivatives, real estate price index, real estate price risk

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Authors: Wei Li, Tuo Yang

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In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: Manal Alosaimi

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Many rehabilitation strategies exist to aid persons with neurological disorders relearn motor skills through intensive training. Evidence supporting the theory that cortical areas involved in motor execution can be triggered by observing actions performed by others is attributed to the function of the mirror neuron system (MNS) indicates that activation of the MNS is associated with improvements in physical action and motor learning. Therefore, it is important to investigate the relationship between motor training (in this case, playing the drums) and the activation of the MNS. To achieve this, 15 healthy right-handed participants received drum-kit training for 21 weeks, during which time blood oxygen level-dependent (BOLD) signals were monitored in the brain using functional magnetic resonance imaging (fMRI). Participants were required to perform action–observation and action–execution fMRI tasks. The main results are that BOLD signals in classical regions of the MNS such as supramarginal gyri, inferior parietal lobule, and supplementary motor area increase significantly over the training period. Activation of these areas indicates that passive-observation of others performing these same skills may facilitate recovery of persons suffering from neurological disorders, and complement conventional rehabilitation programs that focus on action execution or intense training.

Keywords: fMRI, mirror neuron system, magnetic resonance imaging, neuroplasticity, drumming, learning, music, action observation, action execution

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Authors: Parampal Kaur, Deepak Aggarwal

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In the computational Grid, fault tolerance is an imperative issue to be considered during job scheduling. Due to the widespread use of resources, systems are highly prone to errors and failures. Hence, fault tolerance plays a key role in the grid to avoid the problem of unreliability. Scheduling the task to the appropriate resource is a vital requirement in computational Grid. The fittest resource scheduling algorithm searches for the appropriate resource based on the job requirements, in contrary to the general scheduling algorithms where jobs are scheduled to the resources with best performance factor. The proposed method is to improve the fault tolerance of the fittest resource scheduling algorithm by scheduling the job in coordination with job replication when the resource has low reliability. Based on the reliability index of the resource, the resource is identified as critical. The tasks are scheduled based on the criticality of the resources. Results show that the execution time of the tasks is comparatively reduced with the proposed algorithm using real-time approach rather than a simulator.

Keywords: computational grid, fault tolerance, task replication, job scheduling

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Authors: Ates Gul Ergun, Melda Tacyildiz

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The current study focuses on the relationship between the personality traits and starting a romantic relationship on social media. It is interested in the study whether there are any personality trait differences between individuals who started their romantic relationships on social media platforms or through circle of friends in daily life. Sixty five participants between the ages of 18-30 filled out a three-question-survey about romantic relationships and social media, with the Big Five Inventory. Four separate independent samples t tests comparing agreeableness and extraversion scores on the environment of participants first interacted (online vs. real-life) and where they fırst meet without interaction (online vs. real-life) were carried out. The results indicated that there was a statistically significant difference between people who had the first interaction with their partner online vs. real-life in terms of extraversion and agreeableness traits. The more extrovert and agreeable traits reported the more people were likely to interact with their partner through circle of friends in real-life. Furthermore, it was found that people who are less agreeable have a tendency to interact with their partners in social media for the first time. However, there was no statistically significant difference between how participants met with their partners without interaction (online vs. real-life) in terms of extraversion and agreeableness traits. This study has shown the relationships between personality traits and starting a romantic relationship on social media versus in real-life but not the reasons behind it. Further research could examine such reasons. In addition, the data only includes Turkish sample. By virtue of the cultural restriction in the present study, it is suggested that the future research should also include different cultures to investigate how people spend time with their friends and also in social media which can be changed according to individualism levels of countries. Overall, the study emphasizes the importance and the role of social media in individual’s lives, and it opens the ways associated with personal traits and social media relationships for further researches.

Keywords: agreeableness, big five, extraversion, romantic relationships, social media

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Authors: Monika Szymańska-Czerwińsk, Kinga Zaręba-Marchewka, Krzysztof Niemczuk

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Chlamydiaceae are Gram-negative bacteria distributed worldwide in animals and humans. One of them is Chlamydia gallinacea recently discovered. Available data show that C. gallinacea is dominant chlamydial agent found in poultry in European and Asian countries. The aim of the studies was screening of poultry flocks in order to evaluate frequency of C. gallinacea shedding and genetic diversity. Sampling was conducted in different regions of Poland in 2019-2020. Overall, 1466 cloacal/oral swabs were collected in duplicate from 146 apparently healthy poultry flocks including chickens, turkeys, ducks, geese and quails. Dry swabs were used for DNA extraction. DNA extracts were screened using a Chlamydiaceae 23S rRNA real-time PCR assay. To identify Chlamydia species, specific real-time PCR assays were performed. Furthermore, selected samples were used for sequencing based on ompA gene fragments and variable domains (VD1-2, VD3-4). In total, 10.3% of the tested flocks were Chlamydiaceae-positive (15/146 farms). The presence of Chlamydiaceae was confirmed mainly in chickens (13/92 farms) but also in turkey (1/19 farms) and goose (1/26 farms) flocks. Eleven flocks were identified as C. gallinacea-positive while four flocks remained unclassified. Phylogenetic analysis revealed at least 16 genetic variants of C. gallinacea. Research showed that Chlamydiaceae occur in a poultry flock in Poland. The strains of C. gallinacea as dominant species show genetic variability.

Keywords: C. gallinacea, emerging agent, poultry, real-time PCR

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Authors: Ali Kadivar, Kaveh Niayesh

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The integration of green energy resources is a major focus, and the role of Medium Voltage Direct Current (MVDC) systems is exponentially expanding. However, the protection of MVDC systems against DC faults is a challenge that can have consequences on reliable and safe grid operation. This challenge reveals the need for MVDC circuit breakers (MVDC CB), which are in infancies of their improvement. Therefore will be a lack of MVDC CBs standards, including thresholds for acceptable power losses and operation speed. To establish a baseline for comparison purposes, a benchmark system for testing future MVDC CBs is vital. The literatures just give the timing sequence of each switch and the emphasis is on the topology, without in-depth study on the control algorithm of DCCB, as the circuit breaker control system is not yet systematic. A digital testing benchmark is designed for the Proof-of-concept of simulation studies using software models. It can validate studies based on real-time digital simulators and Transient Network Analyzer (TNA) models. The proposed experimental setup utilizes data accusation from the accurate sensors installed on the tested MVDC CB and through general purpose input/outputs (GPIO) from the microcontroller and PC Prototype studies in the laboratory-based models utilizing Hardware-in-the-Loop (HIL) equipment connected to real-time digital simulators is achieved. The improved control algorithm of the circuit breaker can reduce the peak fault current and avoid arc resignation, helping the coordination of DCCB in relay protection. Moreover, several research gaps are identified regarding case studies and evaluation approaches.

Keywords: DC circuit breaker, hardware-in-the-loop, real time digital simulation, testing benchmark

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Authors: Andrés Calvillo Téllez, Marianne Martínez Zanzarric, José Cruz Núñez Pérez

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A modular system that performs basic monitoring of both water and air quality is presented. Monitoring is essential for environmental, aquaculture, and agricultural disciplines, where this type of instrumentation is necessary for data collection. The system uses low-cost components, which allows readings close to those with high-cost probes. The probe collects readings such as the coordinates of the geographical position, as well as the time it records the target parameters of the monitored. The modules or subsystems that make up the probe are the global positioning (GPS), which shows the altitude, latitude, and longitude data of the point where the reading will be recorded, a real-time clock stage, the date marking the time, the module SD memory continuously stores data, data acquisition system, central processing unit, and energy. The system acquires parameters to measure water quality, conductivity, pressure, and temperature, and for air, three types of ammonia, dioxide, and carbon monoxide gases were censored. The information obtained allowed us to identify the schedule of modification of the parameters and the identification of the ideal conditions for the growth of microorganisms in the water.

Keywords: calibration, conductivity, datalogger, monitoring, real time clock, water quality

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Authors: Nevena Jakovčević Stor, Ivan Slapničar

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Any polynomial can be expressed as a characteristic polynomial of a complex symmetric arrowhead matrix. This expression is not unique. If the polynomial is real with only real distinct roots, the matrix can be chosen as real. By using accurate forward stable algorithm for computing eigen values of real symmetric arrowhead matrices we derive a forward stable algorithm for computation of roots of such polynomials in O(n^2 ) operations. The algorithm computes each root to almost full accuracy. In some cases, the algorithm invokes extended precision routines, but only in the non-iterative part. Our examples include numerically difficult problems, like the well-known Wilkinson’s polynomials. Our algorithm compares favorably to other method for polynomial root-finding, like MPSolve or Newton’s method.

Keywords: roots of polynomials, eigenvalue decomposition, arrowhead matrix, high relative accuracy

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Authors: Liang-Ta Cheng, Ching-Yu Yang

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Based on fast discrete cosine transform (FDCT), the authors present a high capacity and high perceived quality method for electrocardiogram (ECG) signal. By using a simple adjusting policy to the 1-dimentional (1-D) DCT coefficients, a large volume of secret message can be effectively embedded in an ECG host signal and be successfully extracted at the intended receiver. Simulations confirmed that the resulting perceived quality is good, while the hiding capability of the proposed method significantly outperforms that of existing techniques. In addition, our proposed method has a certain degree of robustness. Since the computational complexity is low, it is feasible for our method being employed in real-time applications.

Keywords: data hiding, ECG steganography, fast discrete cosine transform, 1-D DCT bundle, real-time applications

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Authors: Chang Liu, John Nash, Stephen D. Prior

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This paper presents the design and implementation details of a complete unmanned aerial system (UAS) based on commercial-off-the-shelf (COTS) components, focusing on safety, security, search and rescue scenarios in GPS-denied environments. In particular, the aerial platform is capable of semi-autonomously navigating through extremely low-light, GPS-denied indoor environments based on onboard sensors only, including a downward-facing optical flow camera. Besides, an additional low-cost payload camera system is developed to stream both infrared video and visible light video to a ground station in real-time, for the purpose of detecting sign of life and hidden humans. The total cost of the complete system is estimated to be $1150, and the effectiveness of the system has been tested and validated in practical scenarios.

Keywords: unmanned aerial system, commercial-off-the-shelf, extremely low-light, GPS-denied, optical flow, infrared video

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Authors: Saw Thiha, Jay Rajasekera

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A boom in video conferencing generated millions of hours of video data daily to be analyzed. However, such enormous data pose certain scalability issues to be analyzed efficiently, let alone do it in real-time, as online conferences can involve hundreds of people and can last for hours. This paper proposes an efficient online proctoring solution that can analyze the online conferences real-time on edge devices such as Android, iOS, and desktops. Since the computation can be done upfront on the devices where online conferences take place, it can scale well without requiring intensive resources such as GPU servers and complex cloud infrastructure. According to the linear models, face orientation does indeed impact the perceived eye openness. Also, the proposed z score facial landmark standardization was proven to be functional in detecting face orientation and contributed to classifying eye blinks with single eyelid distance computation while achieving a better f1 score and accuracy than the Eye Aspect Ratio (EAR) threshold method. Last but not least, the authors implemented the solution natively in the MediaPipe framework and open-sourced it along with the reproducible experimental results on GitHub. The solution provides face orientation, eye blink, facial activity, and translation detections out of the box and is highly customizable and extensible.

Keywords: android, desktop, edge computing, blink, face orientation, facial activity and translation, MediaPipe, open source, real-time, video conference, web, iOS, Z score facial landmark standardization

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: V. Reyes, P. Ferreira

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In response to high levels of competitiveness, industrial systems have evolved to improve productivity. As a consequence, a rapid increase in volume production and simultaneously, a customization process require lower costs, more variety, and accurate quality of products. Reducing time-cycle production, enabling customizability, and ensure continuous quality improvement are key features in advance intelligent industry. In this scenario, customers and producers will be able to participate in the ongoing production life cycle through real-time interaction. To achieve this vision, transparency, predictability, and adaptability are key features that provide the industrial systems the capability to adapt to customer demands modifying the manufacturing process through an autonomous response and acting preventively to avoid errors. The industrial system incorporates a diversified number of components that in advanced industry are expected to be decentralized, end to end communicating, and with the capability to make own decisions through feedback. The evolving process towards advanced intelligent industry defines a set of stages to empower components of intelligence and enhancing efficiency to achieve the decision-making stage. The integrated system follows an industrial cyber-physical system (CPS) architecture whose real-time integration, based on a set of enabler technologies, links the physical and virtual world generating the digital twin (DT). This instance allows incorporating sensor data from real to virtual world and the required transparency for real-time monitoring and control, contributing to address important features of the advanced intelligent industry and simultaneously improve sustainability. Assuming the industrial CPS as the core technology toward the latest advanced intelligent industry stage, this paper reviews and highlights the correlation and contributions of the enabler technologies for the operationalization of each stage in the path toward advanced intelligent industry. From this research, a real-time integration architecture for a cyber-physical system with applications to collaborative robotics is proposed. The required functionalities and issues to endow the industrial system of adaptability are identified.

Keywords: cyber-physical systems, digital twin, sensor data, system integration, virtual model

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Authors: U. Pruthviraj, Praveen Kumar R. A. K. Athul, K. V. Gangadharan, S. Rao Shrikantha

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An open source based autonomous unmanned marine surface vehicle (UMSV) is developed for some of the marine applications such as pollution control, environmental monitoring and thermal imaging. A double rotomoulded hull boat is deployed which is rugged, tough, quick to deploy and moves faster. It is suitable for environmental monitoring, and it is designed for easy maintenance. A 2HP electric outboard marine motor is used which is powered by a lithium-ion battery and can also be charged from a solar charger. All connections are completely waterproof to IP67 ratings. In full throttle speed, the marine motor is capable of up to 7 kmph. The motor is integrated with an open source based controller using cortex M4F for adjusting the direction of the motor. This UMSV can be operated by three modes: semi-autonomous, manual and fully automated. One of the channels of a 2.4GHz radio link 8 channel transmitter is used for toggling between different modes of the USMV. In this electric outboard marine motor an on board GPS system has been fitted to find the range and GPS positioning. The entire system can be assembled in the field in less than 10 minutes. A Flir Lepton thermal camera core, is integrated with a 64-bit quad-core Linux based open source processor, facilitating real-time capturing of thermal images and the results are stored in a micro SD card which is a data storage device for the system. The thermal camera is interfaced to an open source processor through SPI protocol. These thermal images are used for finding oil spills and to look for people who are drowning at low visibility during the night time. A Real Time clock (RTC) module is attached with the battery to provide the date and time of thermal images captured. For the live video feed, a 900MHz long range video transmitter and receiver is setup by which from a higher power output a longer range of 40miles has been achieved. A Multi-parameter probe is used to measure the following parameters: conductivity, salinity, resistivity, density, dissolved oxygen content, ORP (Oxidation-Reduction Potential), pH level, temperature, water level and pressure (absolute).The maximum pressure it can withstand 160 psi, up to 100m. This work represents a field demonstration of an open source based autonomous navigation system for a marine surface vehicle.

Keywords: open source, autonomous navigation, environmental monitoring, UMSV, outboard motor, multi-parameter probe

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Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Yooncheol Lee, Jeongmin Kim, Kwang Ryel Ryu

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This paper deals with the establishment of a short-term operational plan for an air conditioner for efficient energy management of exhibition hall. The short-term operational plan is composed of a time series of operational schedules, which we have searched using genetic algorithms. Establishing operational schedule should be considered the future trends of the variables affecting the exhibition hall environment. To reflect continuously changing factors such as external temperature and occupant, short-term operational plans should be updated in real time. But it takes too much time to evaluate a short-term operational plan using EnergyPlus, a building emulation tool. For that reason, it is difficult to update the operational plan in real time. To evaluate the short-term operational plan, we designed prediction models based on machine learning with fast evaluation speed. This model, which was created by learning the past operational data, is accurate and fast. The collection of operational data and the verification of operational plans were made using EnergyPlus. Experimental results show that the proposed method can save energy compared to the reactive control method.

Keywords: exhibition hall, energy management, predictive model, simulation-based optimization

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Authors: Morteza Ahmadpur, Ilgin Gokasar, Saman Ghaffarian

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That Transportation system plays an important role in management of societies is an undeniable fact and it is one of the most challenging issues in human beings routine life. But by increasing the population in urban areas, the demand for transportation modes also increase. Accordingly, it is obvious that more flexible and dynamic transportation system is required to satisfy peoples’ requirements. Nowadays, there is significant increase in number of environmental issues all over the world which is because of human activities. New technological achievements bring new horizons for humans and so they changed the life style of humans in every aspect of their life and transportation is not an exception. By using new technology, societies can modernize their transportation system and increase the feasibility of their system. Real–time Taxi sharing systems is one of the novel and most modern systems all over the world. For establishing this kind of system in an urban area it is required to use the most advanced technologies in a transportation system. GPS navigation devices, computers and social networks are just some parts of this kind of system. Like carpooling, real-time taxi sharing is one of the best ways to better utilize the empty seats in most cars and taxis, thus decreasing energy consumption and transport costs. It can serve areas not covered by a public transit system and act as a transit feeder service. Taxi sharing is also capable of serving one-time trips, not only recurrent commute trips or scheduled trips. In this study, we describe the requirements and parameters that we need to establish a useful real-time ride sharing system for an urban area. The parameters and requirements of this study can be used in any urban area.

Keywords: transportation, intelligent transportation systems, ride-sharing, taxi sharing

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Authors: Philip J. Purnell

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Innovators propose to use the Internet of Things to solve the problem of maintaining the cold chain during the transport of biopharmaceutical products. Sending a data logger with refrigerated goods is only useful to inform the recipient of the goods that they have either breached the cold chain and are therefore potentially spoiled or that they have not breached it and are therefore assumed to be in good condition. Connecting the data logger to the Internet of Things means that the supply chain manager will be informed in real-time of the exact location and the precise temperature of the material at any point on earth. Readable using a simple online interface, the supply chain manager will watch the progress of their material on a Google map together with accurate and crucially real-time temperature readings. The data logger will also send alarms to the supply chain manager if a cold chain breach becomes imminent allowing them time to contact the transporter and restore the cold chain before the material is affected. This development is expected to save billions of dollars in wasted biologics that currently arrive either spoiled or in an unreliable condition.

Keywords: internet of things, cold chain, data logger, transportation

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Authors: Ergün Şakalar, Şeyma Özçirak Ergün, Emrah Yalazi̇, Emine Altinkaya, Cengiz Ataşoğlu

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In recent years, food allergies which cause serious health problems affect to public health around the world. Foodstuffs which contain allergens are either intentionally used as ingredients or are encased as contaminant in food products. The prevalence of clinical allergy to peanuts and nuts is estimated at about 0.4%-1.1% of the adult population, representing the allergy to pistachio the 7% of the cases of tree nut causing allergic reactions. In order to protect public health and enforce the legislation, methods for sensitive analysis of pistachio and peanut contents in food are required. Pea, pistachio and peanut are used together, to reduce the cost in food production such as baklava, snack foods.DNA technology-based methods in food analysis are well-established and well-roundedtools for species differentiation, allergen detection. Especially, the probe-based TaqMan real-time PCR assay can amplify target DNA with efficiency, specificity, and sensitivity.In this study, pistachio, peanut and pea were finely ground and three separate series of triplet mixtures containing 0.1, 1, 10, 100, 1000, 10,000 and 100,000 mg kg-1 of each sample were prepared for each series, to a final weight of 100 g. DNA from reference samples and industrial products was successfully extracted with the GIDAGEN® Multi-Fast DNA Isolation Kit. TaqMan probes were designed for triplex determination of ITS, Ara h 3 and pea lectin genes which are specific regions for identification pistachio, peanut and pea, respectively.The real-time PCR as quantitative detected pistachio, peanut and pea in these mixtures down to the lowest investigated level of 0.1, 0.1 and 1 mg kg-1, respectively. Also, the methods reported here are capable of detecting of as little as 0.001% level of peanut DNA, 0,000001% level of pistachio DNA and 0.000001% level of pea DNA. We accomplish that the quantitative triplex real-time PCR method developed in this study canbe applied to detect pistachio, peanut and peatraces for three allergens at once in commercial food products.

Keywords: allergens, DNA, real-time PCR, TaqMan probe

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Daofan Guo, Dong Yang

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For decades, well logging with coiled tubing has relied solely on surface data such as pump pressure, wellhead pressure, depth counter, and weight indicator readings. While this data serves the oil industry well, modern smart logging utilizes real-time downhole information, which automatically increases operational efficiency and optimizes intervention qualities. For example, downhole pressure, temperature, and depth measurement data can be transmitted through the electro-optical hybrid cable in the coiled tubing to surface operators on a real-time base. This paper mainly introduces the unique structural features and various applications of the electro-optical hybrid cables which were deployed into downhole with the help of coiled tubing technology. Fiber optic elements in the cable enable optical communications and distributed measurements, such as distributed temperature and acoustic sensing. The electrical elements provide continuous surface power for downhole tools, eliminating the limitations of traditional batteries, such as temperature, operating time, and safety concerns. The electrical elements also enable cable telemetry operation of cable tools. Both power supply and signal transmission were integrated into an electro-optical hybrid cable, and the downhole information can be captured by downhole electrical sensors and distributed optical sensing technologies, then travels up through an optical fiber to the surface, which greatly improves the accuracy of measurement data transmission.

Keywords: electro-optical hybrid cable, underground photoelectric composite cable, seismic cable, coiled tubing, real-time monitoring

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Authors: Mojtaba Mirmontazemi, Habibollah Dadgar

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Bone is the most common metastasis site in some advanced malignancies, such as prostate and breast cancer. Bone metastasis generally indicates fewer prognostic factors in these patients. Different radiological and molecular imaging modalities are used for detecting bone lesions. Molecular imaging including computed tomography, magnetic resonance imaging, planar bone scintigraphy, single-photon emission tomography, and positron emission tomography as noninvasive visualization of the biological occurrences has the potential to exact examination, characterization, risk stratification and comprehension of human being diseases. Also, it is potent to straightly visualize targets, specify clearly cellular pathways and provide precision medicine for molecular targeted therapies. These advantages contribute implement personalized treatment for each patient. Currently, NaF PET/CT has significantly replaced standard bone scintigraphy for the detection of bone metastases. On one hand, 68Ga-PSMA PET/CT has gained high attention for accurate staging of primary prostate cancer and restaging after biochemical recurrence. On the other hand, FDG PET/CT is not commonly used in osseous metastases of prostate and breast cancer as well as its usage is limited to staging patients with aggressive primary tumors or localizing the site of disease. In this article, we examine current studies about FDG, NaF, and PSMA PET/CT images in bone metastases diagnostic utility and assess response to treatment in patients with breast and prostate cancer.

Keywords: skeletal metastases, fluorodeoxyglucose, sodium fluoride, molecular imaging, precision medicine, prostate cancer (68Ga-PSMA-11)

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Authors: Heba Abdelmotaal, Magdy Abdel-Kader

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Purpose: This study explores whether a firm’s effective political risk management is preventing real and accrual earnings management . Design/methodology/approach: Based on a sample of 130 firms operating in Egypt during the period 2008-2013, two hypotheses are tested using the panel data regression models. Findings: The empirical findings indicate a significant relation between real and accrual earnings management and political risk. Originality/value: This paper provides a statistically evidence on the effects of the political risk management failure on the mangers’ engagement in the real and accrual earnings management practices, and its impact on the firm’s performance.

Keywords: political risk, risk management failure, real activities manipulation, accrual earnings management

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Authors: S. Khan, A. Khan, Abdul R. Soomrani, Raja F. Zafar, A. Waqas, G. Akbar

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This paper proposes a low-complexity image interpolation method. Image interpolation is used to convert a low dimension video/image to high dimension video/image. The objective of a good interpolation method is to upscale an image in such a way that it provides better edge preservation at the cost of very low complexity so that real-time processing of video frames can be made possible. However, low complexity methods tend to provide real-time interpolation at the cost of blurring, jagging and other artifacts due to errors in slope calculation. Non-linear methods, on the other hand, provide better edge preservation, but at the cost of high complexity and hence they can be considered very far from having real-time interpolation. The proposed method is a linear method that uses gradient orientation for slope calculation, unlike conventional linear methods that uses the contrast of nearby pixels. Prewitt edge detection is applied to separate uniform regions and edges. Simple line averaging is applied to unknown uniform regions, whereas unknown edge pixels are interpolated after calculation of slopes using gradient orientations of neighboring known edge pixels. As a post-processing step, bilateral filter is applied to interpolated edge regions in order to enhance the interpolated edges.

Keywords: edge detection, gradient orientation, image upscaling, linear interpolation, slope tracing

Procedia PDF Downloads 239

Authors: Fathi Ali Swaid

Abstract:

Soil salinization is one of the most hazardous phenomenons accelerating the land degradation processes. It either occurs naturally or is human-induced. High levels of soil salinity negatively affect crop growth and productivity leading land degradation ultimately. Thus, it is important to monitor and map soil salinity at an early stage to enact effective soil reclamation program that helps lessen or prevent future increase in soil salinity. Geophysical method has outperformed the traditional method for assessing soil salinity offering more informative and professional rapid assessment techniques for monitoring and mapping soil salinity. Soil sampling, EM38 and 2D conductivity imaging have been evaluated for their ability to delineate and map the level of salinity variations at Second Ponds Creek. The three methods have shown that the subsoil in the study area is saline. Salt variations were successfully observed under either method. However, EM38 reading and 2D inversion data show a clear spatial structure comparing to EC1:5 of soil samples in spite of that all soil samples, EM38 and 2D imaging were collected from the same location. Because EM38 readings and 2D imaging data are a weighted average of electrical soil conductance, it is more representative of soil properties than the soil samples method. The mapping of subsurface soil at the study area has been successful and the resistivity imaging has proven to be an advantage. The soil salinity analysis (EC1:5) correspond well to the true resistivity bringing together a good result of soil salinity. Soil salinity clearly indicated by previous investigation EM38 have been confirmed by the interpretation of the true resistivity at study area.

Keywords: 2D conductivity imaging, EM38 readings, soil salinization, true resistivity, urban salinity

Procedia PDF Downloads 347

Authors: Pradeep Kumar Singh Chauhan, Gayatri Devi, Zamir Ahmad, Komal Chauhan, Abha Mittal

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The application of geophysical methods to study the subsurface profile for site investigation is becoming popular globally. These methods are non-destructive and provide the image of subsurface at shallow depths. Seismic refraction method is one of the most common and efficient method being used for civil engineering site investigations particularly for knowing the seismic velocity of the subsurface layers. Resistivity imaging technique is a geo-electrical method used to image the subsurface, water bearing zone, bedrock and layer thickness. Integrated approach combining seismic refraction and 2-D resistivity imaging will provide a better and reliable picture of the subsurface. These are economical and less time-consuming field survey which provide high resolution image of the subsurface. Geophysical surveys carried out in this study include seismic refraction and 2D resistivity imaging method for delineation of sub-surface strata in different parts of Srinagar, Garhwal Himalaya, India. The aim of this survey was to map the shallow subsurface in terms of geological and geophysical properties mainly P-wave velocity, resistivity, layer thickness, and lithology of the area. Both sides of the river, Alaknanda which flows through the centre of the city, have been covered by taking two profiles on each side using both methods. Seismic and electrical surveys were carried out at the same locations to complement the results of each other. The seismic refraction survey was carried out using ABEM TeraLoc 24 channel Seismograph and 2D resistivity imaging was performed using ABEM Terrameter LS equipment. The results show three distinct layers on both sides of the river up to the depth of 20 m. The subsurface is divided into three distinct layers namely, alluvium extending up to, 3 m depth, conglomerate zone lying between the depth of 3 m to 15 m, and compacted pebbles and cobbles beyond 15 m. P-wave velocity in top layer is found in the range of 400 – 600 m/s, in second layer it varies from 700 – 1100 m/s and in the third layer it is 1500 – 3300 m/s. The resistivity results also show similar pattern and were in good agreement with seismic refraction results. The results obtained in this study were validated with an available exposed river scar at one site. The study established the efficacy of geophysical methods for subsurface investigations.

Keywords: 2D resistivity imaging, P-wave velocity, seismic refraction survey, subsurface

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Authors: Kristina Ancutiene

Abstract:

The goal of this research is to perform comparative analysis between the virtual fit of the woman's dress and the fit on a real person. The dress fitting was done using mechanical and structural parameters of the 100 % linen fabric and using Modaris_3D_Fit software (CAD Lectra). The dress was also sawn after which garment fit differences of real and virtual dress was researched. Four respondents whose figures were similar were used to evaluate the ease and strain deformations of the real and virtual dress. The scores that were given by the respondents wearing the real dress were compared to the ease and strain results that were given by the software. The main result was that respondents feel similar to the virtual stretch deformations but their ease feeling is not always matching the virtual ones. The results may be influenced by psychological factors and different understanding about purpose of garment.

Keywords: virtual garment, 3D CAD, garment fit, mechanical properties

Procedia PDF Downloads 302