Online Image Research Articles

Detection of Gasoline Adulteration Using Modified Distillation Curves and Artificial Neural Network

AbstractTo detect adulteration in gasoline, an automatic distillation apparatus was set up to measure the recovered volume and temperature simultaneously. The level metering was performed by online image processing instead of the conventional visual operator‐based measurement. To investigate the effect of additives in super gasoline, regular gasoline and diesel were added and the distillation curves were analyzed. The principal component analysis model was employed to reduce the obtained data. Finally, an artificial neural network was applied to predict the volume percentage of adulterants in super gasoline. Statistical analysis showed that the proposed model has a mean relative error and correlation coefficient of 4.6 % and 0.995, respectively.

Análisis cualitativo de la imagen turística online de Zafra (España) a través de los comentarios en Tripadvisor

Los comentarios online realizados por turistas en diferentes plataformas se están convirtiendo en una valiosa fuente de información que permite a los destinos mejorar su posición competitiva mediante un análisis eficiente de dicha información. En el presente trabajo se realiza una aplicación de la técnica de análisis de contenido para el caso concreto de la ciudad de Zafra, Extremadura, utilizando para ello el contenido generado por los visitantes al destino en la plataforma de viajes Tripadvisor, mediante la asistencia del software NVIVO Pro 11. Los resultados obtenidos nos permiten conocer cuáles son los atractivos más comentados, las actividades mejor valoradas o los principales puntos de mejora. Dicho de otra forma, permiten conocer cuál es la imagen online percibida del destino.

Measuring Online Destination Image, Satisfaction, and Loyalty: Evidence from Barcelona Districts

The relationships between destination image and tourist satisfaction and loyalty have been studied extensively through surveys. This study aims to measure these constructs through big data analytics by going one step further in a line of research undertaken 8 years ago. The data source is content generated by travelers and shared on social media regarding the 10 districts of the city of Barcelona (Catalonia): more than 750,000 online travel reviews (OTRs) hosted on the Airbnb platform. This study also explores a relationship demonstrated by numerous researchers through surveys: the impact of destination image on tourist loyalty through satisfaction. However, the results are not satisfactory due to the great weight of the lodging price variable that unbalances the relationship. For example, the first district in the ranking of cognitive image categories is also the first in the ranking of average scores and of positive feelings and moods. However, the last two districts in the ranking of cognitive categories are the first in the rankings of satisfaction, positive recommendations, and cheaper prices. Additionally, the findings show that the location of the accommodation significantly determines the theme of the OTR narrative. Moreover, the results confirm previous studies on the exaggerated positivity of peer-to-peer accommodation scores: only 0.92% of 15,625 rated properties had negative overall scores.

Visual media analysis for Instagram and other online platforms

Instagram is currently the social media platform most associated with online images (and their analysis), but images from other platforms also can be collected and grouped, arrayed by similarity, stacked, matched, stained, labelled, depicted as network, placed side by side and otherwise analytically displayed. In the following, the initial focus is on Instagram, together with certain schools of thought such as Instagramism and Instagrammatics for its aesthetic and visual cultural study. Building on those two approaches, it subsequently focuses on other web and social media platforms, such as Google Image Search, Twitter, Facebook and 4chan. It provides demonstrations of how querying techniques create online image collections, and how these sets are analytically grouped through arrangements collectively referred to as metapictures.

Plasma optical emission spectroscopy based on feedforward neural network

Optical emission spectroscopy (OES) has been widely applied to plasma etching, material processing, development of plasma equipment and technology, as well as plasma propulsion. The collisional-radiative model used in OES is affected by the deviation of fundamental data such as collision cross sections, thus leading to the error in diagnostic results. In this work, a novel method is developed based on feedforward neural network for OES. By comparing the error characteristics of the new method with those of the traditional least-square diagnostic method, it is found that the neural network diagnosis method can reduce the transmission of basic data deviation to the diagnosis results by identifying the characteristics of the spectral vector. This is confirmed by the experimental results. Finally, the mechanism of the neural network algorithm against fundamental data deviation is analyzed. This method also has a good application prospect in plasma parameter online monitoring, imaging monitoring and mass data processing.

Externally triggered smart drug delivery system encapsulating idarubicin shows superior kinetics and enhances tumoral drug uptake and response.

Rationale: Increasing the bioavailable drug level in a tumor is the key to enhance efficacy of chemotherapy. Thermosensitive smart drug delivery systems (SDDS) in combination with local hyperthermia facilitate high local drug levels, thus improving uptake in the tumor. However, inability to rapidly and efficiently absorb the locally released drug results in reduced efficacy, as well as undesired redistribution of the drug away from the tumor to the system.Methods: Based on this paradigm we propose a novel approach in which we replaced doxorubicin (DXR), one of the classic drugs for nanocarrier-based delivery, with idarubicin (IDA), a hydrophobic anthracycline used solely in the free form for treatment hematologic cancers. We established a series of in vitro and in vivo experiments to in depth study the kinetics of SDDS-based delivery, drug release, intratumor biodistribution and subsequent cell uptake.Results: We demonstrate that IDA is taken up over 10 times more rapidly by cancer cells than DXR in vitro. Similar trend is observed in in vivo online imaging and less drug redistribution is shown for IDA, together resulting in 4-times higher whole tumor drug uptake for IDA vs. DXR. Together his yielded an improved intratumoral drug distribution for IDA-SDDS, translating into superior tumor response compared to DXR-SDDS treatment at the same dose. Thus, IDA - a drug that is not used for treatment of solid cancers - shows superior therapeutic index and better outcome when administered in externally triggered SDDS.Conclusions: We show that a shift in selection of chemotherapeutics is urgently needed, away from the classic drugs towards selection based on properties of a chemotherapeutic in context of the nanoparticle and delivery mode, to maximize the therapeutic efficacy.

Volumetric-Modulated Arc Radiotherapy with Daily Image-Guidance Carries Better Toxicity Profile for Higher Risk Prostate Cancer.

Purpose:To compare radiotherapy-induced toxicity for localized prostate-cancer (PCa) treated with versus without daily image-guidance. Patients and Methods:We identified consecutive intermediate and high-risk localized PCa patients treated with definitive radiotherapy using intensity-modulated radiotherapy (IMRT) with variable duration of androgen-deprivation therapy (ADT) within 2015-2016 (Arm-A) and 2005-2007 (Arm-B). Arm-A cases received daily online imaging guidance (IGRT) using cone-beam computed tomography (CBCT) unlike Arm-B candidates with no daily IGRT. After reporting demographic, clinico-pathological features and treatment details, we compared acute (within 3 months post-therapy) and late RT-induced toxicities between study groups graded by RTOG/CTCAE criteria. Uni/multivariate analyses (UVA/MVA) were performed to identify independent predictors for RT-related side-effects. Results:We were able to identify 257 cases who met our inclusion criteria. Overall, median age was 73 years (48-85), 67% had intermediate-risk and 47% received ADT. Arm-A included 72 patients who received IMRT delivered using volumetric-modulated arc therapy (VMAT), whereas, Arm-B was formed of 185 cases who utilized step-and-shoot static IMRT. Clinico-pathological features and treatment details were non-different across study arms except that Arm-A had more Grade Group 3, higher median total dose (79.2 vs. 74 Gy) and more pelvic lymph-nodes RT (P<0.05). Although acute toxicity was similar across groups, Arm-B encountered higher late toxicity score, more intense late genitourinary side-effects (P=0.008), with non-different late lower-gastrointestinal toxicities. On MVA, lack of daily CBCT, African-American race and higher comorbidities were independently predictive for late toxicities. Conclusion:IMRT with daily CBCT permitted safe delivery of dose-escalated IMRT with improved toxicity profile for higher-risk prostate cancer.

Method of microscopic imaging quality evaluation for board lens based on image parameter extraction of pinhole imaging

The modulation transfer function area (MTFA) of pinhole imaging and the MTFA relative variation ratio were used as the image surface parameters to quantitatively evaluate the microscopic imaging quality of the board lens. The light spot images located in different fields of view positions were obtained by using the pinhole imaging method to extract the modulation transfer function (MTF) of microscopic imaging system for board lens. The MTF mathematical model of lens system for board lens was established, and the MTFA in sagittal direction and meridian direction of edge field of view as well as the MTFA relative variation ratio were extracted to measure the microscopic imaging definition and image surface flatness of the measured lens. An experiment which was for measuring the board lens was carried out, and the microscopic imaging performance of the measured lens was quantitatively evaluated by using the proposed model and method. The results show that the Lens 2 can obtain the optimal microscopic imaging quality. The definition value of average power spectrum value (APSV), the gray mean gradient (GMG) and the laplacian summation (LS) were calculated respectively from the RGB bitmap images captured by these measured lens. The results show that the maximum parameter value of the APSV, GMG and LS of Lens 2 bitmap images is equal to 2.720 2, 17.024 4 and 94.921 2, respectively. At the same time, the bitmap images have the highest definition, which is consistent with the image surface parameter evaluation results, indicating that the proposed method used in the quantitative evaluation of microscopic imaging performance of board lens is accurate and effective, which is of great significance and engineering value to improve the online image probe design of the on-line visual ferrograph (OLVF).

Pre-trained CNNs Models for Content based Image Retrieval

Content based image retrieval (CBIR) systems is a ‎common recent method for image retrieval and is ‎based mainly ‎on two pillars extracted features and similarity measures. Low ‎level image presentations, ‎based on colour, texture and shape ‎properties are the most common feature extraction methods used ‎by ‎traditional CBIR systems. Since these traditional handcrafted ‎features require good prior domain ‎knowledge, inaccurate ‎features used for this type of CBIR systems may widen the ‎semantic gap and ‎could lead to very poor performance retrieval ‎results. Hence, features extraction methods, which ‎are ‎independent of domain knowledge and have automatic ‎learning capabilities from input image are ‎highly useful. Recently, ‎pre-trained deep convolution neural networks (CNN) with ‎transfer learning ‎facilities have ability to generate and extract ‎accurate and expressive features from image data. Unlike ‎other ‎types of deep CNN models which require huge amount of data ‎and massive processing time ‎for training purposes, the pre-‎trained CNN models have already trained for thousands of ‎classes of large-scale data, including huge ‎images and their ‎information could be easily used and transferred. ResNet18 ‎and ‎SqueezeNet are successful and effective examples of pre-‎trained CNN models used recently in many ‎machine learning ‎applications, such as classification, clustering and object ‎recognition. In this ‎study, we have developed CBIR systems ‎based on features extracted using ResNet18 and SqueezeNet ‎pre-‎trained CNN models. Here, we have utilized these pre-trained ‎CNN models to extract two groups of features ‎that are stored ‎separately and then later are used for online image searching and ‎retrieval. Experimental ‎results on two popular image datasets ‎Core-1K and GHIM-10K show that ResNet18 features ‎based on ‎the CBIR method have overall accuracy of 95.5% and 93.9% for ‎the two datasets, respectively, which ‎greatly outperformed the ‎traditional handcraft features based on the CBIR method.‎

Time and Space in the Organization of Online Graffiti Art Image Collections

The research presented herein provides insight into the use of chronological and geographical organization in the management of noninstitutional graffiti art image collections online. The use of smartphones and other GPS-enabled technology may lead one to expect precision in the geographic location of images, but this is not the norm when image galleries are often based on submissions from a large variety of sources, a small percentage of which may include the artists of the original works. The fact that graffiti art is very often carried out illegally influences the granularity of geographic information provided by those who submit photos to online graffiti art websites, as well as the willingness of those managing the websites to divulge precise information. Concerning the element of time, images added to the websites also lack precision in the differentiation of when a work was completed, modified, painted over, buffed or otherwise removed, photographed, or added to the image collection. Despite these challenges, time and, even more so, space remain commonly encountered organizational divisions used by those who manage online image galleries of graffiti art.

Digital Cities: Investigating the Image of Lagos as a Fashion City Online Through Web Search Queries

In the digital era, cities have to manage their brand identities on both offline and online platforms. Digital engagement has become an increasingly important strategy for cities to reach out to both locals and visitors as the internet has become a gateway linking billions of people to global careers, investments, tourism and products. Global cities are positioning their brands through elements such as fashion, music and vacation attractions to stand out even online. In recent times, the city of Lagos has been described as an emerging global fashion capital and is becoming more popular for its fashion activities and personalities. This study examines if the online image of the city on the google web search engine aligns with the city’s image as an emerging fashion capital. This research employs the content analysis method, in investigating certain keywords on the google search engine. Results from this study reveal that information regarding the fashion industry in Lagos city is mostly limited to fashion weeks. It is recommended that stakeholders in the Lagos fashion industry increase visibility on online platforms including the google web search engine, which is a key portal for global information. Keywords: keyword search, fashion capitals, Lagos city, digital cities, web search queries DOI: 10.7176/NMMC/99-03 Publication date: October 31 st 2021

Real-time moisture ratio study of drying date fruit chips based on on-line image attributes using kNN and random forest regression methods

The purpose of this study is to eliminate weighing tools for moisture ratio (MR) estimation due to the high noise in the weighing system of Electro-Hydrodynamic (EHD) dryers. For this purpose, the product was photographed at specified time intervals during the drying process. Accordingly, the MR model of date fruit thin layers based on real-time color attributes and the environmental conditions of drying process was derived. Regression models were designed for each method using random forest (RF) and k-nearest neighbor (kNN) algorithms. Hyper-parameter tuning of RF and kNN models was done by random search and full search of parameters space, respectively. The variable space was also reduced in some of the models using these methods. This was done to reduce the output time in order to increase the detection speed. The MR was estimated by RF and kNN models with r2 of 0.976 and 0.959 in test set respectively. Since the accuracy of the predictive model was high, a correlation was found between the color change and the moisture content of date fruits. The overall results showed that by using online imaging, it is possible to accurately estimate the MR of the product in a convective EHD dryer without using any weighing system.

In situ imaging analysis of the inhibition effect of functional coating on the volume expansion of silicon anodes

As a hopeful anode material for lithium ion batteries, the silicon (Si) is faced with the main problems of huge volume expansion and poor conductivity. Reasonable coating design on silicon surface is a key to restrict its volume expansion. Herein, a novel hybrid coating of amorphous titanium dioxide (TiO2) and graphene on Si nanoparticles (named [email protected]2@G) are designed. The amorphous TiO2 and graphene are bonded by C-O, which mitigates the volume change of nano Si, and endows nano Si with high ionic and electronic conductivity. The electrochemical tests reveal that [email protected]2@G delivers high specific capacity of 2970 mA h g−1 after 50 cycles at 0.1 A g−1 and great rate capability of 2200 mA h g−1 after 5 cycles at 1 A g−1. Additionally, real time on-line imaging analysis shows that [email protected]2@G electrode produces only 53% of maximum volume expansion compared with bare Si electrode of 338%. In other words, the functional coatings of amorphous TiO2/graphene can reduce the volume expansion rate of nano silicon by 285%. This work provides a new idea to reduce volume expansion and improve conductivity by designing the special functional coating on nano silicon materials, and shows a feasible approach to quantitatively analyze the dynamic evolution of electrode during charging/discharging process.

Online social image ranking in diversified preferences

Due to the prevalence of social media service, effective and efficient online image retrieval is in urgent need to satisfy diversified requirements of Web users. Previous studies are mainly focusing on bridging the semantic gap by well-established content modeling with semantic information and social tagging information, but they are not flexible in aggregating the diversified expectations of the online users. In this paper, we present OSIR, a solution framework to facilitate the diversified preference styles in online social media image searching by textual query inputs. First, we propose an efficient Online Multiple Kernel Ranking (OMKR) model which is constructed on multiple query dimensions and complimentary feature channels, and trained by minimizing the triplet loss on hard negative samples. By optimizing the ranking performance with multi-dimensional queries, the semantic consistency between the image ranking and textual query input is directly maximized without relying on the intermediate semantic annotation procedure. Second, we construct random walk-based preference modeling by domain-specific similarity calculation on heterogeneous social attributes. By re-ranking the rank output of OMKR based on each preference ranking model, we obtain a set of ranking lists encoding different potential aspects of user preference. Last, we propose an effective and efficient position-sensitive rank aggregation approach to aggregate multiple ranking results based on the user preference specification. Extensive experiment on two social media datasets demonstrates the advantages of our approach in both retrieval performance and user experience.

Abstract 15553: The Long-term Outcome Beyond Pulmonary Vein Isolation of Ablation Guided by the Online and Real-time Atrial Fibrillation Imaging System: Extra Mapping Project

Introduction: Although modulation of atrial fibrillation (AF) drivers and/or substrates has been proposed as one of the effective ablation strategies for non-paroxysmal AF (Non-PAF), previous meta-analyses showed that ablation, targeting complex fractionated atrial electrogram (CFAE) indirectly-reflecting Non-PAF drivers, had no additional benefit for post-pulmonary vein isolation (PVI) Non-PAF. As an alternative ablation strategy for Non-PAF, rotor ablation has been proposed as one of the potentiated ablation strategies for Non-PAF. However, the optimal ablation strategy is still unclear because reliable method detecting AF drivers remain unsettled in clinical practice. Hypothesis: Because rotors are known as key mechanisms for Non-PAF, we might open the possibility of Non-PAF control by the real-time visualization of wave dynamics during AF ablation. Methods: Consecutive 140 Non-PAF patients were enrolled in this study. The clinically-available online and real-time AF imaging system with high-spatiotemporal density (ExTRa Mapping TM ) developed by our group was applied in case Non-PAF was remained after PVI. Then, we detected and ablated non-passively-activated areas where rotational activations in the form of meandering rotors and/or multiple wavelets assumed to contain Non-PAF drivers were frequently observed (figure, top). The recurrence rate of Non-PAF or atrial tachycardia (AT) after the ablation was compared to that in patients underwent PVI-alone at our hospital. Results: The propensity scores selected each 34 people from ExTRa-strategy group and PVI-alone group (figure, middle). The 24-month freedom ratio from Non-PAF/AT in the matched-ExTRa-strategy group was higher than the matched-PVI-alone group, which was close to significance (p = 0.056) (figure, bottom). Conclusion: The ExTRa Mapping-guided ablation for PVI-refractory Non-PAF might be superior to PVI-alone.

Online image monitoring and kinetics study on photocathodic protection of carbon steel using α-Fe2O3 photoanode

Although the photocathodic protection technique is a green and effective metal protection method, the corrosion images monitoring of protected metals and the carrier dynamics of photoanode materials are rarely studied. In this work, we successfully collected the real-time images of carbon steel (CS) by using a particle video microscope probe and verified that the use of a hematite (α-Fe2O3) photoanode could realize the long-term protection of CS under visible-light illumination in a Na2S-containning alkaline solution. In the absence of Na2S, the irradiated α-Fe2O3 electrode could delay but not prevent CS corrosion. Time-resolved spectroscopy investigation revealed that rapid electron/hole recombination via a trapping-detrapping model and slow water oxidation kinetics with a rate constant of 10 s−1 were the main factors limiting the photocathodic protection efficiency of α-Fe2O3. Trap-state-mediated recombination could be reduced by accelerating interfacial hole transfer with Na2S as the hole scavenger. This effect reduced charge transfer resistance by three orders of magnitude relative to water oxidation. Our work showed that photocathodic protection behavior was determined not only by the relative position between the conduction band potential of α-Fe2O3 and the corrosion potential of CS but also by the reaction dynamics on the electrode surface. The acceleration of the interfacial charge transfer of α-Fe2O3 was proposed to be the key to effective photocathodic protection.

PROPOSED METHOD FOR IMAGE SEGMENTATION USING GRAPH THEORY

This paper presents an image segmentation technique using graph tools for object detection. Graph theoretical systems have many good features among different segmentation schemes. It organizes the image elements into mathematically and structural form, and makes the problem formulation more flexible, and the computation more efficient. In this paper, the work, consists of two stages, in the first stage we apply the pixel-based labeling algorithm to the binary image, the algorithm works similarly to the eight connectivity labeling, but it is broader than it in terms of the search area, where two (horizontal and vertical) thresholds are first defined so that the search is in a block whose height is the vertical threshold and its width (2 * horizontal threshold), in the second stage the output of the first stage is mapping into an undirected weighted graph, in which each vertex represent region rather than pixel. We evaluate the results by comparing it with other method using (RI) parameter. We use 50 image from online source and image taken from Berkeley database.

PD-0192: One-year experience with total body irradiation (TBI) using forward IMRT and on-line image guidance

PD-0192: One-year experience with total body irradiation (TBI) using forward IMRT and on-line image guidance

Online multi-core ranking model for image retrieval

With the popularity of social media services, efficient online image retrieval urgently needs to meet the diverse needs of network users. How to use the existing semantic information and social label information to establish a content model to bridge the semantic gap is an urgent problem to be solved. In this article, we propose an efficient online multi-core ranking model (OMKR), which is trained by minimizing the triplet loss of hard negative samples based on multiple query dimensions and complementary feature channels. By optimizing the sorting performance of multi-dimensional queries, the semantic consistency between image sorting and text query input is directly maximized without relying on the intermediate semantic annotation process. A large number of experiments on two social media data sets prove the advantages of our method in terms of retrieval performance.

브이로그의 온라인 리뷰가 고객 구매의도에 미치는 영향- 정교화 가능성 모델을 중심으로

With the increasingly development of the use of social commerce, the internet word-of-mouth marketing has become one of the key factors driving the expansion of the social commerce market and the growth of consumer purchases. This study adopted Elaboration Likelihood Model(ELM) to measure the impact of information quality and source credibility of online reviews about hotel posted on Vlogs on purchase intention, especially the accuracy, abundance of information quality, expertness and popularity of source credibility. In the meanwhile, to confirm the significant role of customer response in the process of making purchase intention. The result of this study suggested that online image reviews posted on Vlogs stimulate consumer affective response and cognitive response, which have a positive impact on purchasing intentions. In addition, this study has shown that consumers care more about sources credibility of review than information quality when making purchasing decisions.