Search results for: time-lapse imaging data

Authors: Jarek Krajewski, David Daxberger

Abstract:

We propose a stress detection method based on an RGB camera using heart rate detection, also known as Photoplethysmography Imaging (PPGI). This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. A stationary lab setting with simulated video conferences is chosen using constant light conditions and a sampling rate of 30 fps. The ground truth measurement of heart rate is conducted with a common PPG system. The proposed approach for pulse peak detection is based on a machine learning-based approach, applying brute force feature extraction for the prediction of heart rate pulses. The statistical analysis showed good agreement (correlation r = .79, p<0.05) between the reference heart rate system and the proposed method. Based on these findings, the proposed method could provide a reliable, low-cost, and contactless way of measuring HR parameters in daily-life environments.

Keywords: heart rate, PPGI, machine learning, brute force feature extraction

Procedia PDF Downloads 123

Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Effective traffic management and infrastructure planning are crucial for the development of smart cities and intelligent transportation systems. This study addresses the challenge of data scarcity by generating realistic synthetic traffic data using the PeMS-Bay dataset, improving the accuracy and reliability of predictive modeling. Advanced synthetic data generation techniques, including TimeGAN, GaussianCopula, and PAR Synthesizer, are employed to produce synthetic data that replicates the statistical and structural characteristics of real-world traffic. Future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is planned to capture both spatial and temporal correlations, further improving data quality and realism. The performance of each synthetic data generation model is evaluated against real-world data to identify the best models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are utilized to model and predict complex temporal dependencies within traffic patterns. This comprehensive approach aims to pinpoint areas with low vehicle counts, uncover underlying traffic issues, and inform targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study supports data-driven decision-making that enhances urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory, synthetic data generation, traffic management

Procedia PDF Downloads 23

Authors: Jiwon Lee, Chanho Jung, Si-Hwan Jang, Kyung-Ill Kim, Sanghyun Joo, Wook-Ho Son

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Since the advances in digital imaging technologies have led to development of high quality digital devices, there are a lot of illegal copies of copyrighted video content on the internet. Thus, we propose a high-quality (HQ) video watermarking scheme that can prevent these illegal copies from spreading out. The proposed scheme is applied spatial and temporal gradient methods to improve the fidelity and detection performance. Also, the scheme duplicates the watermark signal temporally to alleviate the signal reduction caused by geometric and signal-processing distortions. Experimental results show that the proposed scheme achieves better performance than previously proposed schemes and it has high fidelity. The proposed scheme can be used in broadcast monitoring or traitor tracking applications which need fast detection process to prevent illegally recorded video content from spreading out.

Keywords: editing prevention technique, gradient method, luminance change, video watermarking

Procedia PDF Downloads 455

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

Procedia PDF Downloads 211

Authors: Suglo Tohari Luri

Abstract:

Data analysis is an important aspect of business performance. With the application of artificial intelligence within databases, selecting a suitable database engine for an application design is also very crucial for business data analysis. The application of business intelligence (BI) software into some relational databases such as Neo4j has proved highly effective in terms of customer data analysis. Yet what remains of great concern is the fact that not all business organizations have the neo4j business intelligence software applications to implement for customer data analysis. Further, those with the BI software lack personnel with the requisite expertise to use it effectively with the neo4j database. The purpose of this research is to demonstrate how the Neo4j program code alone can be applied for the analysis of e-commerce website customer visits. As the neo4j database engine is optimized for handling and managing data relationships with the capability of building high performance and scalable systems to handle connected data nodes, it will ensure that business owners who advertise their products at websites using neo4j as a database are able to determine the number of visitors so as to know which products are visited at routine intervals for the necessary decision making. It will also help in knowing the best customer segments in relation to specific goods so as to place more emphasis on their advertisement on the said websites.

Keywords: data, engine, intelligence, customer, neo4j, database

Procedia PDF Downloads 193

Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

Procedia PDF Downloads 517

Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

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The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

Procedia PDF Downloads 335

Authors: Fan Ye

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Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: RWIS, visibility distance, low visibility, adverse weather

Procedia PDF Downloads 246

Authors: Rahul Malhotra

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

Procedia PDF Downloads 554

Authors: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

Procedia PDF Downloads 64

Authors: Christopher Oyediran, Nicola Ubayasiri, Christopher Gough

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Splenic artery aneurysms are often clinically occult, occasionally identified incidentally with imaging. The pathogenesis of aneurysms is complex, but certain factors are thought to contribute to their development. Given the potential fatal complications of rupture, a high index of suspicion is required to make an early diagnosis. We present a case of a 36-year-old female with a history of endometriosis and multiple sclerosis who presented to the Emergency Department with sudden onset epigastric pain and collapse. On arrival, she was pale and clammy with profound tachycardia and hypotension. An ultrasound done in the resuscitation department revealed abdominal free fluid. She was resuscitated with blood and transferred for emergent laparotomy. Laparotomy revealed massive haemoperitoneum from the spleen. She underwent emergency splenectomy and inspection of the spleen revealed a splenic artery aneurysm. She received our massive transfusion protocol followed by a short stay on ITU, making a good post-operative recovery and was discharged home a week later.

Keywords: aneurysm, human chorionic gonadotrophin (hCG), resuscitation, laparotomy

Procedia PDF Downloads 430

Authors: Alina Muradyan, Lina Babayan, Arsen Nanyan, Gohar Galstyan, Vigen Khachatryan

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We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of ∼> 65000 color templates for comparison with observed objects. The method aims for extracting the information content of object colors in a statistically correct way, and performs a classification as well as a redshift estimation for galaxies and quasars in a unified approach based on the same probability density functions. For the redshift estimation, we employ an advanced version of the Minimum Error Variance estimator which determines the redshift error from the redshift dependent probability density function itself. The method was originally developed for the Calar Alto Deep Imaging Survey (CADIS), but is now used in a wide variety of survey projects. We checked its performance by spectroscopy of CADIS objects, where the method provides high reliability (6 errors among 151 objects with R < 24), especially for the quasar selection, and redshifts accurate within σz ≈ 0.03 for galaxies and σz ≈ 0.1 for quasars. For an optimization of future survey efforts, a few model surveys are compared, which are designed to use the same total amount of telescope time but different sets of broad-band and medium-band filters. Their performance is investigated by Monte-Carlo simulations as well as by analytic evaluation in terms of classification and redshift estimation. If photon noise were the only error source, broad-band surveys and medium-band surveys should perform equally well, as long as they provide the same spectral coverage. In practice, medium-band surveys show superior performance due to their higher tolerance for calibration errors and cosmic variance. Finally, we discuss the relevance of color calibration and derive important conclusions for the issues of library design and choice of filters. The calibration accuracy poses strong constraints on an accurate classification, which are most critical for surveys with few, broad and deeply exposed filters, but less severe for surveys with many, narrow and less deep filters.

Keywords: VO, ArVO, DFBS, FITS, image processing, data analysis

Procedia PDF Downloads 75

Authors: Yasin Kutuk, Bengi Yanik Ilhan

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Previous studies investigate the wage differentiations among regions in Turkey between couples who work in the same industry and those who work in different industries by using the models that is appropriate for cross sectional data. However, since there is no available panel data for this investigation in Turkey, pseudo panels using repeated cross-section data sets of the Household Labor Force Surveys 2004-2014 are employed in order to open a new way to examine wage differentiation patterns. For this purpose, household heads are separated into groups with respect to their household composition. These groups’ membership is assumed to be fixed over time such as age groups, education, gender, and NUTS1 (12 regions) Level. The average behavior of them can be tracked overtime same as in the panel data. Estimates using the pseudo panel data would be consistent with the estimates using genuine panel data on individuals if samples are representative of the population which has fixed composition, characteristics. With controlling the socioeconomic factors, wage differentiation of household income is affected by social, cultural and economic changes after global economic crisis emerged in US. It is also revealed whether wage differentiation is changing among the birth cohorts.

Keywords: wage income, same industry, pseudo panel, panel data econometrics

Procedia PDF Downloads 396

Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

Procedia PDF Downloads 583

Authors: Kerem Ok, Serafettin Senturk, Serdar Aktas, Cem Cevikbas

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Mobile technology is very popular nowadays and it provides a digital world where users can experience many value-added services. Service Providers are also eager to offer diverse value-added services to users such as digital identity, mobile financial services and so on. In this context, the security of data storage in smartphones and the security of communication between the smartphone and service provider are critical for the success of these services. In order to provide the required security functions, the SIM card is one acceptable alternative. Since SIM cards include a Secure Element, they are able to store sensitive data, create cryptographically secure keys, encrypt and decrypt data. In this paper, we design and implement a SIM and a smartphone framework that uses a SIM card for secure key generation, key storage, data encryption, data decryption and digital signing for mobile financial services. Our frameworks show that the SIM card can be used as a controlled Secure Element to provide required security functions for popular e-services such as mobile financial services.

Keywords: SIM card, mobile financial services, cryptography, secure data storage

Procedia PDF Downloads 310

Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

Abstract:

Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: Lie-Sian Yap, Ming-Chien Yang

Abstract:

This study demonstrated a novel injectable hydrogel scaffold composing of Pluronic F127, carboxymethyl hexanoyl chitosan (CA) and glutaraldehyde (GA) for encapsulating human fetal osteoblastic cells (hFOB) 1.19. The hydrogel was prepared by mixing F127 and GA in CA solution at 4°C. The mechanical properties and cytotoxicity of this hydrogel were determined through rheological measurements and MTT assay, respectively. After encapsulation process, the hFOB 1.19 cells morphology was examined using fluorescent and confocal imaging. The results indicated that the Tgel of this system was around 30°C, where sol-gel transformation occurred within 90s and F127/CA/GA gel was able to remain intact in the medium for more than 1 month. In vitro cell culture assay revealed that F127/CA/GA hydrogels were non-cytotoxic. Encapsulated hFOB 1.19 cells not only showed the spherical shape and formed colonies, but also reduced their size. Moreover, the hFOB 1.19 cells showed that cells remain alive after the encapsulation process. Based on these results, these F127/CA/GA hydrogels can be used to encapsulate cells for tissue engineering applications.

Keywords: carboxymethyl hexanoyl chitosan, cell encapsulation, hFOB 1.19, Pluronic F127

Procedia PDF Downloads 241

Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

Procedia PDF Downloads 130

Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

Procedia PDF Downloads 273

Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

Procedia PDF Downloads 237

Authors: Ke Lin, Steve Wu Qing Yang, Zhang Nan

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The medicine and drugs has in the past been manufactured to the final products and then used laboratory analysis to verify their quality. However the industry needs crucially a monitoring technique for the final batch to batch quality check. The introduction of process analytical technology (PAT) provides an incentive to obtain real-time information about drugs on the production line, with the following optical techniques being considered: near-infrared (NIR) spectroscopy, Raman spectroscopy and imaging, mid-infrared spectroscopy with the use of chemometric techniques to quantify the final product. However, presents problems in that the spectra obtained will consist of many combination and overtone bands of the fundamental vibrations observed, making analysis difficult. In this work, we describe a non-destructive system and method for capsule and liquid medicine identification, more particularly, using terahertz time-domain spectroscopy and/or designed terahertz portable system for identifying different types of medicine in the package of capsule or in liquid medicine bottles. The target medicine can be detected directly, non-destructively and non-invasively.

Keywords: terahertz, non-destructive, non-invasive, chemical identification

Procedia PDF Downloads 129

Authors: Razak Zakariya, Fazliana Mustajap, Lenny Sharinee Sakai

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A sediment map is quite important in the marine environment. The sediment itself contains thousands of information that can be used for other research. This study was conducted by using a multibeam echo sounder Reson T20 on 15 August 2020 at the Karang Sela (coral reef area) at Pulau Bidong. The study aims to identify the sediment type around the coral reef by using bathymetry and backscatter data. The sediment in the study area was collected as ground truthing data to verify the classification of the seabed. A dry sieving method was used to analyze the sediment sample by using a sieve shaker. PDS 2000 software was used for data acquisition, and Qimera QPS version 2.4.5 was used for processing the bathymetry data. Meanwhile, FMGT QPS version 7.10 processes the backscatter data. Then, backscatter data were analyzed by using the maximum likelihood classification tool in ArcGIS version 10.8 software. The result identified three types of sediments around the coral which were very coarse sand, coarse sand, and medium sand.

Keywords: sediment type, MBES echo sounder, backscatter, ArcGIS

Procedia PDF Downloads 84

Authors: Sedat Bilgili, Alper K. Demir

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The current Internet has become the dominant use with continuing growth in the home, medical, health, smart cities and industrial automation applications. Internet of Things (IoT) is an emerging technology to enable such applications in our lives. Moreover, Named Data Networking (NDN) is also emerging as a Future Internet architecture where it fits the communication needs of IoT networks. The aim of this study is to provide an NDN protocol stack implementation running on the Contiki operating system (OS). Contiki OS is an OS that is developed for constrained IoT devices. In this study, an NDN protocol stack that can work on top of IEEE 802.15.4 link and physical layers have been developed and presented.

Keywords: internet of things (IoT), named-data, named data networking (NDN), operating system

Procedia PDF Downloads 169

Authors: Tan G. K. Y., Chew M. S. J., Sajeev S., Vellasamy A.

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Injuries of the extensor hallucis longus (EHL) tendon are a rare phenomenon, with most occurring due to lacerations or penetrating injuries. Closed traumatic ruptures of the EHL are described as “Mallet injuries of the toe”. These can be classified as bony or soft mallet injuries depending on the presence or absence of a fracture at the insertion site of the EHL tendon in the distal phalanx. We present a case of a 33-year-old woman who presented with a hyperflexion injury to the left big toe with an inability to extend the big toe. Ultrasound showed a complete rupture of the EHL tendon with retraction proximal to the hallucal interphalangeal joint of the big toe. The patient was treated through transarticular pinning and repair using the Arthrex Mini Bio-Suture Tak with a 2-0 fibre wire. Six months postoperatively, the patient had symmetrical EHL power and full range of motion of the toe. The lessons to be drawn from this case report are that isolated hallux mallet injuries are rare and can be easily missed in the absence of penetrating wounds. Patients who have such injuries should be investigated early with the appropriate imaging techniques, such as ultrasound or MRI, and treated surgically.

Keywords: hallux mallet, extensor hallucis longus tendon, extensor hallucis longus

Procedia PDF Downloads 78

Authors: S. Alireza Mirghasemi, Elly Trepman, Mohammad Saleh Sadeghi, Narges Rahimi Gabaran, Shervin Rashidinia

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Bone marrow edema syndrome (BMES) is an uncommon and self-limited syndrome characterized by atraumatic extremity pain with unknown of etiology. Symptom onset may include sudden or gradual swelling and pain at rest or during activity, usually at night. This syndrome mostly affects middle-aged men and younger women who have pain in the lower extremities. The most common sites involved with BMES, in decreasing order of frequency, are the bones about the hip, knee, ankle, and foot. The diagnosis of BMES is made with magnetic resonance imaging to exclude other causes of bone marrow edema. The correct diagnosis often is delayed because of the low prevalence and nonspecific signs in the foot and ankle. This delay may intensify bone pain and impair patient function and quality of life. The goal of BMES treatment is to relieve pain and shorten disease duration. Treatment options are limited and may include symptomatic treatment, pharmacologic treatment, and surgery.

Keywords: transient osteoporosis, bone marrow edema syndrome, iloprost, bisphosphonates

Procedia PDF Downloads 360

Authors: Eun-Joong Kim, Sankarprasad Bhuniya, Hyunseung Lee, Hyun Min Kim, Chaejoon Cheong, Su-khendu Maiti, Kwan Soo Hong, Jong Seung Kim

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Metastatic cancers have historically been difficult to treat. However, metastatic tumors have been found to have high levels of reactive oxygen species such as hydrogen peroxide (H2O2), supporting the hypothesis that a prodrug could be activated by intracellular H2O2 and lead to a potential anti-metastatic therapy. In this study, prodrug 7 was designed to be activated by H2O2-mediated boronate oxidation, resulting in activation of the fluorophore for detection and release of the therapeutic agent, SN-38. Drug release from prodrug 7 was investigated by monitoring fluorescence after addition of H2O2 to the cancer cells. Prodrug 7 activated by H2O2 selectively inhibited tumor cell growth. Furthermore, intratracheally administered prodrug 7 showed effective anti-tumor activity in a mouse model of metastatic lung disease. Thus, this H2O2-responsive prodrug has therapeutic potential as a novel treatment for metastatic cancer via cellular imaging with fluorescence as well as selective release of the anti-cancer drug, SN-38.

Keywords: hydrogen peroxide, prodrug, metastatic tumors, fluorescence

Procedia PDF Downloads 452

Authors: A. Matiushkina, A. Bazhenova, I. Litvinov, E. Kornilova, A. Dubavik, A. Orlova

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Magnetic-luminescent complexes based on superparamagnetic iron oxide nanoparticles (SPIONs) and semiconductor quantum dots (QDs) have been recognized as a new class of materials that have high potential in modern medicine. These materials can serve for theranostics of oncological diseases, and also as a target agent for drug delivery. They combine the qualities characteristic of magnetic nanoparticles, that is, magneto-controllability and the ability to local heating under the influence of an external magnetic field, as well as phosphors, due to luminescence of which, for example, early tumor imaging is possible. The complexity of creating complexes is the energy transfer between particles, which quenches the luminescence of QDs in complexes with SPIONs. In this regard, a relatively new type of alloyed (CdₓZn₁₋ₓSeᵧS₁₋ᵧ)-ZnS QDs is used in our work. The presence of a sufficiently thick gradient semiconductor shell in alloyed QDs makes it possible to reduce the probability of energy transfer from QDs to SPIONs in complexes. At the same time, Forster Resonance Energy Transfer (FRET) is a perfect instrument to confirm the formation of complexes based on QDs and different-type energy acceptors. The formation of complexes in the aprotic bipolar solvent dimethyl sulfoxide is ensured by the coordination of the carboxyl group of the stabilizing QD molecule (L-cysteine) on the surface iron atoms of the SPIONs. An analysis of the photoluminescence (PL) spectra has shown that a sequential increase in the SPIONs concentration in the samples is accompanied by effective quenching of the luminescence of QDs. However, it has not confirmed the formation of complexes yet, because of a decrease in the PL intensity of QDs due to reabsorption of light by SPIONs. Therefore, a study of the PL kinetics of QDs at different SPIONs concentrations was made, which demonstrates that an increase in the SPIONs concentration is accompanied by a symbatic reduction in all characteristic PL decay times. It confirms the FRET from QDs to SPIONs, which indicates the QDs/SPIONs complex formation, rather than a spontaneous aggregation of QDs, which is usually accompanied by a sharp increase in the percentage of the QD fraction with the shortest characteristic PL decay time. The complexes have been studied by the magnetic circular dichroism (MCD) spectroscopy that allows one to estimate the response of magnetic material to the applied magnetic field and also can be useful to check SPIONs aggregation. An analysis of the MCD spectra has shown that the complexes have zero residual magnetization, which is an important factor for using in biomedical applications, and don't contain SPIONs aggregates. Cell penetration, biocompatibility, and stability of QDs/SPIONs complexes in cancer cells have been studied using HeLa cell line. We have found that the complexes penetrate in HeLa cell and don't demonstrate cytotoxic effect up to 25 nM concentration. Our results clearly demonstrate that alloyed (CdₓZn₁₋ₓSeᵧS₁₋ᵧ)-ZnS QDs can be successfully used in complexes with SPIONs reached new hybrid nanostructures, which combine bright luminescence for tumor imaging and magnetic properties for targeted drug delivery and magnetic hyperthermia of tumors. Acknowledgements: This work was supported by the Ministry of Science and Higher Education of Russian Federation, goszadanie no. 2019-1080 and was financially supported by Government of Russian Federation, Grant 08-08.

Keywords: alloyed quantum dots, magnetic circular dichroism, magneto-luminescent complexes, superparamagnetic iron oxide nanoparticles

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Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

Abstract:

Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

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Authors: Langhit Kurar, Loren Charles

Abstract:

Introduction: In light of recent BOAST 12 (August 2016) published guidance on management of ankle fractures, the project aimed to highlight key discrepancies throughout the care trajectory from admission to point of discharge at a district general hospital. Wide breadth of data covering three key domains: accident and emergency, radiology, and orthopaedic surgery were subsequently stratified and recommendations on note documentation, and outpatient follow up were made. Methods: A retrospective twelve month audit was conducted reviewing results of ankle fracture management in 37 patients. Inclusion criterion involved all patients seen at Darent Valley Hospital (DVH) emergency department with radiographic evidence of an ankle fracture. Exclusion criterion involved all patients managed solely by nursing staff or having sustained purely ligamentous injury. Medical notes, including discharge summaries and the PACS online radiographic tool were used for data extraction. Results: Cross-examination of the A & E domain revealed limited awareness of the BOAST 12 recent publication including requirements to document skin integrity and neurovascular assessment. This had direct implications as this would have changed the surgical plan for acutely compromised patients. The majority of results obtained from the radiographic domain were satisfactory with appropriate X-rays taken in over 95% of cases. However, due to time pressures within A & E, patients were often left without a post manipulation XRAY in a backslab. Poorly reduced fractures were subsequently left for a long period resulting in swollen ankles and a time-dependent lag to surgical intervention. This had knocked on implications for prolonged inpatient stay resulting in hospital-acquired co-morbidity including pressure sores. Discussion: The audit has highlighted several areas of improvement throughout the disease trajectory from review in the emergency department to follow up as an outpatient. This has prompted the creation of an algorithm to ensure patients with significant fractures presenting to the emergency department are seen promptly and treatment expedited as per recent guidance. This includes timing for X-rays taken in A & E. Re-audit has shown significant improvement in both documentation at time of presentation and appropriate follow-up strategies. Within the orthopedic domain, we are in the process of creating an ankle fracture pathway to ensure imaging and weight bearing status are made clear to the consulting clinicians in an outpatient setting. Significance/Clinical Relevance: As a result of the ankle fracture algorithm we have adapted the BOAST 12 guidance to shape an intrinsic pathway to not only improve patient management within the emergency department but also create a standardised format for follow up.

Keywords: ankle, fracture, BOAST, radiology

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

Abstract:

Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

Procedia PDF Downloads 475