This document summarizes a study that analyzed seismic signals and detected abnormalities. The study proposed a noise suppression method using bandpass filtering, IIR Wiener filtering, and event detection using recursive Short-Term Average (STA)/Long Term Average (LTA) and Carl STA/LTA algorithms. The methods were applied to broadband seismic data from two stations in India to distinguish seismic and non-seismic events and determine the magnitude and time of actual earthquakes. Frequency domain analysis showed abnormal signal activities before earthquakes between 1-4Hz. Recursive STA/LTA and Carl STA/LTA were used to record event times and signal-to-noise ratios were computed at different processing stages to classify signals
Earthquake trend prediction using long short-term memory RNNIJECEIAES
The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Algorithm performance comparison for earthquake signal recognition on smartph...TELKOMNIKA JOURNAL
Micro-electro-mechanical-system accelerometer is able to detect acceleration signal caused by earthquake. Such type of accelerometer is also used by smartphones. There are few algorithms that can be used to recognize the type of acceleration signal from smartphone. This study aims to find signal recognition algorithm in order to consider the most proper algorithm for earthquake signal detection. The initial stage of designing the recognizer is data collection for each type of signal classification. The next step is to apply a highpass filter to separate the signals collected from the gravitational acceleration signal. The signal is divided into several segments. The system will extract features of each signal segment in the time and frequency domain. Each signal segment is then classified according to the type of signal using the classifier through a series of training data processes. The classifier which has the highest accuracy value is exported into the new input signal modeling. As the result, fine K-NN algorithm has the highest level of accuracy in the classification. The fine K-NN algorithm has an accuracy rate of 99.75% in the classification of human activity signals and earthquake signals with a memory capacity of 6,044 kilobytes and processing time of 15.93 seconds. This algorithm has the best classifier criteria compared to decision tree, support vector machine and linear discriminant analysis algorithms.
This paper introduces the Artifi cial Neural Networks (ANN) function to model probabilistic dependencies, in supervised classification tasks for discrimination between earthquakes and explosions problems. ANNs are regarded as the discriminating tools to classify the natural seismic events (earthquakes) from the artifi cial ones (Man-made explosions) based on the seismic signals recorded at regional distances. The bulk of our novel is to improve the obtained numerical results using this advance technique. The ANNs, by testing the different types of seismic features, showed the potential application of this method to discriminate the classes. During the above study, we found out that the Neural Networks have been used in a fully innovative manner in this work. Here the ARMA coefficients filters detects
the type of the source whenever a natural or artificial source changes the nature of the background noise of the seismograms. During the above study, we found out that this algorithm is sometimes capable to alarm the further natural seismological events just a little before the onset.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
ABSTRACT: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition Monitoring with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices, possible future trends of Condition Monitoring with a brief outline on the novelty of the current research work.
Earthquake trend prediction using long short-term memory RNNIJECEIAES
The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Algorithm performance comparison for earthquake signal recognition on smartph...TELKOMNIKA JOURNAL
Micro-electro-mechanical-system accelerometer is able to detect acceleration signal caused by earthquake. Such type of accelerometer is also used by smartphones. There are few algorithms that can be used to recognize the type of acceleration signal from smartphone. This study aims to find signal recognition algorithm in order to consider the most proper algorithm for earthquake signal detection. The initial stage of designing the recognizer is data collection for each type of signal classification. The next step is to apply a highpass filter to separate the signals collected from the gravitational acceleration signal. The signal is divided into several segments. The system will extract features of each signal segment in the time and frequency domain. Each signal segment is then classified according to the type of signal using the classifier through a series of training data processes. The classifier which has the highest accuracy value is exported into the new input signal modeling. As the result, fine K-NN algorithm has the highest level of accuracy in the classification. The fine K-NN algorithm has an accuracy rate of 99.75% in the classification of human activity signals and earthquake signals with a memory capacity of 6,044 kilobytes and processing time of 15.93 seconds. This algorithm has the best classifier criteria compared to decision tree, support vector machine and linear discriminant analysis algorithms.
This paper introduces the Artifi cial Neural Networks (ANN) function to model probabilistic dependencies, in supervised classification tasks for discrimination between earthquakes and explosions problems. ANNs are regarded as the discriminating tools to classify the natural seismic events (earthquakes) from the artifi cial ones (Man-made explosions) based on the seismic signals recorded at regional distances. The bulk of our novel is to improve the obtained numerical results using this advance technique. The ANNs, by testing the different types of seismic features, showed the potential application of this method to discriminate the classes. During the above study, we found out that the Neural Networks have been used in a fully innovative manner in this work. Here the ARMA coefficients filters detects
the type of the source whenever a natural or artificial source changes the nature of the background noise of the seismograms. During the above study, we found out that this algorithm is sometimes capable to alarm the further natural seismological events just a little before the onset.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
ABSTRACT: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition Monitoring with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices, possible future trends of Condition Monitoring with a brief outline on the novelty of the current research work.
OFFLINE SPIKE DETECTION USING TIME DEPENDENT ENTROPY ijbesjournal
Analysis of the neuronal activities is essential in studying nervous system mechanisms.True interpretation of such mechanisms relies on the detection of the neuronal activities, which appear as action potentials or spikes in recorded neural data. So far several algorithms have been developed for spike detection. In this paper such issue is addressed using entropy measures.Transient events like spikes affect the entropy content of a signal. Thus, a time-dependent entropy framework can be used for spike detection where the entropy of each windowed segment of neural data is computed based on a generalized form of entropy. Detection method is tested on different signal to noise ratios. The results show that the time-dependent entropy method in comparison with available methods enables us to detect spikes in their exact time of occurrence with relatively lower false alarm rate.
Recovery of aftershock sequences using waveform cross correlation: from catas...Ivan Kitov
Description of a software package for signal detection and association using waveform cross correlation. Recovery of aftershock sequences of the largest events: Sumatra 2004 and Tohoku 2011. Finding of a small aftershock of the September 9, 2016 DPRK test.
Photoacoustic technology for biological tissues characterizationjournalBEEI
The existing photoacoustics (PA) imaging systems showed mixed performance in imaging characteristic and signal-to-noise ratio (SNR). This work presents the use of an in-house assembled PA system using a modulating laser beam of wavelength 633 nm for two-dimensional (2D) characterization of biological tissues. The differentiation of the tissues in this work is based on differences in their light absorption, wherein the produced photoacoustic signal detected by a transducer was translated into phase value that corresponds to the peak amplitude of optical absorption of tissue namely fat, liver and muscle. This work found fat tissue to produce the strongest PA signal with mean ± standard deviation (SD) phase value = 2.09 ± 0.31 while muscle produced the least signal with phase value = 1.03 ± 0.17. This work discovered the presence of stripes pattern in the reconstructed images of fat and muscle resulted from their structural properties. In addition, a comparison is made in an attempt to better assess the performance of the developed system with the related ones. This work concluded that the developed system may use as an alternative, noninvasive and label-free visualization method for characterization of biological tissues in the future.
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...IJCSEIT Journal
The exploration of oceans and sea beds is being made increasingly possible through the development of
Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it
must confront the existence of notable challenges. However, an automatic detecting and tracking system is
the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of
Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was
extracted by threshold segment and morphology process, and the features of invariant moment and area
were analysed. Results show that the method presented has the advantages of good robustness, high
accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images
and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained
underwater environment.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
Granular Mobility-Factor Analysis Framework for enrichingOccupancy Sensing wi...IJECEIAES
With the growing need for adoption of smarter resource control system in existing infrastructure, the proliferation of occupancy sensing is slowly increasing its pace. After reviewing an existing system, we find that utilization of Doppler radar is less progressive in enhancing the accuracy of occupancy sensing operation. Therefore, we introduce a novel analytical model that is meant for incorporating granularity in tracing the psychological periodic characteristic of an object by emphasizing on the mobility and uncertainty movement of an object in the monitoring area. Hence, the model is more emphasized on identifying the rate of change in any periodic physiological characteristic of an object with the aid of mathematical modelling. At the same time, the model extracts certain traits of frequency shift and directionality for better tracking of the unidentified object behavior where its applicabilibility can be generalized in majority of the fields related to object detection.
HOME APPLIANCE IDENTIFICATION FOR NILM SYSTEMS BASED ON DEEP NEURAL NETWORKSijaia
This paper presents the proposal for the identification of residential equipment in non-intrusive load
monitoring systems. The system is based on a Convolutional Neural Network to classify residential
equipment. As inputs to the system, transient power signal data obtained at the time an equipment is
connected in a residence is used. The methodology was developed using data from a public database
(REED) that presents data collected at a low frequency (1 Hz). The results obtained in the test database
indicate that the proposed system is able to carry out the identification task, and presented satisfactory
results when compared with the results already presented in the literature for the problem in question.
HMM Classifier for Human Activity RecognitionCSEIJJournal
The rapid improvement in technology causes more attention towards to Recognizing of human activities
from video. These new technological growth has made vision-based research much more interesting and
efficient than ever before. This paper present novel HMM (Hidden Markov Model) based approach for
Human activity recognition from video. There are different approaches of HMM to recognize action of
human from video. Like threshold and voting to automatically and effectively segment and recognize
complex activities, segment and recognize complex activities and for simple activities we use Elman
Network (EN) and two hybrids of Neural Network (NN) and HMM, i.e. HMM-NN and NN-HMM.
IOT SOLUTIONS FOR SMART PARKING- SIGFOX TECHNOLOGYCSEIJJournal
Sigfox technology has emerged as a competitive product in the communication service provider market for
approximately a decade. Widely implemented for smart parking solutions across various European
countries, it has now gained traction in Germany as well. The technology's successful track record and
reputation in the market demonstrate its effectiveness and reliability in addressing the communication
needs of IoT applications, particularly in the context of vehicle parking systems. This is noted in terms of a
city like Berlin-Germany, for on which the study is conducted. The major challenge being on how to relate
the parking techniques in a more user friendly, cost effective and less energy consumpmti0n mode where
the questions had at the beginning of the paper, relatively at the end the answers are sought to it via Sigfox
and its comparison with other related technologies like LoRA WAN and weightless. But more so future
areas of research study is also pointed out on areas which are not clearly identified in this particular
research area.
This paper entails the pros, cons adaptive, emerging and existing technology study in terms of cloud, big
data, Data analytics are all discussed in tandem to Sigfox.
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Analysis of the neuronal activities is essential in studying nervous system mechanisms.True interpretation of such mechanisms relies on the detection of the neuronal activities, which appear as action potentials or spikes in recorded neural data. So far several algorithms have been developed for spike detection. In this paper such issue is addressed using entropy measures.Transient events like spikes affect the entropy content of a signal. Thus, a time-dependent entropy framework can be used for spike detection where the entropy of each windowed segment of neural data is computed based on a generalized form of entropy. Detection method is tested on different signal to noise ratios. The results show that the time-dependent entropy method in comparison with available methods enables us to detect spikes in their exact time of occurrence with relatively lower false alarm rate.
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Description of a software package for signal detection and association using waveform cross correlation. Recovery of aftershock sequences of the largest events: Sumatra 2004 and Tohoku 2011. Finding of a small aftershock of the September 9, 2016 DPRK test.
Photoacoustic technology for biological tissues characterizationjournalBEEI
The existing photoacoustics (PA) imaging systems showed mixed performance in imaging characteristic and signal-to-noise ratio (SNR). This work presents the use of an in-house assembled PA system using a modulating laser beam of wavelength 633 nm for two-dimensional (2D) characterization of biological tissues. The differentiation of the tissues in this work is based on differences in their light absorption, wherein the produced photoacoustic signal detected by a transducer was translated into phase value that corresponds to the peak amplitude of optical absorption of tissue namely fat, liver and muscle. This work found fat tissue to produce the strongest PA signal with mean ± standard deviation (SD) phase value = 2.09 ± 0.31 while muscle produced the least signal with phase value = 1.03 ± 0.17. This work discovered the presence of stripes pattern in the reconstructed images of fat and muscle resulted from their structural properties. In addition, a comparison is made in an attempt to better assess the performance of the developed system with the related ones. This work concluded that the developed system may use as an alternative, noninvasive and label-free visualization method for characterization of biological tissues in the future.
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER RO...IJCSEIT Journal
The exploration of oceans and sea beds is being made increasingly possible through the development of
Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it
must confront the existence of notable challenges. However, an automatic detecting and tracking system is
the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of
Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was
extracted by threshold segment and morphology process, and the features of invariant moment and area
were analysed. Results show that the method presented has the advantages of good robustness, high
accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images
and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained
underwater environment.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
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This paper presents the proposal for the identification of residential equipment in non-intrusive load
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The rapid improvement in technology causes more attention towards to Recognizing of human activities
from video. These new technological growth has made vision-based research much more interesting and
efficient than ever before. This paper present novel HMM (Hidden Markov Model) based approach for
Human activity recognition from video. There are different approaches of HMM to recognize action of
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approximately a decade. Widely implemented for smart parking solutions across various European
countries, it has now gained traction in Germany as well. The technology's successful track record and
reputation in the market demonstrate its effectiveness and reliability in addressing the communication
needs of IoT applications, particularly in the context of vehicle parking systems. This is noted in terms of a
city like Berlin-Germany, for on which the study is conducted. The major challenge being on how to relate
the parking techniques in a more user friendly, cost effective and less energy consumpmti0n mode where
the questions had at the beginning of the paper, relatively at the end the answers are sought to it via Sigfox
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quality of the process and the product. The Evaluation + Improvement (Ei) process is performed in our
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employability can help students to have a better understanding of business organizations and find the right
one for them. The data for the training classification models is gathered through a survey in which students
are asked to fill out a questionnaire in which they may indicate their abilities and academic achievement.
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skills, problem-solving skills and technical abilities and so on.The goal of this research is to use data
mining to predict student employability by considering different factors such as skills that the students have
gained during their diploma level and time duration with respect to the knowledge they have captured
when they expect the placement at the end of graduation. Further during this research most specific skills
with relevant to each job category also was identified. In this research for the prediction of the student
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Call for Articles - Computer Science & Engineering: An International Journal ...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
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Therefore quality assurance, and in particular, software testing is a crucial step in the software
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significantly reducing the number of test cases without having a significant drop in test effectiveness. The
strategy makes use of a comprehensive taxonomy of complexity metrics based on the product level (class,
method, statement) and its characteristics.We use a series of experiments based on three applications with
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securities' objectives; integrity, confidentiality, non- repudiation, authentication, and authorization. The
proposed model describes SOAP messages and the way to secure their contents. Due to the reason that
SOAP message is the core of the exchanging information in Web Services, this research has developed a
security model needed to ensure e-business security. The essence of our model depends on XML encryption
and XML signature to encrypt and sign SOAP message. The proposed model looks forward to achieve a
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implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
Call for Papers - Computer Science & Engineering: An International Journal (C...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Paper Submission - Computer Science & Engineering: An International Journal (...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image RetrievalCSEIJJournal
This paper compares the performance of face image retrieval system based on discrete wavelet transforms
and Lifting wavelet transforms with principal component analysis (PCA). These techniques are
implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
Call for Papers - Computer Science & Engineering: An International Journal (C...CSEIJJournal
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Data security and privacy are important to prevent the re-
veal, modification and unauthorized usage of sensitive information. The
introduction of using critical power devices for internet of things (IoTs),
e-commerce, e-payment, and wireless sensor networks (WSNs) has brought
a new challenge of security due to the low computation capability of sen-
sors. Therefore, the lightweight authenticated key agreement protocols
are important to protect their security and privacy. Several researches
have been published about authenticated key agreement. However, there
is a need of lightweight schemes that can fit with critical capability de-
vices. Addition to that, a malicious key generation center (KGC) can
become a threat to watch other users, i.e impersonate user by causing
the key escrow problem
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Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
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distribution and use of the television network and Internet in the country. This article explains how joining
different technologies like social networks, information adaptation and DTT, to get an application that
offers information services to users, based on their data, preferences, inclinations, use and interaction with
others users and groups inside the network.
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system has to decide which tasks are more suitable for hardware execution. In order to make an efficient
use of the FPGA it is convenient to choose one that allows hardware multitasking, which is implemented by
using partial dynamic reconfiguration. One of the challenges for hardware multitasking in embedded
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presented take efficient and fast decisions based on the information available at each moment. Experiments
have been made in order to analyze the performance and convenience of these reconfiguration strategies.
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of static network infrastructure to provide network connectivity. MANETs have applications in rapidly
deployed and dynamic military and civilian systems. The network topology in a MANET usually changes
with time. Therefore, there are new challenges for routing protocols in MANETs since traditional routing
protocols may not be suitable for MANETs. Researchers are designing new MANET routing protocols
and comparing and improving existing MANET routing protocols before any routing protocols are
standardized using simulations. However, the simulation results from different research groups are not
consistent with each other. This is because of a lack of consistency in MANET routing protocol models
and application environments, including networking and user traffic profiles. Therefore, the simulation
scenarios are not equitable for all protocols and conclusions cannot be generalized. Furthermore, it is
difficult for one to choose a proper routing protocol for a given MANET application. According to the
aforementioned issues, this paper focuses on MANET routing protocols. Specifically, my contribution
includes the characterization of different routing protocols and compare and analyze the performance of
different routing protocols.
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dimensional hyperchaotic systems. This paper investigates the adaptive stabilization and synchronization
of hyperchaotic Qi system with unknown parameters. First, adaptive control laws are designed to
stabilize the hyperchaotic Qi system to its equilibrium point at the origin based on the adaptive control
theory and Lyapunov stability theory. Then adaptive control laws are derived to achieve global chaos
synchronization of identical hyperchaotic Qi systems with unknown parameters. Numerical simulations
are shown to demonstrate the effectiveness of the proposed adaptive stabilization and synchronization
schemes.
An Energy Efficient Data Secrecy Scheme For Wireless Body Sensor NetworksCSEIJJournal
Data secrecy is one of the key concerns for wireless body sensor networks (WBSNs). Usually, a data
secrecy scheme should accomplish two tasks: key establishment and encryption. WBSNs generally face
more serious limitations than general wireless networks in terms of energy supply. To address this, in this
paper, we propose an energy efficient data secrecy scheme for WBSNs. On one hand, the proposed key
establishment protocol integrates node IDs, seed value and nonce seamlessly for security, then
establishes a session key between two nodes based on one-way hash algorithm SHA-1. On the other hand,
a low-complexity threshold selective encryption technology is proposed. Also, we design a security
selection patter exchange method with low-complexity for the threshold selection encryption. Then, we
evaluate the energy consumption of the proposed scheme. Our scheme shows the great advantage over
the other existing schemes in terms of low energy consumption.
To improve the QoS in MANETs through analysis between reactive and proactive ...CSEIJJournal
A Mobile Ad hoc NETwork (MANET), is a self-configuring infra structure less network of mobile devices
connected by wireless links. ad hoc is Latin and means "for this purpose". Each device in a MANET is free
to move independently in any direction, and will therefore change its links to other devices frequently. Each
must forward traffic unrelated to its own use, and therefore be a router. The primary challenge in building
a MANET is equipping each device to continuously maintain the information required to properly route
traffic. QOS is defined as a set of service requirements to be met by the network while transporting a
packet stream from source to destination. Intrinsic to the notion of QOS is an agreement or a guarantee by
the network to provide a set of measurable pre-specified service attributes to the user in terms of delay,
jitter, available bandwidth, packet loss, and so on. The analysis is mainly between proactive or table-driven
protocols like OLSR (Optimized Link State Routing) viz DSDV (Destination Sequenced Distance Vector) &
CGSR (Cluster Head Gateway Switch Routing) and reactive or source initiated routing protocols viz
AODV (Ad hoc on Demand distance Vector) & DSR (Dynamic Source Routing). The QoS analysis of the
above said protocols is simulated on NS2 and results are shown thereby.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
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About
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water Industry Process Automation and Control Monthly - May 2024.pdf
Analysis of Seismic Signal and Detection of Abnormalities
1. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
DOI:10.5121/cseij.2022.12607 53
ANALYSIS OF SEISMIC SIGNAL AND DETECTION OF
ABNORMALITIES
Sujata Kulkarni1
, Udhav Bhosle2
and Vijay Kumar T2
.
1
School of Earth Science SRTMU, Nanded, Associate Professor S.P.I.T. Mumbai
2
School of Earth Science SRTMU, Nanded
ABSTRACT
Seismic signals are ground vibrations used to detect seismic events. However, seismic signal captured from
sensors is distorted signal contains noise and makes actual event detection difficult. In most cases, external
noise such as manmade or any heavy vehicle vibration always overlaps with the seismic reflections over
time. The presence of noise in the seismic signal makes it difficult to determine the magnitude at which the
seismic events have occurred. The aim of our study is to process the signals received from seismic sensor
and identify it as seismic events signal and non-seismic events signal based on the magnitude. The authors
propose a robust noise suppression method using bandpass filter, IIR Wiener filter and event detection
using recursive Short-Term Average (STA)/Long Term Average (LTA) and Carl Short Term Average
(STA)/Long Term Average (LTA). The proposed study determines reference magnitude to distinguish
seismic and non-seismic activity. The projected study is based on the analysis of seismic signal received
from single sensor and sensor networks (SN) and determines the magnitude to distinguish seismic and non-
seismic events and time of an actual earthquake event. The experimental dataset is a broadband seismic
signal from BSVK and CUKG station sensors located at Basavakalyan, Karnataka, and the Central
University of Karnataka respectively. The proposed approach helps to extract the information about pre-
seismic event, actual seismic event, post-seismic event activities and identify the abnormal pattern that
supports to detect heearth’s activities before the actual seismic event.
KEYWORDS
Seismic signal, non-seismic signal, Carl STA/LTA, Abnormal pattern.
1. INTRODUCTION
The seismic station captured the signal continuously at a high sampling frequency. Such a huge
amount of data is challenging to store. This issue motivates the creation of seismic data
acquisition.Thatwillprocessalltheseismicsignalswithoutcontinuousstorage. A trigger algorithm
aids in the detection of abnormal activities in the ever-present seismic noise signal. When a
seismic event is recorded, incoming signals are stored and stop when the signal reaches the
background noise level [1]. Seismic noise is significant for microzonation studies and surface-
wave to mography[1,2].The detection of earthquakes above background noise is required for the
study of earthquakes or the imaging of the Earth with seismic wave arrivals. Depending on the
location, season, time of day, and weather conditions, the levels of natural ambient noise in
seismic records can vary by 60 dB (a factor of 1000 in amplitude). This is equivalent to
variations in seismic arrival detection thresholds of around three magnitude units. Therefore, it is
crucial to first investigate the noise levels at prospective recording locations. [1].A seismic
network is made up of numerous stations (receivers) spread out over a large area, each of which
has a seismometer that continuously records ground motion. An energy detector such a short-
term average (STA) or long-term average(LTA) is typically used to detect an earthquake at one
station at a time. As these windows move through the continuous data, STA/LTA calculates the
2. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
54
ratio of the STA energy in a small time window to the LTA energy in a larger time window.
When the STA/LTA ratio surpasses specific criteria, detection is reported [3,4]. The next step is
to analyse whether detections at various stations around the network are consistent with a seismic
source using an association algorithm. Let's say that a seismic event is picked up by at least four
stations. In that instance, it is listed in an earthquake catalogue, a database that lists the place,
date, and magnitude of all previously recorded earthquakes. Earthquakes are successfully
identified using STA/LTA.
The outline of this paper is as follows: in Section 2, summarize the prior art and related work by
the researchers Section 3,shows the detailing of the real time dataset for the experimental testing.
The proposed work and methodology are discussed in section 4. The experimental testing and the
results section 5, focuses on the magnitude and time detection of the seismic event. Distribution
patterns to distinguish seismic event and abnormality. Finally, a conclusions are drawn in Section
6.
2. LITERATURE SURVEY
To effectively and efficiently reduce seismic random sounds, Fangyu Li et al. (2021) [5] offer a
unique seismic signal processing method. The author focuses on how the design of traditional
filtering operators "regularises" the denoised outcomes. The efficiency of denoising is increased
by the resampling process. Random noise has been decreased using the suggested method,
resulting in an improved recovery of the intrinsic seismic signal components. Excellent
achievements are shown by qualitative and quantitative demonstrations using synthetic and field
data. A residual neural network for detection is proposed by Abdullah Othman et al. in 2021 [6].
In comparison to previous denoising approaches, this method along with the IIR Wiener filter-
based denoising method produces better results.
A stochastic signal analysis method is used in Jae Sang-Moon and Mintaek Yoo's (2020) [7]
study to make use of the smartphone sensors for the quick EEW system. The virtual earthquake
detection data in the train by smartphone sensor has been built from the train vibration data from
the poor fidelity on-board accelerometer. The produced data's stochastic features have been
examined using the short-time Fourier transform (STFT) method. stochastic methods that
effectively analyse low fidelity sensor data, like that from a smartphone, for the quick EEW. A
denoising technique based on IIR Wiener filters was proposed by Iqbal et al. in 2018 [8]. The
second-order statistics of the noise and the observations, which are easily derived from the time-
series data that have been recorded, serve as the direct foundation for the suggested method.
When there is low SNR, the proposed approach performs admirably. This is advantageous for the
applicability of the denoising method to field data gathered in a variety of seismic noise
situations because the filter does not presume any specific noise statistics.
B.K.Sharma et al.,(2010)[9]give an overview of the different detection algorithms used for
seismicsignal detection. Event detection algorithms have already been created by numerous
researchers for seismic data from earthquakes and explosions. The method based on the Short
Term Average to Long-term Average (STA/LTA) principle is suggested to be more effective in
detecting earthquakes and strong motions. It also puts light on the performance of the standard
STA/LTA algorithm and the recursive STA/LTA-based earthquake detector algorithm, which is
more commonly known and frequently used.
According to Withers et al. (1998) [4,] the best output for a global correlation-based event-
detection and location system was found to be produced by a STA/LTA algorithm that
incorporates adaptive window lengths controlled by non stationary seismogram spectral
3. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
55
characteristics, even though no algorithm was clearly optimal under all source, receiver, path, and
noise conditions tested.
Polkowski et al. and the Passeq Working Group (2016) [10] classified and located the events
using standard STA/LTA triggers (Carl Johnson's STA/LTA algorithm) and grid search. The
outcome was manually verified. Real-Time Recurrent Network (RTRN) detection was used in the
second approach (Wiszniowski et al. 2014) [11]. Both methods produced similar results, revealing
four previously unknown seismic events in the Gulf of Gdansk area of the southern Baltic Sea.
Both detection methods are discussed in this paper, along with their advantages and
disadvantages.
A summary of the literature survey indicates that most of the author uses the STA/LTA algorithm
for seismic detection, but the analysis of seismic signal before the actual seismic event is missing
which is very much important to give early warning about the actual seismic event, that motivates
us to focus on the characterization ofpre, actual, post-seismic signal with the non-seismic signal.
This analysis also distinguishes the abnormal event which is not an actual seismic event.
3. DATASET DESCRIPTION
The data is collected from the School of Earth sciences of Swami Ramanand Teerth Marathwada
University, Nanded, Maharashtra, India. The data is collected from stations such as Basavakalyan
(BVSK) the Central University of Karnataka, and Gulbarga (CUKG) located at different places.
The experimental dataset is a broadband seismic signal from BSVK and CUKG station sensors
located at Basavakalyan, Karnataka, and the Central University of Karnataka respectively. It is a
broadband seismological sensor (Trillium 120QA) with sensitivity 2000V/m/s and data collected
at 100samples/s.Its natural period is 120seconds. The signal used in this paper is of total 5-hour
duration and analysed for every one-hour duration segments sampled at 100Hz. Seismic signal is
recorded on 12-10-2021 from 0:00:00-4:59:59 hours, and non-seismic or noise signal is recorded
on 9-7-2021 from 0:00:00-4:59:59 hours. The analysis is performed on each station's individual
recorded signal and then combined to extract additional information for the detection of
abnormality and actual seismic event. The seismic institute records two events, one at 2:36AM
with a magnitude of 3.6 and one at 2:47AM with a magnitude of 2.8.Table 1 gives the detail of the
earthquake event.Thecaptured non-seismic signal from the respective stations is showninfigure1.
Table 1. Seismic event details
4. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
56
Figure 1. The non-seismicsignal recorded at BSVKand CUKG
4. PROPOSED WORK
Figure 2 shows the flow of detection of seismic event. The recorded signals is pre-processed and
filters to reduce the noise . Applied the STA/LTA algorithm on the filtered seismic signal for
magnitude detection.
Figure 2. Proposed work flow
The paper proposes the analysis of the seismic signal, its characteristics, and how it is different
from the non-seismic signal. Further, the analysis emphasizes on accurately detecting actual
earthquake magnitude w.r.t. time. As per [6] to verify the seismic event, the time difference of
seismic event in all directions should be low, and the corresponding trigger ratio should be high.
The main aim is to verify the actual event w.r.t magnitude and time. The actual seismic event
occurred between 02:00:00- 2:59:59 dated 12-10-2021, so the given seismic signal is divided into
3 cases pre, actual seismic, and post-seismic signals. Sample of pre-seismic at the CUKG station
is shown in figure 3.
5. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
57
Figure 3. The pre seismicsignal recorded at CUKG
To verify the seismic event, the pre-seismic case is considered (01:00:00AM-01:59:59AM). The
signal is detrended linearly and tapered with 5% tapering with the Blackman window. This
process will make the signal linear, and the corners are tapered. The pre-processed signal is band
passed with the frequency band of 0.001-5Hz, to remove the low noise component. After that, IIR
Wiener filter is applied on the band passed filtered signal with a mask size of 73 to reduce the
noise level. (Filter mask value must be odd, and after few iterations of different random values
such as 5, 25, 53, and 73, 117 the mask size is set to 73) The clean signal will give the idea about
the few high peaks i.e., abnormal conditions before the actual earthquake. The frequency domain
magnitude spectrum of seismic signal shows the frequency peaks in the lower frequency range
and in the higher frequency bands. The seismic event is seen at the lower frequencies and its
effect is extended to the higher frequencies [12,13]. This leads us to examine the magnitude
spectrum of FFT, and the STFT and Wavelet Spectrograms carefully in the frequency range of
0.001-4 Hz.The figure 4 gives the difference between the FFT, STFT and wavelet spectrum of
seismic and non seismic signals.
(a) (b)
The above figure clearly indicates the difference between seismic event and non-seismic event
frequency. The difference is clearly seen between the frequency range from 1-3Hz as highlighted
by red box.
6. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
58
( c )
(d)
(e)
(f)
Figure 4. Magnitude spectrum of seismic and non seismic event (a)(b)FFT(c)(d) STFT(e)(f) Wavelet
Spectrum
The magnitude spectrum of non-seismic signal is crowded with noise component. The maximum
energy of this spectrum is concentrated at very low frequency. Whereas, the magnitude spectrum
of the seismic event signal shows the maximum energy during 2:35:00-2:40:00AM. This energy is
spread from 1Hz to 4Hz. Both Wavelet magnitude spectrum and STFT magnitude spectrum
indicates the same results.
Frequency domain analysis done using FFT, STFT [7], and wavelet figure 5(a),(b),(c) respectively
shows that the high energy levels between 0.001 to 4Hz which proved that the few abnormal
activities occurred before the actual seismic event.
7. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
59
(a)
(b)
(c )
Figure 5. Frequency domain analysis of pre seismic
4.1. Methodology
Magnitude and time are the important parameters to ensure the abnormal change in the amplitude
of event. This paper focuses on three mechanisms w.r.t. magnitude and time parameter help to
classify the abnormal activity before the actual earthquake. The STA/LTA algorithm evaluates the
ratio of short- to long-term energy density. The filtered data are then scanned using a recursive
STA/LTA algorithm to obtain trigger times of potential events [4]. The STA/LTA threshold to
declare a trigger depends on the signal-to-noise ratio. The recursive STA/LTA [9] is like the
standard STA/LTA except that for each successive time step, a fraction of the average data value,
rather than a specific data point value is removed[14].
Standard STAi+1 = STAi + {x(i) − x(i − NSTA)}/ NSTA (1)
Where NSTA denotes the number of STA points and i denotes the set of STA point data values.
Seismic data is recorded if the STA/LTA is equal to or greater than the pre-set value for the true
condition of the event. In the proposed method, the event time is recorded as the time where the
trigger ratio value is maximum. Both the Recursive STA/LTA and carl STA trigger algorithm are
applied on the signal. The values are recorded before and after applying IIR Wiener filter. Also,
the event time is recorded as the time where the amplitude of the STFT magnitude spectrum is
maximum. SNR values are recorded after pre-processing, Bandpass filtering and after wiener
filtering. This techniques applied on non-seismic and seismic signal.
4.2. Recursive STA/LTA [14]
Developed at the University of Wisconsin, Madison recursive STA/LTA is a method used for
triggering tele seismic and was used by L. Powell in a portable data acquisition system. Statistical
independence from
8. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
60
STA I+1
LTA I+1
=
1
NSTA
∗X
I2 + 1−
1
NSTA
∗STA I
1
NLTA
∗XI2+ 1−
1
NLTA
∗LTA I
(2)
Where xi is time series amplitude, NSTA*sampling rate, and NLTA*sampling rate. The recursive
STA/LTA is smoother than the standard STA/LTA algorithm.
4.3. Carls STA/LTA
The majority of seismic detectors employ a relationship between the short-term average (STA)
and the long-term average (LTA). This algorithm computes four moving averages using two
parameters: eta, star, ratio, ltar, abs sta, and lta quiet [10].
ETA = STAR − (RATIO ∗ LTAR) − ABS(STA − LTA) − QUIET (3)
where eta is the detector response – a value over 0 means detection, STAR is the short-term
moving average of signal, LTA is thelong-term moving average of signal, the star is the short-
term moving average of the absolute value of signal and LTA difference, LTAR is the long-term
moving average of star, ratio, and quiet-sensitivity parameters.
4.4. Wiener Filter [8]
Wiener filter on the band passed filtered signal with mask size is 73. It is observed that after few
iterations of different values of mask the noise is much reduced at window 73. Figure 6(c) shows
the filtered signal which is much clean as compared to the original signal. Let x be the input
signal, then the output is
=
𝜎2
𝜎𝑥
2 𝑚𝑥 + 1 −
𝜎2
𝜎𝑥
2 𝑥 𝜎𝑥
2
≥ 𝜎2
,
𝑚𝑥 𝜎𝑥
2
< 𝜎2
,
(4)
Where 𝑚𝑥 is local estimate of the mean,𝜎𝑥
2
is local estimate of the variance
𝜎2
is threshold noise parameter, If𝜎 is not specified, the average of local variances is used.
4.5. Short Term Fouriertransform [7]
The STFT and wavelet Transform are used to analyses the filtered seismic signal. The STFT is a
popular method for detecting changes in the characteristics of time-history data over time. A
short-time Fourier transform is a series of Fourier transforms of a windowed signal (STFT).
STFT provides time-localized frequency information in situations where a signal's frequency
components vary over time.
5. RESULT AND DISCUSSION
The sensor recorded signal usually consist of seismic activities occurred as well as the noise
captured by the sensor. Our method of double filtering i.e., Bandpass filtering followed by
Wiener filtering reduces the noise level of this signal as seen in figure 6. This noise reduction can
also be observed in the form of Signal-to-Noise ratio (SNR) as shown in table 2. Here, the SNR
value of the two different seismic event signals (dated 12-10-2021 and 5-2-2022), pre-seismic
signal (dated 12-10-2021) and a non-seismic signal (dated 9-7-2021) is tabulated.
9. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
61
The Signal to noise ratio is computed at two stages; after pre-processing the recorded signal and
after the application of the wiener filter. As seen in the table 2, for both the seismic signal, the
SNR after wiener filtering is improved. For the seismic signal dated 12-10-2021 the E channel
SNR is -96.784dB after pre-processing and it improves to -93.23dB after wiener filtering,
whereas for seismic signal dated 5-2-2022, the SNR for the channel E is -104dB and it is seen to
be improved after wiener filtering to -74.177dB. On first seismic signal, the improvement of SNR
or the reduction of noise level is seen lower as compared to the second seismic signal due to the
intensity of the seismic event. This seismic event is much higher than the intensity of noise
present during the occurrence of the event. However, for the non-seismic signal, the SNR for
channel E is -76.65dB after only pre-processing. After filtering it is improved to-51.672dB.
Regarding the filtered pre seismic signal, the SNR results are also improved for the channel E is -
48.923dB. The SNR values plays important role to distinguish between the non-seismic and pre
seismic signals. This indicates that the wiener filtering approach for the noise reduction is well
suited to detect the seismic event at micro levels which may overlapped with the noise.
Table 2. Signal to noise ratio of seismic signals
The figure6 shows the sensor recorded signal and filtered signal. It can be seen that double
filtering significantly drops the noise level of the signal; thus aiding for better detection of
the events. Same effect of the filter is seen on the non-seismic signals.
10. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
62
(a)
(b)
(c)
Figure 6. Pre-processing of seismic signals(a)detrain and tapering (b) Bandpass filtered (c) wiener
filtered
5.1. Seismic Event Time Detection
Different detection algorithms are used for event identification. The experimentation uses the
Recursive Short Time Average/ Long Time Average (STA/LTA) and Carl Short Time Average/
Long Time Average (STA/LTA) methods. The event detection is observed by both the
techniques before and after Wiener filtering. Also, the event detection is compared with the
maximum energy time of Short Time Fourier Transform magnitude spectrum. Table 3 shows
the event time computed from the different algorithm.
Table 3. Seismic signal time detection
For pre-seismic and non-seismic signal, the recursive trigger algorithm gives the maximum
trigger value at the beginning of each one-hour segment, thus giving the false detection. This
can be verified from above table. For the duration of 0:00:00-1:59:59 for seismic signal, for all
the directions, the time recorded by recursive algorithm varies. Also, there is a significant
difference in the time before filtering, after filtering with STFT detection. Same observation is
11. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
63
seen for the non-seismic signal. For seismic and post seismic signal this algorithm picks the p-
wave time accurately. The time recorded are more or less same with or without IIR Wiener
filter.The Carl STA algorithm does not detect the maximum trigger at the beginning of each
segment of preseismic and non-seismic signals, instead it gives the maximum trigger ratio
where the signal amplitude is maximum. For preseismic and non-seismic signals, the time
detected for the maximum trigger ratio value is almost same before and after wiener filtering.
Also, these detections are match with the STFT for few directions. The preseismic signal is
highlighted in the table.From the table 4 the seismicsignal (5-2-2022T4:00:00-4:59:59 CUKG
Station.) using Carl STA/LTA algorithm gives maximum trigger ratio for s-wave time of the
signal. Timing is almost same before and after wiener filtering and with STFT detected time.
Table 4. Seismic signal time detection
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(a)
(b)
(c)
(d)
Figure 7. Trigger plots of seismic signals (a) Recursive STA/LTA [sta=1, lta=25], (b) Wiener filtering
and Recursive STA/LTA, (c) Carl STA/LTA [sta =1, lta = 3], (d) Wiener filtering and Carl STA/LTA , ( e)
Wiener filter and STFT
5.2. Seismic Event Magnitude Estimation
Magnitude is an important parameter that forms the base of analysis used for the detection of the
earthquake. The actual seismic event was recorded from 2:00 am to 3:00 am duration by BSVK
and CUKG stations. To verify the magnitude and corresponding time, the signal is sliced to 15 sec
intervals of the signal duration. The Magnitude is calculated using the following formula [15].
M_l=logA+[nlogR100+KR-100+3] (4)
K is attenuation coefficient Hypocentral distance, A = wood Anderson amplitude, n = constant,
assumed to be 1. From Table I revised calculation for the hypocentre is as follows
Hypocentral Distance (km)(D) = [(d + Δ)]0.5 (5)
Where D is the Hypocentral distance (km), Δ is the epicentral distance (km), and d is depth. Table
5 shows the revised parameter of the seismic signalfor magnitude estimation of seismic signals.
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Table 5. Magnitude estimation of seismic signal
The experimental testing of magnitude and time calculations for the station matches with the
given magnitude and time. Figure 8-time Vs magnitude, shows the magnitude of 3.60069 at
2:37:30 and 2.819 at 2:48:15 in 0.001 to 4Hz filter band. The BSVK station also shows the similar
value of magnitude and time duration. Here, it should be noted that the magnitude is estimated
with reference to the Hypocentral value corresponding to the event occurred on 2:36 AM, for the
rest of the signal also the same Hypocentral distance is taken as the reference to estimate the
magnitude. Hence, the mean level of the estimated magnitude is much higher in the pre, post and
non-seismic signal as compared to the seismic signal as seen in the figure 8. These verification
graph, interpreted that, the proposed approach of seismic signal analysis detects the event with
magnitude value and time accurately.
Figure 8. The magnitude Vs Time verification of actual seismic event
The second approach is to distinguish the distribution pattern of actual seismic signal with non-
seismic signal i.e., abnormal signal due to manmade or vehicle vibration. The actual seismic signal
is considered from 2:00 am to 3:00 am. The signal is sliced in the duration of 1 min and computed
the magnitude using equation 4.This process is repeated in all the filter bands such as Filter1
(0.001-30Hzband),andFilter2(2-8Hzband).The correlation between actual seismic events with non-
seismic events at station CUKG is shown in Table6 and the corresponding magnitude distribution
and histogram plots are in figure 9.
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Table 6. Correlation between seismic and non-seismic event
Filter0.001 to30 HZ
EVENT
TIME Non-
seismic
preseismic seismic
CUKG
seismic 0.161941 0.290694 1.000000
Filter2to8Hz
CUKG
seismic 0.384093 0.364055 1.000000
It is observed that the correlation coefficients of the actual seismic signal are different from the
non-seismic/abnormal signals. Seismic to seismic signal autocorrelation is 1. The correlation of
pre-seismic in the CUKG station is near to seismic signal which indicates the abnormality before
the actual seismic event. From the figure 9 (a)it is observed that the actual seismic event from
CUKG stations shows the high peak only when the event happened while the non-
seismic/abnormal event shows many peaks during time slots that are not the actual seismic event.
Figure 9(b) shows the outliers correspond to the event magnitude values, which can be seen in
both filter bands. The same observation can be made with the CUKG station values. These
outliers are not seen in the non-seismic and pre- seismic signals.
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(a)
(b)
Figure 9. Comparison of seismic and non-seismic event (a) Magnitude and time estimation(b)
distribution pattern
6. CONCLUSIONS
The proposed a technique to distinguish seismic and non-seismic event based on the magnitude
of the signal. The technique consists of noise removal and determination of magnitude of the
signal. During the experimentation, it is observed that the double filtering significantly remove
noise. As shown in figure 8 seismic signal analysis matches the estimated magnitude and time
with recorded magnitude and time. The correlation coefficient to f pre-seismicis nearest to actual
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events which can be used as a warning alarm before the actual earth quake event. Such warning
will be further helpful for prediction in detail. The recursive STA/LTA triggering algorithm on
the non-seismic may give false alarms since the averaging window length has to be manually set.
Carl STA/LTA algorithm shows better results than that of the recursive algorithm shown in Table
III. The Carl STA/LTA algorithm detects the highest event peak of the S wave of the seismic
event same for all the directions and for both the network signals. The proposed distribution
pattern of the magnitude of a given seismic signal is helpful to distinguish between seismic and
non-seismic events. A few points of the magnitude are the high and low time difference in all
directions, especially at pre event which will be helpful to characterize the earth’s activity before
the actual event. All data points are plotted to show the approximate Gaussian distribution that
differs between the seismic and non-seismic events.
ACKNOWLEDGEMENTS
This research was supported by the research centre of the school of Earth sciences, SRTMU,
Nanded, Maharashtra, India. We would like to thank RUSA Maharashtra for funding CESS. The
author would also like to show our gratitude to our colleagues who provided insight and expertise
that greatly assisted the research.
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AUTHORS
Dr. Sujata Kulkarni is an academician with an overall experience of 22 years. She is
pursuing post doctorate from the school of earth science from SRTMU, Nanded.
Currently she is working as a associate professor in Sardar Patel Institute of
Technology, Mumbai. Her research interest is Image and Pattern Recognition, Machine
learning&AI.