Search results for: infrared small target detection
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 10918

Search results for: infrared small target detection

10708 Integrated Microsystem for Multiplexed Genosensor Detection of Biowarfare Agents

Authors: Samuel B. Dulay, Sandra Julich, Herbert Tomaso, Ciara K. O'Sullivan

Abstract:

An early, rapid and definite detection for the presence of biowarfare agents, pathogens, viruses and toxins is required in different situations which include civil rescue and security units, homeland security, military operations, public transportation securities such as airports, metro and railway stations due to its harmful effect on the human population. In this work, an electrochemical genosensor array that allows simultaneous detection of different biowarfare agents within an integrated microsystem that provides an easy handling of the technology which combines a microfluidics setup with a multiplexing genosensor array has been developed and optimised for the following targets: Bacillus anthracis, Brucella abortis and melitensis, Bacteriophage lambda, Francisella tularensis, Burkholderia mallei and pseudomallei, Coxiella burnetii, Yersinia pestis, and Bacillus thuringiensis. The electrode array was modified via co-immobilisation of a 1:100 (mol/mol) mixture of a thiolated probe and an oligoethyleneglycol-terminated monopodal thiol. PCR products from these relevant biowarfare agents were detected reproducibly through a sandwich assay format with the target hybridised between a surface immobilised probe into the electrode and a horseradish peroxidase-labelled secondary reporter probe, which provided an enzyme based electrochemical signal. The potential of the designed microsystem for multiplexed genosensor detection and cross-reactivity studies over potential interfering DNA sequences has demonstrated high selectivity using the developed platform producing high-throughput.

Keywords: biowarfare agents, genosensors, multipled detection, microsystem

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10707 Change Detection Method Based on Scale-Invariant Feature Transformation Keypoints and Segmentation for Synthetic Aperture Radar Image

Authors: Lan Du, Yan Wang, Hui Dai

Abstract:

Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method.

Keywords: change detection, Synthetic Aperture Radar (SAR), Scale-Invariant Feature Transformation (SIFT), segmentation

Procedia PDF Downloads 354
10706 Small Scale Mobile Robot Auto-Parking Using Deep Learning, Image Processing, and Kinematics-Based Target Prediction

Authors: Mingxin Li, Liya Ni

Abstract:

Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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10705 Optimized Road Lane Detection Through a Combined Canny Edge Detection, Hough Transform, and Scaleable Region Masking Toward Autonomous Driving

Authors: Samane Sharifi Monfared, Lavdie Rada

Abstract:

Nowadays, autonomous vehicles are developing rapidly toward facilitating human car driving. One of the main issues is road lane detection for a suitable guidance direction and car accident prevention. This paper aims to improve and optimize road line detection based on a combination of camera calibration, the Hough transform, and Canny edge detection. The video processing is implemented using the Open CV library with the novelty of having a scale able region masking. The aim of the study is to introduce automatic road lane detection techniques with the user’s minimum manual intervention.

Keywords: hough transform, canny edge detection, optimisation, scaleable masking, camera calibration, improving the quality of image, image processing, video processing

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10704 Design and Developing the Infrared Sensor for Detection and Measuring Mass Flow Rate in Seed Drills

Authors: Bahram Besharti, Hossein Navid, Hadi Karimi, Hossein Behfar, Iraj Eskandari

Abstract:

Multiple or miss sowing by seed drills is a common problem on the farm. This problem causes overuse of seeds, wasting energy, rising crop treatment cost and reducing crop yield in harvesting. To be informed of mentioned faults and monitoring the performance of seed drills during sowing, developing a seed sensor for detecting seed mass flow rate and monitoring in a delivery tube is essential. In this research, an infrared seed sensor was developed to estimate seed mass flow rate in seed drills. The developed sensor comprised of a pair of spaced apart circuits one acting as an IR transmitter and the other acting as an IR receiver. Optical coverage in the sensing section was obtained by setting IR LEDs and photo-diodes directly on opposite sides. Passing seeds made interruption in radiation beams to the photo-diode which caused output voltages to change. The voltage difference of sensing units summed by a microcontroller and were converted to an analog value by DAC chip. The sensor was tested by using a roller seed metering device with three types of seeds consist of chickpea, wheat, and alfalfa (representing large, medium and fine seed, respectively). The results revealed a good fitting between voltage received from seed sensor and mass flow of seeds in the delivery tube. A linear trend line was set for three seeds collected data as a model of the mass flow of seeds. A final mass flow model was developed for various size seeds based on receiving voltages from the seed sensor, thousand seed weight and equivalent diameter of seeds. The developed infrared seed sensor, besides monitoring mass flow of seeds in field operations, can be used for the assessment of mechanical planter seed metering unit performance in the laboratory and provide an easy calibrating method for seed drills before planting in the field.

Keywords: seed flow, infrared, seed sensor, seed drills

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10703 Process of Dimensioning Small Type Annular Combustors

Authors: Saleh B. Mohamed, Mohamed H. Elhsnawi, Mesbah M. Salem

Abstract:

Current and future applications of small gas turbine engines annular type combustors have requirements presenting difficult disputes to the combustor designer. Reduced cost and fuel consumption and improved durability and reliability as well as higher temperatures and pressures for such application are forecast. Coupled with these performance requirements, irrespective of the engine size, is the demand to control the pollutant emissions, namely the oxides of nitrogen, carbon monoxide, smoke and unburned hydrocarbons. These technical and environmental challenges have made the design of small size combustion system a very hard task. Thus, the main target of this work is to generalize a calculation method of annular type combustors for small gas turbine engines that enables to understand the fundamental concepts of the coupled processes and to identify the proper procedure that formulates and solves the problems in combustion fields in as much simplified and accurate manner as possible. The combustion chamber in task is designed with central vaporizing unit and to deliver 516.3 KW of power. The geometrical constraints are 142 mm & 140 mm overall length and casing diameter, respectively, while the airflow rate is 0.8 kg/sec and the fuel flow rate is 0.012 kg/sec. The relevant design equations are programmed by using MathCAD language for ease and speed up of the calculation process.

Keywords: design of gas turbine, small engine design, annular type combustors, mechanical engineering

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10702 The Using of Hybrid Superparamagnetic Magnetite Nanoparticles (Fe₃O₄)- Graphene Oxide Functionalized Surface with Collagen, to Target the Cancer Stem Cell

Authors: Ahmed Khalaf Reyad Raslan

Abstract:

Cancer stem cells (CSCs) describe a class of pluripotent cancer cells that behave analogously to normal stem cells in their ability to differentiate into the spectrum of cell types observed in tumors. The de-differentiation processes, such as an epithelial-mesenchymal transition (EMT), are known to enhance cellular plasticity. Here, we demonstrate a new hypothesis to use hybrid superparamagnetic magnetite nanoparticles (Fe₃O₄)- graphene oxide functionalized surface with Collagen to target the cancer stem cell as an early detection tool for cancer. We think that with the use of magnetic resonance imaging (MRI) and the new hybrid system would be possible to track the cancer stem cells.

Keywords: hydrogel, alginate, reduced graphene oxide, collagen

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10701 Spontaneous and Posed Smile Detection: Deep Learning, Traditional Machine Learning, and Human Performance

Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

Abstract:

A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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10700 Robust and Real-Time Traffic Counting System

Authors: Hossam M. Moftah, Aboul Ella Hassanien

Abstract:

In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. Traffic counting as a kind of traffic control is important to know the road traffic density in real time. This paper presents a fast and robust traffic counting system using different image processing techniques. The proposed system is composed of the following four fundamental building phases: image acquisition, pre-processing, object detection, and finally counting the connected objects. The object detection phase is comprised of the following five steps: subtracting the background, converting the image to binary, closing gaps and connecting nearby blobs, image smoothing to remove noises and very small objects, and detecting the connected objects. Experimental results show the great success of the proposed approach.

Keywords: traffic counting, traffic management, image processing, object detection, computer vision

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10699 Image Classification with Localization Using Convolutional Neural Networks

Authors: Bhuyain Mobarok Hossain

Abstract:

Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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10698 Evaluation of Heterogeneity of Paint Coating on Metal Substrate Using Laser Infrared Thermography and Eddy Current

Authors: S. Mezghani, E. Perrin, J. L. Bodnar, J. Marthe, B. Cauwe, V. Vrabie

Abstract:

Non contact evaluation of the thickness of paint coatings can be attempted by different destructive and nondestructive methods such as cross-section microscopy, gravimetric mass measurement, magnetic gauges, Eddy current, ultrasound or terahertz. Infrared thermography is a nondestructive and non-invasive method that can be envisaged as a useful tool to measure the surface thickness variations by analyzing the temperature response. In this paper, the thermal quadrupole method for two layered samples heated up with a pulsed excitation is firstly used. By analyzing the thermal responses as a function of thermal properties and thicknesses of both layers, optimal parameters for the excitation source can be identified. Simulations show that a pulsed excitation with duration of ten milliseconds allows to obtain a substrate-independent thermal response. Based on this result, an experimental setup consisting of a near-infrared laser diode and an Infrared camera was next used to evaluate the variation of paint coating thickness between 60 µm and 130 µm on two samples. Results show that the parameters extracted for thermal images are correlated with the estimated thicknesses by the Eddy current methods. The laser pulsed thermography is thus an interesting alternative nondestructive method that can be moreover used for non conductive substrates.

Keywords: non destructive, paint coating, thickness, infrared thermography, laser, heterogeneity

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10697 A Framework for Review Spam Detection Research

Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

Abstract:

With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a high-quality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: fake reviews, feature collection, opinion spam, spam detection

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10696 Application of UAS in Forest Firefighting for Detecting Ignitions and 3D Fuel Volume Estimation

Authors: Artur Krukowski, Emmanouela Vogiatzaki

Abstract:

The article presents results from the AF3 project “Advanced Forest Fire Fighting” focused on Unmanned Aircraft Systems (UAS)-based 3D surveillance and 3D area mapping using high-resolution photogrammetric methods from multispectral imaging, also taking advantage of the 3D scanning techniques from the SCAN4RECO project. We also present a proprietary embedded sensor system used for the detection of fire ignitions in the forest using near-infrared based scanner with weight and form factors allowing it to be easily deployed on standard commercial micro-UAVs, such as DJI Inspire or Mavic. Results from real-life pilot trials in Greece, Spain, and Israel demonstrated added-value in the use of UAS for precise and reliable detection of forest fires, as well as high-resolution 3D aerial modeling for accurate quantification of human resources and equipment required for firefighting.

Keywords: forest wildfires, surveillance, fuel volume estimation, firefighting, ignition detectors, 3D modelling, UAV

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10695 Measurement of Susceptibility Users Using Email Phishing Attack

Authors: Cindy Sahera, Sarwono Sutikno

Abstract:

Rapid technological developments also have negative impacts, namely the increasing criminal cases based on technology or cybercrime. One technique that can be used to conduct cybercrime attacks are phishing email. The issue is whether the user is aware that email can be misused by others so that it can harm the user's own? This research was conducted to measure the susceptibility of selected targets against email abuse. The objectives of this research are measurement of targets’ susceptibility and find vulnerability in email recipient. There are three steps being taken in this research, (1) the information gathering phase, (2) the design phase, and (3) the execution phase. The first step includes the collection of the information necessary to carry out an attack on a target. The next step is to make the design of an attack against a target. The last step is to send phishing emails to the target. The levels of susceptibility are three: level 1, level 2 and level 3. Level 1 indicates a low level of targets’ susceptibility, level 2 indicates the intermediate level of targets’ susceptibility, and level 3 indicates a high level of targets’ susceptibility. The results showed that users who are on level 1 and level 2 more that level 3, which means the user is not too careless. However, it does not mean the user to be safe. There are still vulnerabilities that may occur, such as automatic location detection when opening emails and automatic downloaded malware as user clicks a link in the email.

Keywords: cybercrime, email phishing, susceptibility, vulnerability

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10694 Small-Sided Games in Football: Effect of Field Sizes on Technical Parameters

Authors: Faruk Guven, Nurtekin Erkmen, Samet Aktas, Cengiz Taskin

Abstract:

The aim of this study was to determine effects of field sizes on technical parameters of small-sided games in football players. Eight amateur football players (27.23±3.08 years, heigth: 171.01±5.36 cm, body weigth: 66.86±4.54 kg, sports experience: 12.88±3.28 years) performed 4-a-side small-sided games (SSG) with different field sizes. In SSGs, field sizes were 30 x 40 m and 26 mx24 m. SSGs was conducted as a series of 3 bouts of 6 min with 5 min recovery durations. All SSGs were video recorded using two digital video camcorder positioned on a tripot. Shoot on taget, passes, succesful passes, unsuccesful passes, dripling, tackle, possession in SSGs were counted by Mathball Match Analysis System. The effects of bouts on technical score were examined separately using a Friedman’s test. Mann Whitney U test was applied to analyse differences between field sizes. There were no significant differences in shoots on target, total pass, successful pass, tackle, interception, possession between bouts in 30x40 m field size (p>0.05). Unsuccessful pass in bout 3 for 30x40 m field size was lower than bout 1 and bout 2 (p<0.05) and dripling in bout 3 was lower than bout 2 (p<0.05). There was no significant difference in technical actions between bouts for 26x34 m field size (p>0.05). Shoot on target in SSG with 26 x 34 m field size was higher than SSG with 30x40 m field size (p<0.05). Unsuccessful pass for 26x34 m field size in bout 3 was higher than SSG with 30x40 m field size (p<0.05). There was no significant difference in technical actions between field sizes (p>0.05). In conclusion; in this study demonstrates that technical actions in a-4-side SSG are not influenced by different field sizes (for 30x40 m and 26x34 m field sizes). This consequence is same for both total SSG time and each bout. Dripling and unsuccessful pass decrease in bout 3 during SSG in 30 x 40 m field size.

Keywords: small-sided games, football, technical actions, sport science

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10693 A Speeded up Robust Scale-Invariant Feature Transform Currency Recognition Algorithm

Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

Abstract:

All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

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10692 The Effect of Closed Circuit Television Image Patch Layout on Performance of a Simulated Train-Platform Departure Task

Authors: Aaron J. Small, Craig A. Fletcher

Abstract:

This study investigates the effect of closed circuit television (CCTV) image patch layout on performance of a simulated train-platform departure task. The within-subjects experimental design measures target detection rate and response latency during a CCTV visual search task conducted as part of the procedure for safe train dispatch. Three interface designs were developed by manipulating CCTV image patch layout. Eye movements, perceived workload and system usability were measured across experimental conditions. Task performance was compared to identify significant differences between conditions. The results of this study have not been determined.

Keywords: rail human factors, workload, closed circuit television, platform departure, attention, information processing, interface design

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10691 Determination of Klebsiella Pneumoniae Susceptibility to Antibiotics Using Infrared Spectroscopy and Machine Learning Algorithms

Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

Abstract:

Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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10690 Study on an Integrated Real-Time Sensor in Droplet-Based Microfluidics

Authors: Tien-Li Chang, Huang-Chi Huang, Zhao-Chi Chen, Wun-Yi Chen

Abstract:

The droplet-based microfluidic are used as micro-reactors for chemical and biological assays. Hence, the precise addition of reagents into the droplets is essential for this function in the scope of lab-on-a-chip applications. To obtain the characteristics (size, velocity, pressure, and frequency of production) of droplets, this study describes an integrated on-chip method of real-time signal detection. By controlling and manipulating the fluids, the flow behavior can be obtained in the droplet-based microfluidics. The detection method is used a type of infrared sensor. Through the varieties of droplets in the microfluidic devices, the real-time conditions of velocity and pressure are gained from the sensors. Here the microfluidic devices are fabricated by polydimethylsiloxane (PDMS). To measure the droplets, the signal acquisition of sensor and LabVIEW program control must be established in the microchannel devices. The devices can generate the different size droplets where the flow rate of oil phase is fixed 30 μl/hr and the flow rates of water phase range are from 20 μl/hr to 80 μl/hr. The experimental results demonstrate that the sensors are able to measure the time difference of droplets under the different velocity at the voltage from 0 V to 2 V. Consequently, the droplets are measured the fastest speed of 1.6 mm/s and related flow behaviors that can be helpful to develop and integrate the practical microfluidic applications.

Keywords: microfluidic, droplets, sensors, single detection

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10689 Standard Protocol Selection for Acquisition of Breast Thermogram in Perspective of Early Breast Cancer Detection

Authors: Mrinal Kanti Bhowmik, Usha Rani Gogoi Jr., Anjan Kumar Ghosh, Debotosh Bhattacharjee

Abstract:

In the last few decades, breast thermography has achieved an average sensitivity and specificity of 90% for breast tumor detection. Breast thermography is a non-invasive, cost-effective, painless and radiation-free breast imaging modality which makes a significant contribution to the evaluation and diagnosis of patients, suspected of having breast cancer. An abnormal breast thermogram may indicate significant biological risk for the existence or the development of breast tumors. Breast thermography can detect a breast tumor, when the tumor is in its early stage or when the tumor is in a dense breast. The infrared breast thermography is very sensitive to environmental changes for which acquisition of breast thermography should be performed under strictly controlled conditions by undergoing some standard protocols. Several factors like air, temperature, humidity, etc. are there to be considered for characterizing thermal images as an imperative tool for detecting breast cancer. A detailed study of various breast thermogram acquisition protocols adopted by different researchers in their research work is provided here in this paper. After going through a rigorous study of different breast thermogram acquisition protocols, a new standard breast thermography acquisition setup is proposed here in this paper for proper and accurate capturing of the breast thermograms. The proposed breast thermogram acquisition setup is being built in the Radiology Department, Agartala Government Medical College (AGMC), Govt. of Tripura, Tripura, India. The breast thermograms are captured using FLIR T650sc thermal camera with the thermal sensitivity of 20 mK at 30 degree C. The paper is an attempt to highlight the importance of different critical parameters of breast thermography like different thermography views, patient preparation protocols, acquisition room requirements, acquisition system requirements, etc. This paper makes an important contribution by providing a detailed survey and a new efficient approach on breast thermogram capturing.

Keywords: acquisition protocol, breast cancer, breast thermography, infrared thermography

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10688 Amorphous Silicon-Based PINIP Structure for Human-Like Photosensor

Authors: Sheng-Chuan Hsu

Abstract:

Because the existing structure of ambient light sensor is most silicon photodiode device, it is extremely sensitive in the red and infrared regions. Even though the IR-Cut filter had added, it still cannot completely eliminate the influence of infrared light, and the spectral response of infrared light was stronger than that of the human eyes. Therefore, it is not able to present the vision spectrum of the human eye reacts with the ambient light. Then it needs to consider that the human eye feels the spectra that show significant differences between light and dark place. Consequently, in practical applications, we must create and develop advanced device of human-like photosensor which can solve these problems of ambient light sensor and let cognitive lighting system to provide suitable light to achieve the goals of vision spectrum of human eye and save energy.

Keywords: ambient light sensor, vision spectrum, cognitive lighting system, human eye

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10687 Design and Implementation of an Image Based System to Enhance the Security of ATM

Authors: Seyed Nima Tayarani Bathaie

Abstract:

In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: face detection algorithm, Haar features, security of ATM

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10686 Oncogenic Role of MicroRNA-346 in Human Non-Small Cell Lung Cancer by Regulation of XPC/ERK/Snail/E-Cadherin Pathway

Authors: Cheng-Cao Sun, Shu-Jun Li, De-Jia Li

Abstract:

Determinants of growth and metastasis in cancer remain of great interest to define. MicroRNAs (miRNAs) have frequently emerged as tumor metastatic regulator by acting on multiple signaling pathways. Here, we report the definition of miR-346 as an oncogenic microRNA that facilitates non-small cell lung cancer (NSCLC) cell growth and metastasis. XPC, an important DNA damage recognition factor in nucleotide excision repair was defined as a target for down-regulation by miR-346, functioning through direct interaction with the 3'-UTR of XPC mRNA. Blocking miR-346 by an antagomiR was sufficient to inhibit NSCLC cell growth and metastasis, an effect that could be phenol-copied by RNAi-mediated silencing of XPC. In vivo studies established that miR-346 overexpression was sufficient to promote tumor growth by A549 cells in xenografts mice, relative to control cells. Overall, our results defined miR-346 as an oncogenic miRNA in NSCLC, the levels of which contributed to tumor growth and invasive aggressiveness.

Keywords: microRNA-346, miR-346, XPC, non-small cell lung cancer, oncogenesis

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10685 Development of a Sensitive Electrochemical Sensor Based on Carbon Dots and Graphitic Carbon Nitride for the Detection of 2-Chlorophenol and Arsenic

Authors: Theo H. G. Moundzounga

Abstract:

Arsenic and 2-chlorophenol are priority pollutants that pose serious health threats to humans and ecology. An electrochemical sensor, based on graphitic carbon nitride (g-C₃N₄) and carbon dots (CDs), was fabricated and used for the determination of arsenic and 2-chlorophenol. The g-C₃N₄/CDs nanocomposite was prepared via microwave irradiation heating method and was dropped-dried on the surface of the glassy carbon electrode (GCE). Transmission electron microscopy (TEM), X-ray diffraction (XRD), photoluminescence (PL), Fourier transform infrared spectroscopy (FTIR), UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS) were used for the characterization of structure and morphology of the nanocomposite. Electrochemical characterization was done by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The electrochemical behaviors of arsenic and 2-chlorophenol on different electrodes (GCE, CDs/GCE, and g-C₃N₄/CDs/GCE) was investigated by differential pulse voltammetry (DPV). The results demonstrated that the g-C₃N₄/CDs/GCE significantly enhanced the oxidation peak current of both analytes. The analytes detection sensitivity was greatly improved, suggesting that this new modified electrode has great potential in the determination of trace level of arsenic and 2-chlorophenol. Experimental conditions which affect the electrochemical response of arsenic and 2-chlorophenol were studied, the oxidation peak currents displayed a good linear relationship to concentration for 2-chlorophenol (R²=0.948, n=5) and arsenic (R²=0.9524, n=5), with a linear range from 0.5 to 2.5μM for 2-CP and arsenic and a detection limit of 2.15μM and 0.39μM respectively. The modified electrode was used to determine arsenic and 2-chlorophenol in spiked tap and effluent water samples by the standard addition method, and the results were satisfying. According to the measurement, the new modified electrode is a good alternative as chemical sensor for determination of other phenols.

Keywords: electrochemistry, electrode, limit of detection, sensor

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10684 Strategy and Mechanism for Intercepting Unpredictable Moving Targets in the Blue-Tailed Damselfly (Ischnura elegans)

Authors: Ziv Kassner, Gal Ribak

Abstract:

Members of the Odonata order (dragonflies and damselflies) stand out for their maneuverability and superb flight control, which allow them to catch flying prey in the air. These outstanding aerial abilities were fine-tuned during millions of years of an evolutionary arms race between Odonata and their prey, providing an attractive research model for studying the relationship between sensory input – and aerodynamic output in a flying insect. The ability to catch a maneuvering target in air is interesting not just for insect behavioral ecology and neuroethology but also for designing small and efficient robotic air vehicles. While the aerial prey interception of dragonflies (suborder: Anisoptera) have been studied before, little is known about how damselflies (suborder: Zygoptera) intercept prey. Here, high-speed cameras (filming at 1000 frames per second) were used to explore how damselflies catch unpredictable targets that move through air. Blue-tailed damselflies - Ischnura elegans (family: Coenagrionidae) were introduced to a flight arena and filmed while landing on moving targets that were oscillated harmonically. The insects succeeded in capturing targets that were moved with an amplitude of 6 cm and frequencies of 0-2.5 Hz (fastest mean target speed of 0.3 m s⁻¹) and targets that were moved in 1 Hz (an average speed of 0.3 m s⁻¹) but with an amplitude of 15 cm. To land on stationary or slow targets, damselflies either flew directly to the target, or flew sideways, up to a point in which the target was fixed in the center of the field of view, followed by direct flight path towards the target. As the target moved in increased frequency, damselflies demonstrated an ability to track the targets while flying sideways and minimizing the changes of their body direction on the yaw axis. This was likely an attempt to keep the targets at the center of the visual field while minimizing rotational optic flow of the surrounding visual panorama. Stabilizing rotational optic flow helps in estimation of the velocity and distance of the target. These results illustrate how dynamic visual information is used by damselflies to guide them towards a maneuvering target, enabling the superb aerial hunting abilities of these insects. They also exemplifies the plasticity of the damselfly flight apparatus which enables flight in any direction, irrespective of the direction of the body.

Keywords: bio-mechanics, insect flight, target fixation, tracking and interception

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10683 Aza-Flavanones as Small Molecule Inhibitors of MicroRNA-10b in MDA-MB-231 Breast Cancer Cells

Authors: Debasmita Mukhopadhyay, Manika Pal Bhadra

Abstract:

MiRNAs contribute to oncogenesis either as tumor suppressors or oncogenes. Hence, discovery of miRNA-based therapeutics are imperative to ameliorate cancer. Modulation of miRNA maturation is accomplished via several therapeutic agents, including small molecules and oligonucleotides. Due to the attractive pharmacokinetic properties of small molecules over oligonucleotides, we set to identify small molecule inhibitors of a metastasis-inducing microRNA. Cytotoxicity profile of aza-flavanone C1 was analyzed in a panel of breast cancer cells employing the NCI-60 screen protocols. Flow cytometry, immunofluorescence and western blotting of apoptotic or EMT markers were performed to analyze the effect of C1. A dual luciferase assay unequivocally suggested that C1 repressed endogenous miR-10b in MDA-MB-231 cells. A derivative of aza-flavanone C1 is shown as a strong inhibitor miR-10b. Blockade of miR-10b by C1 resulted in decreased expression of miR-10b targets in an aggressive breast cancer cell line model, MDA-MB-231. Abrogation of TWIST1, an EMT-inducing transcription factor also contributed to C1 mediated apoptosis. Moreover C1 exhibited a specific and selective down-regulation of miR-10b and did not function as a general inhibitor of miRNA biogenesis or other oncomiRs of breast carcinoma. Aza-flavanone congener C1 functions as a potent inhibitor of the metastasis-inducing microRNA, miR-10b. Our present study provides evidence for targeting metastasis-inducing microRNA, miR-10b with a derivative of Aza-flavanone. Better pharmacokinetic properties of small molecules place them as attractive agents compared to nucleic acids based therapies to target miRNA. Further work, in generating analogues based on aza-flavanone moieties will significantly improve the affinity of the small molecules to bind miR-10b. Finally, it is imperative to develop small molecules as novel miRNA-therapeutics in the fight against cancer.

Keywords: breast cancer, microRNA, metastasis, EMT

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10682 Use of Hierarchical Temporal Memory Algorithm in Heart Attack Detection

Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

Abstract:

In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

Procedia PDF Downloads 188
10681 Design of a New Architecture of IDS Called BiIDS (IDS Based on Two Principles of Detection)

Authors: Yousef Farhaoui

Abstract:

An IDS is a tool which is used to improve the level of security.In this paper we present different architectures of IDS. We will also discuss measures that define the effectiveness of IDS and the very recent works of standardization and homogenization of IDS. At the end, we propose a new model of IDS called BiIDS (IDS Based on the two principles of detection).

Keywords: intrusion detection, architectures, characteristic, tools, security

Procedia PDF Downloads 435
10680 Hyperspectral Imaging and Nonlinear Fukunaga-Koontz Transform Based Food Inspection

Authors: Hamidullah Binol, Abdullah Bal

Abstract:

Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: food (ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods

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10679 Wireless Backhauling for 5G Small Cell Networks

Authors: Abdullah A. Al Orainy

Abstract:

Small cell backhaul solutions need to be cost-effective, scalable, and easy to install. This paper presents an overview of small cell backhaul technologies. Wireless solutions including TV white space, satellite, sub-6 GHz radio wave, microwave and mmWave with their backhaul characteristics are discussed. Recent research on issues like beamforming, backhaul architecture, precoding and large antenna arrays, and energy efficiency for dense small cell backhaul with mmWave communications is reviewed. Recent trials of 5G technologies are summarized.

Keywords: backhaul, small cells, wireless, 5G

Procedia PDF Downloads 467