Search results for: aerial imaging and detection

Authors: Chiling Chen, Chiaoying Chou, Siyang Wu

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Taiwan locates at the west of Pacific Ocean and intersects between continental and marine climate. Typhoons frequently strike Taiwan and come with meteorological disasters, i.e., heavy flooding, landslides, loss of life and properties, etc. Global climate change brings more extremely meteorological disasters. So, develop techniques to improve disaster prevention and mitigation is needed, to improve rescue processes and rehabilitations is important as well. In this study, UAVs (Unmanned Aerial Vehicles) are applied to take instant images for improving the disaster investigation and rescue processes. Paddy rice fields in the central Taiwan are the study area. There have been attacked by heavy rain during the monsoon season in June 2016. UAV images provide the high ground resolution (3.5cm) with 3D Point Clouds to develop image discrimination techniques and digital surface model (DSM) on rice lodging. Firstly, image supervised classification with Maximum Likelihood Method (MLD) is used to delineate the area of rice lodging. Secondly, 3D point clouds generated by Pix4D Mapper are used to develop DSM for classifying the lodging levels of paddy rice. As results, discriminate accuracy of rice lodging is 85% by image supervised classification, and the classification accuracy of lodging level is 87% by DSM. Therefore, UAVs not only provide instant images of agricultural damage after the meteorological disaster, but the image discriminations on rice lodging also reach acceptable accuracy (>85%). In the future, technologies of UAVs and image discrimination will be applied to different crop fields. The results of image discrimination will be overlapped with administrative boundaries of paddy rice, to establish GIS-based assist system on agricultural damage discrimination. Therefore, the time and labor would be greatly reduced on damage detection and monitoring.

Keywords: Monsoon, supervised classification, Pix4D, 3D point clouds, discriminate accuracy

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Authors: Alireza Mirrashid, Mohammad Khoshbin, Ali Atghaei, Hassan Shahbazi

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In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: attention, fire detection, smoke detection, spatio-temporal

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Aleksander Nawrat, Karol Jędrasiak, Artur Ryt, Dawid Sobel

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The goal of the article is to present a novel Multimedia Firearms Training System. The system was developed in order to compensate for major problems of existing shooting training systems. The designed and implemented solution can be characterized by five major advantages: algorithm for automatic geometric calibration, algorithm of photometric recalibration, firearms hit point detection using thermal imaging camera, IR laser spot tracking algorithm for after action review analysis, and implementation of ballistics equations. The combination of the abovementioned advantages in a single multimedia firearms training system creates a comprehensive solution for detecting and tracking of the target point usable for shooting training systems and improving intervention tactics of uniformed services. The introduced algorithms of geometric and photometric recalibration allow the use of economically viable commercially available projectors for systems that require long and intensive use without most of the negative impacts on color mapping of existing multi-projector multimedia shooting range systems. The article presents the results of the developed algorithms and their application in real training systems.

Keywords: firearms shot detection, geometric recalibration, photometric recalibration, IR tracking algorithm, thermography, ballistics

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Authors: Chowduru Ramachandra Sharma, Shatunjay Rawat

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A false positive state is when the IDS/IPS identifies an activity as an attack, but the activity is acceptable behavior in the system. False positives in a Network Intrusion Detection System ( NIDS ) is an issue because they desensitize the administrator. It wastes computational power and valuable resources when rules are not tuned properly, which is the main issue with anomaly NIDS. Furthermore, most false positives reduction techniques are not performed during the real-time of attempted intrusions; instead, they have applied afterward on collected traffic data and generate alerts. Of course, false positives detection in ‘offline mode’ is tremendously valuable. Nevertheless, there is room for improvement here; automated techniques still need to reduce False Positives in real-time. This paper uses the Snort signature detection model to redirect the alerted attacks to Honeypots and verify attacks.

Keywords: honeypot, TPOT, snort, NIDS, honeybird, iptables, netfilter, redirection, attack detection, docker, snare, tanner

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Authors: Babatunde Olatujoye, Binbin Yang

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Microwave imaging is a portable, noninvasive, and non-ionizing imaging technique that employs low-power microwave signals to reveal objects in the microwave frequency range. This technique has immense potential for adoption in commercial and scientific applications such as security scanning, material characterization, and nondestructive testing. This work presents a monostatic microwave imaging setup using an Ultra-Wideband (UWB), low-cost, miniaturized Vivaldi antenna with a bandwidth of 1 – 6 GHz. The backscattered signals (S-parameters) of the Vivaldi antenna used for scanning targets were measured in the lab using a VNA. An automated two-dimensional (2-D) scanner was employed for the 2-D movement of the transceiver to collect the measured scattering data from different positions. The targets consist of four metallic objects, each with a distinct shape. Similar setup was also simulated in Ansys HFSS. A high-resolution Back Propagation Algorithm (BPA) was applied to both the simulated and experimental backscattered signals. The BPA utilizes the phase and amplitude information recorded over a two-dimensional aperture of 50 cm × 50 cm with a discreet step size of 2 cm to reconstruct a focused image of the targets. The adoption of BPA was demonstrated by coherently resolving and reconstructing reflection signals from conventional time-of-flight profiles. For both the simulation and experimental data, BPA accurately reconstructed a high resolution 2D image of the targets in terms of shape and location. An improvement of the BPA, in terms of target resolution, was achieved by applying the filtering method in frequency domain.

Keywords: back propagation, microwave imaging, monostatic, vivialdi antenna, ultra wideband

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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

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

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

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Authors: Ajibola Isola, Shattri Mansor, Ojogbane Sani, Olugbemi Tope

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The cadastral map is the rationale behind city management planning and development. For years, cadastral maps have been produced by ground and photogrammetry platforms. Recent evolution in photogrammetry and remote sensing sensors ignites the use of Unmanned Aerial Vehicle systems (UAVs) for cadastral mapping. Despite the time-saving and multi-dimensional cost-effectiveness of the UAV platform, issues related to cadastral map accuracy are a hindrance to the wide applicability of UAVs' cadastral mapping. This study aims to present an approach leading to the generation and assessing the credibility of UAV cadastral mapping. Different sets of Red, Green, and Blue (RGB) photos were obtained from the Tarot 680-hexacopter UAV platform flown over the Universiti Putra Malaysia campus sports complex at an altitude range of 70 m, 100 m, and 250. Before flying the UAV, twenty-eight ground control points were evenly established in the study area with a real-time kinematic differential global positioning system. The second phase of the study utilizes an image-matching algorithm for photos alignment wherein camera calibration parameters and ten of the established ground control points were used for estimating the inner, relative, and absolute orientations of the photos. The resulting orthoimages are exported to ArcGIS software for digitization. Visual, tabular, and graphical assessments of the resulting cadastral maps showed a different level of accuracy. The results of the study show a gradual approach for generating UAV cadastral mapping and that the cadastral map acquired at 70 m altitude produced better results.

Keywords: aerial mapping, orthomosaic, cadastral map, flying altitude, image processing

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. Currently, many children with strabismus remain undiagnosed until school entry because current automated screening methods have limited success in the preschool age range. A method for strabismus detection using eye alignment stability (EAS) is proposed. This method starts with face detection, followed by facial landmark detection, eye region segmentation, eye gaze extraction, and eye alignment stability estimation. Binarization and morphological operations are performed for segmenting the pupil region from the eye. After finding the EAS, its absolute value is used to differentiate the strabismic eye from the non-strabismic eye. If the value of the eye alignment stability is greater than a particular threshold, then the eyes are misaligned, and if its value is less than the threshold, the eyes are aligned. The method was tested on 175 strabismic and non-strabismic images obtained from Kaggle and Google Photos. The strabismic eye is taken as a positive class, and the non-strabismic eye is taken as a negative class. The test produced a true positive rate of 100% and a false positive rate of 7.69%.

Keywords: strabismus, face detection, facial landmarks, eye segmentation, eye gaze, binarization

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Authors: Chawarat Rotejanaprasert, Andrew Lawson

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Early detection of unusual health events depends on the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study.

Keywords: Bayesian, spatial, temporal, surveillance, prospective

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Authors: Ebrahim Taherzadeh, Helmi Z. M. Shafri, Kaveh Shahi

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One of the most important tasks in urban area remote sensing is detection of impervious surface (IS), such as building roof and roads. However, detection of IS in heterogeneous areas still remains as one of the most challenging works. In this study, detection of concrete roof using an object-oriented approach was proposed. A new rule-based classification was developed to detect concrete roof tile. The proposed rule-based classification was applied to WorldView-2 image. Results showed that the proposed rule has good potential to predict concrete roof material from WorldView-2 images with 85% accuracy.

Keywords: object-based, roof material, concrete tile, WorldView-2

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Authors: A. Bhartiya, S. Botchway, M. Yusuf, I. Robinson

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Chromatin condensation is maintained by DNA-based proteins and some divalent cations (Mg²⁺, Ca²⁺, etc.). Condensation process during cell division maintains structural and functional organizations of chromosomes by transferring genetic information correctly to daughter cells. Fluorescence Lifetime Imaging (FLIM) technique measures the fluorescence decay of fixed human chromosomes by calculating the lifetime of fluorophores at a pixel x of the arrival of each photon as a function of time delay t, following excitation with a laser pulse. Fixed metaphase human chromosomes were labelled with DNA-binding dye, DAPI and later DAPI fluorescence lifetime measured using multiphoton microscopy. 5 out of 23 pairs of human chromosomes shown shorter lifetime at the centromere region, differentiating proportion of compaction along the length of chromosomes. Different lifetime was observed in a condensed and de-condensed chromosome. It clearly indicates the involvement of divalent cations in the process of condensation.

Keywords: divalent cations, FLIM (Fluorescence Lifetime Imaging), human chromosomes, multiphoton microscopy

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Zain Ul Hassan, Muhammad Zain Ul Abadin, Muhammad Zubair Khan

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Unmanned aerial vehicles (UAVs) have now become of predominant importance for various operations, and an immense amount of work is going on in this specific category. The structural stability and life of these UAVs is key factor that should be considered while deploying them to different intelligent operations as their failure leads to loss of sensitive real-time data and cost. This paper presents an applied research on the development of a structural health monitoring system for a UAV designed and fabricated by deploying modular approach. Firstly, a modular UAV has been designed which allows to dismantle and to reassemble the components of the UAV without effecting the whole assembly of UAV. This novel approach makes the vehicle very sustainable and decreases its maintenance cost to a significant value by making possible to replace only the part leading to failure. Then the SHM for the designed architecture of the UAV had been specified as a combination of wings integrated with strain gauges, on-board data logger, bridge circuitry and the ground station. For the research purpose sensors have only been attached to the wings being the most load bearing part and as per analysis was done on ANSYS. On the basis of analysis of the load time spectrum obtained by the data logger during flight, fatigue life of the respective component has been predicted using fracture mechanics techniques of Rain Flow Method and Miner’s Rule. Thus allowing us to monitor the health of a specified component time to time aiding to avoid any failure.

Keywords: fracture mechanics, rain flow method, structural health monitoring system, unmanned aerial vehicle

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Authors: Frank Dorrian, Aniket Adhikari

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The placenta accreta spectrum (PAS), which includes placenta accreta, increta, and percreta, is characterized by the abnormal implantation of placental chorionic villi beyond the decidua basalis. Key risk factors include placenta previa, prior cesarean sections, advanced maternal age, uterine surgeries, multiparity, pelvic radiation, and in vitro fertilization (IVF). The incidence of PAS has increased tenfold over the past 50 years, largely due to rising cesarean rates. PAS is associated with significant peripartum and postpartum hemorrhage. Magnetic resonance imaging (MRI) and ultrasound assist in the evaluation of PAS, enabling a multidisciplinary approach to mitigate morbidity and mortality. This study retrospectively analyzed PAS cases at Royal Prince Alfred Hospital, Sydney, Australia. Using the SAR-ESUR joint consensus statement, seven imaging signs were reassessed for their sensitivity and specificity in predicting PAS, with histopathological correlation. The standardized MRI protocols for PAS at the institution were also reviewed. Data were collected from the picture archiving and communication system (PACS) records from 2010 to July 2024, focusing on cases where MR imaging and confirmed histopathology or operative notes were available. This single-center, observational study provides insights into the reliability of MRI for PAS detection and the optimization of imaging protocols for accurate diagnosis. The findings demonstrate that intraplacental dark bands serve as highly sensitive markers for diagnosing PAS, achieving sensitivities of 88.9%, 85.7%, and 100% for placenta accreta, increta, and percreta, respectively, with a combined specificity of 42.9%. Sensitivity for abnormal vascularization was lower (33.3%, 28.6%, and 50%), with a specificity of 57.1%. The placenta bulge exhibited sensitivities of 55.5%, 57.1%, and 100%, with a specificity of 57.1%. Loss of the T2 hypointense interface had sensitivities of 66.6%, 85.7%, and 100%, with 42.9% specificity. Myometrial thinning showed high sensitivity across PAS conditions (88.9%, 71.4%, and 100%) and a specificity of 57.1%. Bladder wall thinning was sensitive only for placenta percreta (50%) but had a specificity of 100%. Focal exophytic mass displayed variable sensitivity (22.9%, 42.9%, and 100%) with a specificity of 85.7%. These results highlight the diagnostic variability among markers, with intraplacental dark bands and myometrial thinning being useful in detecting abnormal placentation, though they lack high specificity. The literature and the results of our study highlight that while no single feature can definitively diagnose PAS, the presence of multiple features -especially when combined with elevated clinical risk- significantly increases the likelihood of an underlying PAS. A thorough understanding of the range of MRI findings associated with PAS, along with awareness of the clinical significance of each sign, helps the radiologist more accurately diagnose the condition and assist in surgical planning, ultimately improving patient care.

Keywords: placenta, accreta, spectrum, MRI

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Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

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Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

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Authors: Olga St. Tsiftsoglou, Diamanto M. Lazari, Eugene L. Kokkalou

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Most of Alyssum species of Brassicaceae family have been mainly studied for their contribution in ecology. In this study, A. alyssoides was examined for its chemical substitutes. The methanol extract of its aerial parts was fractionated with liquid-liquid extraction (distribution) with four different solvents of increasing polarity: diethyl ether, ethyl acetate, 1-butanol and water. The diethyl ether and ethyl acetate extracts were further studied for their chemical composition. So far, secondary metabolites which belong to phenolics were isolated by using several chromatographic methods (C.C. and HPLC) and were identified by using spectroscopic methods (UV/Vis, NMR and MS): two phenolic acids (p-hydroxy-benzoic acid and 3-methoxy-4-hydroxy-benzoic acid (vanillic acid)), and five flavonoids, which are derivatives of flavonol: kaempferol 3-O-β-D-glucopyranoside (astragalin), kaempferol 3-O-(6′′-α-L-rhamnopyranosyl)-β-D-glucopyranoside (nicotiflorin), quercetin 3-O-β-D-glucopyranoside (isoquercetin), isorhamnetin-3-O-β-D-glucopyranoside, and isoramnetin 3-O-(6′′-α-L-rhamnopyranosyl)-β-D-glucopyranoside (narcissin).

Keywords: Alyssum, chemical substitutes, flavonoids, phenolic acids

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Authors: Samaneh Nadali, Masrah Azrifah Azmi Murad, Nurfadhlina Mohammad Sharef

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Sarcasm is a common phenomenon in social media which is a nuanced form of language for stating the opposite of what is implied. Due to the intentional ambiguity, analysis of sarcasm is a difficult task not only for a machine but even for a human. Although sarcasm detection has an important effect on sentiment, it is usually ignored in social media analysis because sarcasm analysis is too complicated. While there is a few systems exist which can detect sarcasm, almost no work has been carried out on a study and the review of the existing work in this area. This survey presents a nearly full image of sarcasm detection techniques and the related fields with brief details. The main contributions of this paper include the illustration of the recent trend of research in the sarcasm analysis and we highlight the gaps and propose a new framework that can be explored.

Keywords: sarcasm detection, sentiment analysis, social media, sarcasm analysis

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Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

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Authors: Fathi Ali Swaid

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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

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Authors: S. N. Derrar, M. Sekkal-Rahal

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The Nonlinear optics (NLO) technique is widely used in the field of biological imaging. In fact, grafting biological entities with a high NLO response on tissues and cells enhances the NLO responses of these latter, and ameliorates, consequently, their biological imaging quality. In this optics, we carried out a theoretical study, in the aim of analyzing the peptide bonds created between alanine amino acid and both unnatural amino acids: L-Dopa and Azatryptophan, respectively. Ramachandran plots have been performed for these systems, and their structural parameters have been analyzed. The NLO responses of these peptides have been reported by calculating the first hyperpolarizability values of all the minima found on the plots. The use of such unnatural amino acids as endogenous probing molecules has been investigated through this study. The Density Functional Theory (DFT) has been used for structural properties, while the Second-order Møller-Plesset Perturbation Theory (MP2) has been employed for the NLO calculations.

Keywords: biological imaging, hyperpolarizability, nonlinear optics, probing molecule

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Authors: Enqing Chen, Jianbo Wang

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It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images.

Keywords: edge detection, NSCT, shift invariant, modulus maxima

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Authors: Fei Ye, Åsa Barrefelt, Manuchehr Abedi-Valugerdi, Khalid M. Abu-Salah, Salman A. Alrokayan, Mamoun Muhammed, Moustapha Hassan

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With appropriate encapsulation in functional nanoparticles drugs are more stable in physiological environment and the kinetics of the drug can be more carefully controlled and monitored. Furthermore, targeted drug delivery can be developed to improve chemotherapy in cancer treatment, not only by enhancing intracellular uptake by target cells but also by reducing the adverse effects in non-target organs. Inorganic imaging agents, delivered together with anti-cancer drugs, enhance the local imaging contrast and provide precise diagnosis as well as evaluation of therapy efficacy. We have developed biodegradable polymeric vesicles as a nanocarrier system for multimodal bio-imaging and anticancer drug delivery. The poly (lactic-co-glycolic acid) PLGA) vesicles were fabricated by encapsulating inorganic imaging agents of superparamagnetic iron oxide nanoparticles (SPION), manganese-doped zinc sulfide (MN:ZnS) quantum dots (QDs) and the anticancer drug busulfan into PLGA nanoparticles via an emulsion-evaporation method. T2-weighted magnetic resonance imaging (MRI) of PLGA-SPION-Mn:ZnS phantoms exhibited enhanced negative contrast with r2 relaxivity of approximately 523 s-1 mM-1 Fe. Murine macrophage (J774A) cellular uptake of PLGA vesicles started fluorescence imaging at 2 h and reached maximum intensity at 24 h incubation. The drug delivery ability PLGA vesicles was demonstrated in vitro by release of busulfan. PLGA vesicles degradation was studied in vitro, showing that approximately 32% was degraded into lactic and glycolic acid over a period of 5 weeks. The biodistribution of PLGA vesicles was investigated in vivo by MRI in a rat model. Change of contrast in the liver could be visualized by MRI after 7 min and maximal signal loss detected after 4 h post-injection of PLGA vesicles. Histological studies showed that the presence of PLGA vesicles in organs was shifted from the lungs to the liver and spleen over time.

Keywords: biodegradable polymers, multifunctional nanoparticles, quantum dots, anticancer drugs

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Authors: Bang Young Kim, Sang Hoon Park, Chul Gyu Song

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A conventional optical coherence tomography (OCT) system has limited imaging depth, which is 1-2 mm, and suffers unwanted noise such as speckle noise. The motorized-stage-based OCT system, using a common-path Fourier-domain optical coherence tomography (CP-FD-OCT) configuration, provides enhanced imaging depth and less noise so that we can overcome these limitations. Using this OCT systems, OCT images were obtained from an onion, and their subsurface structure was observed. As a result, the images obtained using the developed motorized-stage-based system showed enhanced imaging depth than the conventional system, since it is real-time accurate depth tracking. Consequently, the developed CP-FD-OCT systems and algorithms have good potential for the further development of endoscopic OCT for microsurgery.

Keywords: common-path OCT, FD-OCT, OCT, tracking algorithm

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Authors: Juneseok You, Chanho Park, Kuehwan Jang, Sungsoo Na

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Sensing of nanotoxic materials is strongly important, as their engineering applications are growing recently and results in that nanotoxic material can harmfully influence human health and environment. In current study we report the quartz crystal microbalance (QCM)-based, in situ and real-time sensing of nanotoxic-material by frequency shift. We propose the in situ detection of nanotoxic material of zinc oxice by using QCM functionalized with a taget-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz electrode is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated the in-situ and fast detection of zinc oxide using the quartz crystal microbalance (QCM). The detection was derived from the DNA hybridization between the DNA on the quartz electrode. The results suggest that QCM-based detection opens a new avenue for the development of a practical water-testing sensor.

Keywords: nanotoxic material, qcm, frequency, in situ sensing

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Authors: Krzysztof Malczewski

Abstract:

Magnetic Resonance Imaging (MRI) reconstruction algorithm using compressed sensing is presented in this paper. It is exhibited that the offered approach improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging method struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the objective is to combine super-resolution image enhancement algorithm with CS framework benefits to achieve high resolution MR output image. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements.

Keywords: super-resolution, MRI, compressed sensing, sparse-sense, image enhancement

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Authors: Azam Fazelpour, Saeed Reza Dehghani, Vlastimil Masek, Yuri S. Muzychka

Abstract:

The study uses a novel image analysis based on thermal imaging to detect temperature contours created on salt ice surface during transient phenomena. Thermal cameras detect objects by using their emissivities and IR radiance. The ice surface temperature is not uniform during transient processes. The temperature starts to increase from the boundary of ice towards the center of that. Thermal cameras are able to report temperature changes on the ice surface at every individual moment. Various contours, which show different temperature areas, appear on the ice surface picture captured by a thermal camera. Identifying the exact boundary of these contours is valuable to facilitate ice surface temperature analysis. Image processing techniques are used to extract each contour area precisely. In this study, several pictures are recorded while the temperature is increasing throughout the ice surface. Some pictures are selected to be processed by a specific time interval. An image segmentation method is applied to images to determine the contour areas. Color thermal images are used to exploit the main information. Red, green and blue elements of color images are investigated to find the best contour boundaries. The algorithms of image enhancement and noise removal are applied to images to obtain a high contrast and clear image. A novel edge detection algorithm based on differences in the color of the pixels is established to determine contour boundaries. In this method, the edges of the contours are obtained according to properties of red, blue and green image elements. The color image elements are assessed considering their information. Useful elements proceed to process and useless elements are removed from the process to reduce the consuming time. Neighbor pixels with close intensities are assigned in one contour and differences in intensities determine boundaries. The results are then verified by conducting experimental tests. An experimental setup is performed using ice samples and a thermal camera. To observe the created ice contour by the thermal camera, the samples, which are initially at -20° C, are contacted with a warmer surface. Pictures are captured for 20 seconds. The method is applied to five images ,which are captured at the time intervals of 5 seconds. The study shows the green image element carries no useful information; therefore, the boundary detection method is applied on red and blue image elements. In this case study, the results indicate that proposed algorithm shows the boundaries more effective than other edges detection methods such as Sobel and Canny. Comparison between the contour detection in this method and temperature analysis, which states real boundaries, shows a good agreement. This color image edge detection method is applicable to other similar cases according to their image properties.

Keywords: color image processing, edge detection, ice contour boundary, salt ice, thermal image

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Authors: A. S. Lunev, A. A. Larenkov, O. E. Klementyeva, G. E. Kodina

Abstract:

Background and aims: 68Ga-citrate is one of prospective radiopharmaceutical for PET-imaging of inflammation and infection. 68Ga-citrate is 67Ga-citrate analogue using since 1970s for SPECT-imaging. There's known rebinding reaction occurs past Ga-citrate injection and gallium (similar iron Fe3+) binds with blood transferrin. Then radiolabeled protein complex is delivered to pathological foci (inflammation/infection sites). But excessive gallium bindings with transferrin are cause of slow blood clearance, long accumulation time in foci (24-72 h) and exception of application possibility of the short-lived gallium-68 (T½ = 68 min). Injection of additional chemical agents (e.g. Fe3+ compounds) competing with radioactive gallium to the blood transferrin joining (blocking of its metal binding capacity) is one of the ways to solve formulated problem. This phenomenon can be used for correction of 68Ga-citrate pharmacokinetics for increasing of the blood clearance and accumulation in foci. The aim of real studying is research of effect of stable Fe-citrate on 68Ga-citrate tissue distribution. Materials and methods: 68Ga-citrate without/with extra injection of stable Fe-citrate (III) was injected nonlinear mice with inflammation models (aseptic soft tissue inflammation, lung infection, osteomyelitis). PET/X-RAY Genisys4 (Sofie Bioscience, USA) was used for non-invasive PET imaging (for 30, 60, 120 min past injection 68Ga-citrate) with subsequent reconstruction of imaging and their analysis (value of clearance, distribution volume). Scanning time is 10 min. Results and conclusions: I. v. injection of stable Fe-citrate blocks the metal-binding capability of transferrin serum and allows decreasing gallium-68 radioactivity in blood significantly and increasing accumulation in inflammation (3-5 time). It allows receiving more informative PET-images of inflammation early (for 30-60 min after injection). Pharmacokinetic parameters prove it. Noted there is no statistically significant difference between 68Ga-citrate accumulation for different inflammation model because PET imaging is indication of pathological processes and is not their identification.

Keywords: 68Ga-citrate, Fe-citrate, PET imaging, mice, inflammation, infection

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Authors: Oluwole Opeyemi Dina, Saheed E. Elugoke, Peter Olutope Fayemi, Omolola E. Fayemi

Abstract:

This study presents the detection of epinephrine (EP) at Fe₃O₄ modified screen printed silver electrode (SPSE). The iron oxide (Fe₃O₄) nanoparticles were characterized with UV-visible spectroscopy, Fourier-Transform infrared spectroscopy (FT-IR) and Scanning electron microscopy (SEM) prior to the modification of the SPSE. The EP oxidation peak current (Iap) increased with an increase in the concentration of EP as well as the scan rate (from 25 - 400 mVs⁻¹). Using cyclic voltammetry (CV), the relationship between Iap and EP concentration was linear over a range of 3.8 -118.9 µM and 118.9-175 µM with a detection limit of 41.99 µM and 83.16 µM, respectively. Selective detection of EP in the presence of ascorbic acid was also achieved at this electrode.

Keywords: screenprint electrode, iron oxide nanoparticle, epinephrine, serum, cyclic voltametry

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