Recent Trends in Signal and Image Processing
ISSIP 2020
- 2021
- Book
- Editors
- Dr. Siddhartha Bhattacharyya
- Dr. Leo Mršić
- Dr. Maja Brkljačić
- Dr. Joseph Varghese Kureethara
- Prof. Dr. Mario Koeppen
- Book Series
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Nature Singapore
About this book
This book gathers selected papers presented at the Third International Symposium on Signal and Image Processing (ISSIP 2020), organized by the Department of Information Technology, RCC Institute of Information Technology, Kolkata, during March 18–19, 2020. It presents fascinating, state-of-the-art research findings in the field of signal and image processing. It includes conference papers covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis. It addresses various types of signals, such as image, video, speech, non-speech audio, handwritten text, geometric diagram, ECG and EMG signals; MRI, PET and CT scan images; THz signals; solar wind speed signals (SWS); and photoplethysmogram (PPG) signals, and demonstrates how new paradigms of intelligent computing, like quantum computing, can be applied to process and analyze signals precisely and effectively.
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Table of Contents
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Frontmatter
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Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study
Mohamed Issa, A. M. Helmi, Mohamed Abd Elaziz, Siddhartha BhattacharyyaThe chapter delves into the optimization of biological sequence local alignment using Chaotic Ions Motion Optimization (CIMO). It begins by discussing the importance of sequence alignment in bioinformatics and the limitations of the Smith–Waterman algorithm. The authors introduce the FLAT algorithm, which uses meta-heuristic algorithms to fragment sequences and align them more efficiently. The main focus is on the integration of chaotic theory into the Ions Motion Optimization (IMO) algorithm to improve its performance. The chapter presents experimental results using real biological data, including an analysis of COVID-19 protein sequences. The findings demonstrate that CIMO significantly enhances the quality of local sequence alignment, particularly for long sequences. The chapter concludes with a discussion on the future potential of CIMO in bioinformatics and its applications in understanding viral sequences like COVID-19.AI Generated
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AbstractCOVID-19 is a pandemic that broke out throughout the world and has a high mobility to transfer between humans. Developing intelligent bioinformatics tools is a mandatory to aid in the analysis of the disease. One of these tools is local aligner which aims to find the longest common subsequence between two biological sequences. Fragmented local aligner technique (FLAT) was developed based on meta-heuristic algorithms to accelerating the alignment process, especially for sequences with huge length. In this paper, the performance of ions motion optimization (IMO) algorithm for implementing FLAT was measured. The performance was poor, and a chaotic parameter was added in the exploration equations of IMO to enhance its performance for FLAT. A set of real proteins having a product length which ranges from 250,000 to 9,000,000 were used as a dataset to test the performance of IMO and its developed version. Besides, COVID-19 virus was aligned using FLAT according to IMO and chaotic IMO to verify the enhancement of IMO. All results were compared to the results founded by Smith–Waterman approach. The tests prove the superiority of chaotic IMO over IMO for implementing FLAT on all datasets. -
Differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval
Abhishek Dixit, Ashish Mani, Rohit BansalThe chapter discusses an innovative shot boundary detection algorithm that leverages differential evolution (DE) and SVM classifiers for content-based video retrieval. It addresses the critical initial step of video content analysis by segmenting video sequences into shots. The authors compare their approach with existing methods, showcasing improved precision, recall, and F1-score across various datasets. The proposed method optimizes feature selection, leading to enhanced algorithm efficiency and reduced computational cost. Experimental results demonstrate the superior performance of the DE-based approach, making it a significant contribution to the field of video analysis.AI Generated
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AbstractWith the increase of multimedia devices on internet, enormous number of videos are being added as part of digital content. There is a huge challenge in retrieval of these videos as mostly the videos are kept in unstructured form. Intended users try to retrieve video content as per the relevancy and need. Shot boundary detection is a significant and critical approach in the domain of digital video processing. It is the foremost critical job of content-based video retrieval and indexing. In this paper, a novel approach for shot boundary detection algorithm based on Differential evolution algorithm (DE) with SVM classifier is proposed. In this method we first calculate the curves difference of U-component histograms as the feature of difference between video frames. In the next step, Slide-Window Mean Filter to filter difference curves and SVM Classifier applying DE to detect and classify the shot transitions. The well-known TRECVID 2005, 2006, and 2007 datasets are used to test the performance of our proposed approach. The result shows the superior performance of our proposed approach, and this method can achieve high recall, precision rate, accuracy, and better computation time. -
Qutrit-Based Genetic Algorithm for Hyperspectral Image Thresholding
Tulika Dutta, Sandip Dey, Siddhartha Bhattacharyya, Somnath MukhopadhyayThe chapter introduces a qutrit-based genetic algorithm (QutritGA) for the thresholding of hyperspectral images, a process crucial for image segmentation and reducing computational complexity. The algorithm leverages quantum computing principles, specifically the use of qutrits, to enhance search efficiency and achieve superior results. The method involves an interactive information-based band selection technique to choose optimal bands, followed by the application of QutritGA with a new quantum-based mutation operator. The proposed algorithm demonstrates improved performance over classical genetic algorithms and qubit-inspired methods, as evidenced by experimental results on the Salinas Dataset. The chapter concludes with a discussion on the significance of the findings and potential future research directions in the application of quantum computing to image processing.AI Generated
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AbstractThresholding of hyperspectral images is a tedious task. The interactive information value between three bands is used to reduce the redundant bands in the pre-processing stage. A qutrit-inspired genetic algorithm is proposed for thresholding the minimized hyperspectral images with improved quantum genetic operators. In this paper, a quantum disaster operation is implemented to rescue the qutrit-inspired genetic algorithm from getting stuck into local optima. The proposed algorithm produces better results than classical genetic algorithm and qubit-inspired genetic algorithm in most of the cases. -
Employing Parallel Hardware Architectures to Diagnose Sickle Cell Anemia in Real-Time Basis
Mohammed A. Fadhel, Omran Al-ShammaThe chapter introduces a novel approach to diagnose sickle cell anemia using parallel hardware architectures, specifically FPGA and GPU, for real-time processing. The Circle Hough Transform (CHT) technique is employed for accurate cell classification, and the performance of these architectures is compared with traditional CPU-based methods. The FPGA and GPU architectures demonstrate significant improvements in execution time and power efficiency, making them suitable for real-time diagnostic applications. The chapter also discusses the advantages and drawbacks of each architecture, providing valuable insights for professionals in the field of medical diagnostics and biomedical engineering.AI Generated
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AbstractAmong the solid components of the human blood, the type that has the largest number of them is well known as red blood cells (RBCs). These cells have flat-round shapes, where their centers are depressed like a doughnut missing its hole. When the cell shape is changed from circular to sickle, then this case is a blood disease named sickle cell anemia (SCA). Based on its number, the dangerous level is obtained. This paper employs parallel hardware architectures to detect the sickle cells and its dangerous level in a real-time basis. These parallel architectures include the field programmable gated array (FPGA) and the graphical processing unit (GPU). In addition, the central processing unit (CPU) as the common serial architecture is also employed for comparison basis in terms of time consuming and power consumption. The circular Hough Transform (CHT) method is employed for detecting the sickle cells. To determine the dangerous level, the number of sickle cells and the number of normal ones are counted. The detection, counting, and classification algorithms are all coded in the Verilog language (for the FPGA) and in the MATLAB software (for the GPU and CPU). The findings have achieved well-behaved performances and acceptable results are obtained. -
Implementing a Hardware Accelerator to Enhance the Recognition Performance of the Fruit Mature
Mohammed A. Fadhel, Omran Al-ShammaThe chapter delves into the critical role of digital image processing in the food industry, focusing on the recognition of fruit maturity. It introduces two primary techniques—color-threshold and K-means clustering—for classifying ripe apples based on color features. The chapter meticulously explains the image segmentation process, the conversion of color formats, and the implementation of these techniques on FPGA for real-time applications. It also presents a comparative analysis of the two methods, highlighting their strengths and weaknesses. The chapter concludes with a discussion on the execution time and hardware requirements of each technique, offering valuable insights for professionals seeking to optimize fruit maturity recognition systems.AI Generated
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AbstractFor recognizing vegetable and fruit maturing, various techniques have been released in the last two decades. These techniques have different accuracies and are generally time consuming. To speed up the recognition performance to be suitable for real-time basis, a hardware accelerator is needed to implement. This paper introduced a field programmable gate array (FPGA), as a parallel hardware architecture, to solve the problem of time-consuming. Moreover, color-threshold and k-means clustering are two techniques utilized for recognition purposes and for comparison principles. The findings showed that the color-threshold technique required 16% of the total logic elements and performed the recognition task in 10.25 ms. In contrast, the k-means clustering technique required 62% of the logic elements and performed the recognition task in 64.88 ms. Thus, color-threshold technique is more efficient and much faster than the k-means technique. -
Time Series Modelling and Forecasting of Patient Arrivals at an Emergency Department of a Select Hospital
V. Rema, K. SikdarThe chapter delves into the critical management of patient arrivals in an emergency department (ED) using time series modeling and forecasting. It begins by highlighting the stochastic nature of patient arrivals and the necessity for effective forecasting to optimize resource utilization. The study employs descriptive statistics and time series models to analyze and predict patient volumes across different operational shifts. Key methods include the Augmented Dickey-Fuller (ADF) test for stationarity and exponential smoothing models for forecasting. The chapter also reviews relevant literature on technology adoption and mathematical modeling in healthcare, emphasizing the specific application of time series analysis to emergency department operations. By focusing on real-time data from a hospital in Bengaluru, the study provides actionable insights for healthcare administrators seeking to enhance operational efficiency and patient care.AI Generated
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AbstractManaging overcrowding with fluctuating patient arrivals in emergency department (ED) of hospitals requires a quantitative approach to make decisions related to resource planning and deployment by hospital administrators. In this context, analysing patient flow and predicting demand will enable better decision making. In this study, 7748 ED arrivals were recorded from a multi-specialty hospital in Bengaluru. The patient flow in each of the working shifts of the ED was analysed separately. Time series modelling techniques have shown to be useful in generating short-term forecasts. Shift-wise modelling approach has been used since hospital resources were planned according to the shifts. Exponential smoothing techniques proposed by Hyndman were used in this study. Model validation was further carried out along with residual analysis. The prediction intervals shift-wise have been obtained with an average confidence level of 90% which will help hospital management to redeploy resources and handle demand with increased operational efficiency. -
Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma
Smaranjit Ghose, Suhrid Datta, C. Malathy, M. GayathriThe chapter delves into the critical issue of breast cancer, particularly invasive ductal carcinoma (IDC), and its significant impact on global health. It begins by highlighting the alarming statistics of cancer-related deaths and the prevalence of breast cancer. The text then discusses the challenges of traditional diagnostic methods such as mammography, which can be subjective and prone to errors. The main focus is on the innovative application of deep convolutional neural networks (DCNNs) for the automatic diagnosis of IDC. The authors present a comprehensive study using a dataset of breast histopathology images, employing transfer learning with MobileNetV2 to classify cancerous and non-cancerous patches. The proposed model achieves remarkable accuracy, with validation and test accuracies of 94% and 98%, respectively. The chapter also provides a detailed analysis of the model's performance, including precision, recall, and F1 scores, and concludes with the potential of AI in assisting oncologists in early diagnosis and treatment planning.AI Generated
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AbstractThe morbidity rate of breast cancer is among the highest exhibited by all forms of known cancer. It accounts for a high mortality rate in women. Detection in the early stages and corresponding attempts to treatment can help avert the fatality. The most common method used for the screening of breast cancer is mammography. However, the isolation of the breast lesion using this method is quite difficult and requires highly skilled radiologists. The most cardinal form of breast cancer is invasive ductile carcinoma (IDC) where the malignant growth spreads over to the breast fatty tissue after originating from the ducts. The difficulty in the analysis of images for invasive ductile carcinoma, and the time spent on the diagnosis gives rise to the urgent need for an accurate computer-aided diagnosis system. In this effort, we use a deep convolutional neural networks (DCNN) for the automating the detection of invasive ductal carcinoma in the early stages. This would serve as an efficient tool for assisting radiologists in the decision-making process and further save more lives in the long term. -
Speaker Identification in Spoken Language Mismatch Condition: An Experimental Study
Joyanta Basu, Swanirbhar MajumderThe chapter delves into the complexities of speaker identification (SID) in scenarios where there is a mismatch between the languages used during enrollment and testing. Focusing on low-resource languages from East and Northeastern India, the study highlights the degradation of SID performance due to acoustic property mismatches. It introduces a comprehensive experimental study using a newly collected speech corpus from sixteen native languages and two non-native languages. The research explores various state-of-the-art features and classifiers, including MFCC, SDC, GMM, SVM, i-vectors, and DNN models like TDNN and LSTM-RNN. The chapter presents performance evaluations under language mismatch conditions and discusses potential improvements, making it a valuable resource for advancing SID in multilingual and low-resource language environments.AI Generated
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AbstractThis paper describes the impact of spoken language variation in a multilingual speaker identification (SID) system. The development of speech technology applications in low resource languages (LRL) is challenging due to the unavailability of proper speech corpus. This paper illustrates an experimental study of SID on Eastern and Northeastern (E&NE) Indian languages in language mismatch conditions. For this purpose, several experiments are carried out using the LRL data to build speaker identification models. Here, spectral features are explored for investigating the presence of speaker-specific information. Mel frequency cepstral coefficients (MFCCs) and shifted delta cepstral (SDC) are used for representing the spectral information. Gaussian mixture model (GMM) and support vector machine (SVM)-based models are developed to represent the speaker-specific information captured through the spectral features. Apart from that, to build the modern SID i-vectors, time delay neural networks (TDNN), and recurrent neural network with long short-term memory (LSTM-RNN) have been considered. For the evaluation, equal error rate (EER) has been used as a performance matrix of the SID system. Performances of the developed systems are analyzed with native and non-native corpus in terms of speaker identification (SID) accuracy. The best SID performances are observed to be EER 10.52% after the corpus fusion mechanism. -
Ultrasound Image Classification Using ACGAN with Small Training Dataset
Sudipan Saha, Nasrullah SheikhThe chapter explores the application of Auxiliary Classifier Generative Adversarial Networks (ACGANs) for ultrasound image classification, particularly focusing on scenarios with limited training data. It highlights the advantages of combining data augmentation and classifier training within a single framework. The proposed method leverages ACGAN to generate synthetic images and simultaneously train a robust classifier. The approach is validated through experiments on a small dataset of ultrasound images, demonstrating superior performance compared to traditional transfer learning methods. The chapter also discusses the state of the art in ultrasound image analysis and the unique benefits of the ACGAN-based approach, making it a valuable read for professionals in medical imaging and machine learning.AI Generated
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AbstractB-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models require large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work, we exploit auxiliary classifier generative adversarial network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach. -
Assessment of Eyeball Movement and Head Movement Detection Based on Reading
Saadman Sayeed, Farjana Sultana, Partha Chakraborty, Mohammad Abu YousufThis chapter delves into the assessment of eyeball and head movement detection based on reading, highlighting the significance of human-computer interaction in modern technology. It offers a thorough survey of detection methods and presents a system that captures video directly from a PC camera to detect human faces, eye areas, and noses. The system calculates movement data in real-time, generating a CSV file of these data for every minute. The chapter discusses the challenges faced and the potential applications of this technology, including understanding a reader's attention during book reading and diagnosing certain diseases in medicine and optometry. The implementation of a low-cost system using Python, OpenCV, and Dlib is a notable feature, showcasing the feasibility of real-time data collection. The chapter concludes with the system's limitations and suggests future improvements, emphasizing the potential for psychological research and educational applications.AI Generated
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AbstractThe present age is the age of technology. People are increasingly using technology in their daily activities. That is why when we go to consider people’s attention to something, our head detection comes first. Detecting the head is not the only thing that ends here; the thing that is directly related to it is the eyeball movement. For example, when a student is studying, the level of attention means his concentration deeply. And the depth of this attention depends not only on the head movement but also on how many times his eyes are moving from left to right or from right to left. Because it is seen that looking at the same object at a glance does not mean that he is not paying attention, maybe he is thinking of something else or is immersed in another thought. And by using this motivation, eyeball and head movements play a vital role in the study. The system’s goal is to read the video frames and determine the number of eyeballs and head movements in real time. Eyeball and head movements from left to right and right to left are counted per minute. After one minute, the previous data will be refreshed, and new data will be recorded for the next minute. Thus, the system will give us the result of each minute movement numbers, and very nicely, our system can detect eyeballs and head movements in case of reading. -
Using Hadoop Ecosystem and Python to Explore Climate Change
Ivan Ksaver Šušnjara, Tomislav HlupićThe chapter delves into the use of the Hadoop ecosystem and Python for exploring climate change data. It begins by introducing the significance of climate change and the role of human activities in exacerbating it. The study then outlines the data sources used, including global temperature and carbon dioxide emission datasets. The Hadoop ecosystem, particularly Apache Hive and Impala, is highlighted for its capabilities in handling large datasets. The chapter walks through the process of data storage, migration, and transformation using these tools. Additionally, it demonstrates how Python, with libraries like PyHive, Pandas, and Matplotlib, can be used to analyze and visualize climate data. The analysis reveals trends in carbon dioxide emissions and global temperatures, suggesting a correlation between these phenomena. The chapter concludes with a statistical test to confirm the existence of global warming, making it a comprehensive guide for professionals interested in leveraging modern data analysis tools for climate research.AI Generated
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AbstractIn this article, the data storing and processing capabilities of Apache Hadoop ecosystem and its components: Hive, Impala and Sqoop are demonstrated. Also, it demonstrates Python programming language’s capabilities in data analysis, plotting and statistical computing. It does so by exploring climate change problem, one of today’s most relevant and detrimental problems. Apache Sqoop was employed to migrate data from RDBMS system and store it into Hive database, where Hive and Impala were used for data processing and ELT. Finally, data was analyzed using Python, showing strong evidence for global warming presence, as well as exploring relationship between carbon dioxide (CO2) emissions and climate change. -
A Brief Review of Intelligent Rule Extraction Techniques
Abhishek Gunjan, Siddhartha BhattacharyyaThis chapter presents a thorough review of intelligent rule extraction techniques, focusing on their applications in robotic process automation, economic load dispatch, and financial reconciliations. It discusses the limitations of classical approaches and the emergence of intelligent tools like neural networks, fuzzy logic, and evolutionary intelligence. The chapter highlights the advantages of fuzzy logic in representing domain knowledge and creating explainable systems. It also explores various intelligent approaches, including decision trees, support vector machines, fuzzy- and neuro-fuzzy-based techniques, and evolutionary methods. The comparative study of these techniques offers valuable insights into their advantages and limitations, making this chapter a must-read for professionals seeking to understand the latest advancements in rule extraction.AI Generated
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AbstractRule extraction is a process of extracting rules which helps in building domain knowledge. Rules plays an important role in reconciling financial transactions. This paper presents a brief study of intelligent methods for rule extraction. The paper touches upon heuristic, regression, fuzzy-based, evolutionary, and dynamic adaptive techniques for rule extraction. This paper also presents the state-of-the-art techniques used in dealing with numerical and linguistic data for rule extraction. The objective of the paper is to provide directional guidance to researchers working on rule extraction. -
The Effect of Different Feature Selection Methods for Classification of Melanoma
Ananjan Maiti, Biswajoy ChatterjeeThe chapter delves into the crucial role of feature selection in improving the classification of melanoma using skin lesion images. It begins with an introduction to the importance of proper pre-processing and feature extraction techniques, highlighting the significance of texture and shape features. The literature review discusses various studies that have explored these features, including methods like GLCM, LBP, and shape indices. The methodology section outlines the steps taken to collect and pre-process the images, extract features, and apply different feature selection algorithms. The study compares the performance of Gradient Boosting, Particle Swarm Optimization, and Mutual Information methods, demonstrating how each algorithm selects and optimizes features. The results and discussion section presents a detailed analysis of the classifiers' performance with the selected features, highlighting the superior accuracy achieved with the Gradient Boosting method. The conclusion summarizes the findings and their implications for future CAD systems, emphasizing the need for careful feature selection to enhance the classification of melanoma.AI Generated
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AbstractFeatures of skin cancer have a certain impact on computer-aided diagnosis (CAD) systems. Researchers had used different techniques to experience with patterns. The melanoma lesion could also be identified with a different texture, shape, and clinical features. The proposed study has used 22 features of texture, 12 features of shape. The study has exposed three feature selection (FS) techniques like gradient boosting (GB), particle swarm optimization (PSO), and statistical approach. The features are evaluated with these methods and highlighted the effectiveness of each feature for the classification of melanoma. Selected key features have less than the cost of computation. The reduced feature set can make classification better than per the selection of the model. The random forest has the highest performance based on accuracy as it got the highest accuracy of 97.1% on GB feature sets. Decision tree and K-nearest neighbors have shown a decent accuracy of 96.8 and 93.3% on GB feature sets. The study rewards upcoming explorations to select an effective subset of features for machine learning and deep learning techniques. -
Intelligent Hybrid Technique to Secure Bluetooth Communications
Alaa Ahmed Abbood, Qahtan Makki Shallal, Haider Khalaf JabbarThe chapter begins by outlining the basics of Bluetooth technology, its range, data transfer rate, and common devices it connects. It then delves into the significant security weaknesses of Bluetooth, highlighting popular hacking methods such as Blue Jacking, Bluesnarfing, and Blue Bugging. The traditional E0 stream cipher algorithm used for encryption is critiqued for its insufficiency in ensuring data confidentiality. The chapter introduces a proposed hybrid technique that combines Blowfish and MD5 algorithms to enhance Bluetooth communication security. The Blowfish algorithm, a 64-bit block cipher with variable key sizes, and the MD5 hashing algorithm are explained in detail. The mechanism of the proposed method, including encryption and decryption processes at both the sender and receiver sides, is elaborated. The chapter concludes by emphasizing the importance of addressing Bluetooth's security issues and proposes future work to further evaluate the performance of the hybrid technique.AI Generated
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AbstractE0 algorithm is the most popular which used for data transmission Bluetooth communication among devices. E0 is having a 128-bit of symmetric stream cipher key length. Many types of attacks at Bluetooth protocol and cryptanalysis of E0 has proved that it would be broken by using 264 operations. In this work, we have proposed hybrid encryption based on blowfish and md5 algorithms to im-prove the security of transferring data between two computers connected using Bluetooth technique. Because of the advantages of key management of the MD5 algorithm, we used it to encrypt the secret key of Blowfish algorithm which used for encryption of plaintext. Therefore, the proposed hybrid encryption (Blowfish and MD5) will positively improve the data security during communication in Bluetooth media. -
Parallel Algorithm to find Integer k where a given Well-Distributed Graph is k-Metric Dimensional
Xavier Chelladurai, Joseph Varghese KureetharaThe chapter introduces a parallel algorithm to find the integer k for which a given well-distributed graph is k-metric dimensional. It begins by defining key graph theory concepts such as eccentricity, radius, and diameter. The Breadth First Search (BFS) algorithm is then adapted for parallel processing, reducing its time complexity from O(n) to O(log2 n) for well-distributed graphs. The chapter also presents a parallel algorithm to find the positive integer k such that the graph is k-metric dimensional, demonstrating its correctness and efficiency. The algorithms are designed to run on a Parallel Random Access Memory (PRAM) model CRCW, leveraging parallel processors to achieve significant speedups. The chapter concludes with a discussion on the NP-completeness of finding the metric dimension and the open problems in this area.AI Generated
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AbstractNetworks are very important in the world. In signal processing, the towers are modeled as nodes (vertices) and if two towers communicate, then they have an arc (edge) between them or precisely, they are adjacent. The least number of nodes in a network that can uniquely locate every node in the network is known in the network theory as the resolving set of a network. One of the properties that is used in determining the resolving set is the distance between the nodes. Two nodes are at a distance one if there is a single arc can link them whereas the distance between any two random nodes in the network is the least number of distinct arcs that can link them. We propose two algorithms in this paper with the proofs of correctness. The first one is in lines with the BFS that find distance between a designated node to every other node in the network. This algorithm runs in O(log n). The second algorithm is to identify the integer k, such that the given graph is k-metric dimensional. This can be implemented in O(log n) time with O(n2) processors in a CRCW PRAM. -
A Fog-Based Retrieval of Real-Time Data for Health Applications
I. Diana Jeba Jingle, P. Mano PaulThe chapter delves into the challenges of managing data in the Internet of Things (IoT) domain, where the number of connected devices is expected to reach 21 billion by 2025. It introduces fog computing as a superior alternative to cloud computing for handling time-sensitive data, emphasizing its advantages such as reduced latency, distributed architecture, and direct communication with edge devices. The authors present a novel fog computing approach designed to efficiently retrieve real-time patient vitals, comparing its performance with cloud computing through an experimental setup. The proposed model uses sensors to monitor patient data, which is then processed and displayed in both cloud and fog layers. The performance analysis shows that fog computing updates and retrieves data faster than cloud computing, making it a promising solution for real-time healthcare applications. The chapter concludes with a discussion on future research directions, including the integration of fog computing with 5G technology for improved performance.AI Generated
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AbstractFog computing is an emerging technology that offers high-quality cloud services by providing high bandwidth, low latency, and efficient computational power and storage capacity. Although cloud computing is an efficient solution so far to store and retrieve the huge data of IoT devices, it is expected to limit its performance due to low latency and storage capacity. Fog computing addresses these limitations by extending its services to the cloud at the edge of the network. In this paper, we use a fog computing network approach for efficiently retrieving the real-time patient data. The performance of our proposed approach has been compared with the cloud computing approach in terms of retrieval time of real-time data.
- Title
- Recent Trends in Signal and Image Processing
- Editors
-
Dr. Siddhartha Bhattacharyya
Dr. Leo Mršić
Dr. Maja Brkljačić
Dr. Joseph Varghese Kureethara
Prof. Dr. Mario Koeppen
- Copyright Year
- 2021
- Publisher
- Springer Nature Singapore
- Electronic ISBN
- 978-981-336-966-5
- Print ISBN
- 978-981-336-965-8
- DOI
- https://doi.org/10.1007/978-981-33-6966-5
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