As digital communication technologies continue to grow and evolve, applications for this steady development are also growing. This growth has generated a growing need to look for automated methods for recognizing and classifying the digital modulation type used in the communication system, which has an important effect on many civil and military applications. This paper suggests a recognizing system capable of classifying multiple and different types of digital modulation methods (64QAM, 2PSK, 4PSK, 8PSK, 4ASK, 2FSK, 4FSK, 8FSK). This paper focuses on trying to recognize the type of digital modulation using the artificial neural network (ANN) with its complex algorithm to boost the performance and increase the noise immunity of the system. This system succeeded in recognizing all the digital modulation types under the current study without any prior information. The proposed system used 8 signal features that were used to classify these 8 modulation methods. The system succeeded in achieving a recognition ratio of at least 68% for experimental signals on a signal to noise ratio (SNR=5dB) and 89.1% for experimental signals at (SNR=10dB) and 91% for experimental signals at (SNR=15dB) for a channel with additive white gaussian noise (AWGN).
Enhanced signal detection slgorithm using trained neural network for cognitiv...IJECEIAES
Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications
A New Wavelet based Digital Watermarking Method for Authenticated Mobile SignalsCSCJournals
The mobile network security is becoming more important as the number of data being exchanged on the internet increases. The growing possibilities of modern mobile computing environment are potentially more vulnerable to attacks. As a result, confidentiality and data integrity becomes one of the most important problems facing Mobile IP (MIP). To address these issues, the present paper proposes a new Wavelet based watermarking scheme that hides the mobile signals and messages in the transmission. The proposed method uses the successive even and odd values of a neighborhood to insert the authenticated signals or digital watermark (DW). That is the digital watermark information is not inserted in the adjacent column and row position of a neighborhood. The proposed method resolves the ambiguity between successive even odd gray values using LSB method. This makes the present method as more simple but difficult to break, which is an essential parameter for any mobile signals and messages. To test the efficacy of the proposed DW method, various statistical measures are evaluated, which indicates high robustness, imperceptibility, un-ambiguity, confidentiality and integrity of the present method.
Performance Analysis of Noise Uncertainty in Energy Detection Spectrum Sensin...Onyebuchi nosiri
Abstract—The Performance of Energy Detection (ED) spectrum sensing technique depends on threshold selected for deciding the presence or absence of Primary User. In practice, noise density is uncertain and can affect the performance of ED in that sometimes presence of signals is confused for their absence (noise) and vice versa. The traditional energy detection algorithm was based on fixed threshold and has been observed to be inefficient under noise uncertainty. The technique requires optimizing the threshold to be more flexible to check the noise uncertainty effects. The paper therefore proposed an algorithm relative to a unique environment which in effect considered the dynamism relatively and dependent on the environment. The results obtained demonstrated significant improvement compared to the traditional energy detection system
Improved performance of scs based spectrum sensing in cognitive radio using d...eSAT Journals
Abstract
Tremendous growth in current wireless networks raises the demand of more frequency spectrum, over the finite availability of spectrum resource. Although, the research has specifies that the available primary users (i.e. licensed user) has not occupying the channel all the time. The most effective technology known as Cognitive radio giving promises for a solution of under utilization of available frequency spectrum in wireless communication. In cognitive radio network two types of wireless user can be define as primary user and secondary user. Primary users have highest priority to utilize the available band of frequency and secondary user can utilize these services only when the channel is vacant by primary user and there will be no any interference. The optimization of this may be implemented by a smart technique such as cognitive radio, which is fully automated intelligent wireless sensor tool having capability to sense, learn & adjust relevant operating parameters dynamically in radio atmosphere. This can be happen if we prefer the appropriate window technique to evaluate system parameter for sensing the availability of vacant band. We show that by comparing the different windows techniques, cognitive radios not only provide better spectrum opportunity but also provide the chance to huge number of wireless users.
Keywords: Primary user, Secondary user, Spectrum Sensing and Window technique etc.
Public wireless LAN services have been increasing recently. These are convenient services for carrier
companies particularly. And carrier companies and the user can disperse traffic. However in order to
improve the convenience, the services do not often use the encryption key or do not change the encryption
key that has already been set. Therefore, the services have a problem in safety. This paper solves this
problem using the visible light communication.
The visible light communication sends a signal by blinking the light. One of the visible light communication
features is that we can see the transmission area. The visible light communication can use lighting
equipments as the transmitter unlike infrared or conventional radio communications. Further, visible light
communication can separate to clarify the transmission range by using light.
We propose the distribution of the encryption key and the SSID using visible light communication. Visible
light communication can easily prepare a small network, such as a partition or per a room basis. For
malicious users connecting to the network is necessary to enter in the service provided area. Thus the
administrator is able to easily manage legitimate users. In addition, it is possible to update the SSID and
the encryption key by visible light communication easily for an administrator. Thus if malicious users get
the SSID and the encryption key, they cannot use the SSID and the encryption key immediately. Normal
users may only need to run the shell script for receiving information from the receiver. Therefore,
convenience is good.
In order to confirm the improved convenience, we measured the time it takes for a user to connect to the
network. Conventional methods are methods that use or do not use the encryption key. As a result, users
can connect to the network in a short time compared with the conventional methods. The system becomes
stronger than conventional methods because it is possible to update the encryption key and SSID
automatically in the security aspect.
Enhanced signal detection slgorithm using trained neural network for cognitiv...IJECEIAES
Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications
A New Wavelet based Digital Watermarking Method for Authenticated Mobile SignalsCSCJournals
The mobile network security is becoming more important as the number of data being exchanged on the internet increases. The growing possibilities of modern mobile computing environment are potentially more vulnerable to attacks. As a result, confidentiality and data integrity becomes one of the most important problems facing Mobile IP (MIP). To address these issues, the present paper proposes a new Wavelet based watermarking scheme that hides the mobile signals and messages in the transmission. The proposed method uses the successive even and odd values of a neighborhood to insert the authenticated signals or digital watermark (DW). That is the digital watermark information is not inserted in the adjacent column and row position of a neighborhood. The proposed method resolves the ambiguity between successive even odd gray values using LSB method. This makes the present method as more simple but difficult to break, which is an essential parameter for any mobile signals and messages. To test the efficacy of the proposed DW method, various statistical measures are evaluated, which indicates high robustness, imperceptibility, un-ambiguity, confidentiality and integrity of the present method.
Performance Analysis of Noise Uncertainty in Energy Detection Spectrum Sensin...Onyebuchi nosiri
Abstract—The Performance of Energy Detection (ED) spectrum sensing technique depends on threshold selected for deciding the presence or absence of Primary User. In practice, noise density is uncertain and can affect the performance of ED in that sometimes presence of signals is confused for their absence (noise) and vice versa. The traditional energy detection algorithm was based on fixed threshold and has been observed to be inefficient under noise uncertainty. The technique requires optimizing the threshold to be more flexible to check the noise uncertainty effects. The paper therefore proposed an algorithm relative to a unique environment which in effect considered the dynamism relatively and dependent on the environment. The results obtained demonstrated significant improvement compared to the traditional energy detection system
Improved performance of scs based spectrum sensing in cognitive radio using d...eSAT Journals
Abstract
Tremendous growth in current wireless networks raises the demand of more frequency spectrum, over the finite availability of spectrum resource. Although, the research has specifies that the available primary users (i.e. licensed user) has not occupying the channel all the time. The most effective technology known as Cognitive radio giving promises for a solution of under utilization of available frequency spectrum in wireless communication. In cognitive radio network two types of wireless user can be define as primary user and secondary user. Primary users have highest priority to utilize the available band of frequency and secondary user can utilize these services only when the channel is vacant by primary user and there will be no any interference. The optimization of this may be implemented by a smart technique such as cognitive radio, which is fully automated intelligent wireless sensor tool having capability to sense, learn & adjust relevant operating parameters dynamically in radio atmosphere. This can be happen if we prefer the appropriate window technique to evaluate system parameter for sensing the availability of vacant band. We show that by comparing the different windows techniques, cognitive radios not only provide better spectrum opportunity but also provide the chance to huge number of wireless users.
Keywords: Primary user, Secondary user, Spectrum Sensing and Window technique etc.
Public wireless LAN services have been increasing recently. These are convenient services for carrier
companies particularly. And carrier companies and the user can disperse traffic. However in order to
improve the convenience, the services do not often use the encryption key or do not change the encryption
key that has already been set. Therefore, the services have a problem in safety. This paper solves this
problem using the visible light communication.
The visible light communication sends a signal by blinking the light. One of the visible light communication
features is that we can see the transmission area. The visible light communication can use lighting
equipments as the transmitter unlike infrared or conventional radio communications. Further, visible light
communication can separate to clarify the transmission range by using light.
We propose the distribution of the encryption key and the SSID using visible light communication. Visible
light communication can easily prepare a small network, such as a partition or per a room basis. For
malicious users connecting to the network is necessary to enter in the service provided area. Thus the
administrator is able to easily manage legitimate users. In addition, it is possible to update the SSID and
the encryption key by visible light communication easily for an administrator. Thus if malicious users get
the SSID and the encryption key, they cannot use the SSID and the encryption key immediately. Normal
users may only need to run the shell script for receiving information from the receiver. Therefore,
convenience is good.
In order to confirm the improved convenience, we measured the time it takes for a user to connect to the
network. Conventional methods are methods that use or do not use the encryption key. As a result, users
can connect to the network in a short time compared with the conventional methods. The system becomes
stronger than conventional methods because it is possible to update the encryption key and SSID
automatically in the security aspect.
Analysis of different digital filters for received signal strength indicatorjournalBEEI
Due to high demand in Internet of Things applications, researchers are exploring deeper alternative methods to provide efficiency in terms of application, energy, and cost among other factors. A frequently used technique is the Received Signal Strength Indicator value for different Internet of Things applications. It is imperative to investigate the digital signal filter for the Received Signal Strength Indicator readings to interpret it into more reliable data. A contrasting analysis of three different types of digital filters is presented in this paper, namely: Simple Moving Average filter, Alpha Trimmed Mean filter and Kalman filter. There are three criteria used to observe the performance of these digital filters which are noise reduction, data proximity and delays. Based on the criteria, the choice of digital signal processing filter can be determined in accordance with its implementations in [ractice. For example, Alpha-Trimmed Meanfilter is shown to be more efficient if used in the pre-processing of Received Signal Strength Indicator readings for physical intrusion detection due to its high data proximity. Hence, this paper illustrates the possibilities of the use of Received Signal Strength Indicator in different Internet of Things applications given a proper choice of digital signal processing filter.
OPTIMIZATION OF THE RECURSIVE ONE-SIDED HYPOTHESIS TESTING TECHNIQUE FOR AUTO...ijwmn
In this paper, an optimized Recursive One-Sided Hypothesis Testing (ROHT) threshold estimation algorithm for energy detection based on Cognitive Radio (CR) application is presented. The ROHT algorithm is well known to compute and correct threshold values based on the choice of the parameter
values; namely the coefficient of standard deviation (z-value) and the stopping criteria (). A fixed computational process has been employed in most cases to estimate these parameter values, thus rendering them non-adaptive under different sensing conditions. Also, this fixed (manual tuning) process requires prior knowledge of some noise level to enable pre-configuration of a predefined target false alarm rate.
This renders the parameter estimation process cumbrous and unworkable for real-time purposes, particularly for autonomous CR applications. Furthermore, using wrong parameter values may lead to either too high or too low false alarms or detection rates of the algorithm. Sequel to aforementioned mentioned constraints, we propose a new mechanism for instantaneous parameter optimization of the ROHT algorithm using Particle Swarm Optimization (PSO) algorithm. Our PSO-ROHT model design was extensively tested under different conditions and results were compared to the non-optimized ROHT. The
results obtained show that the proposed design effectively adapts the parameter values of the Recursive One-Sided Hypothesis Testing algorithm in accordance with the input dataset under consideration. Also, that the proposed optimized model outperforms its non-optimized counterpart following the estimated detection probability and false alarm probability of both schemes, particularly in detecting Orthogonal Frequency-Division Multiplexing signals. In detecting the Orthogonal Frequency-Division Multiplexing signals at signal-to-noise ratio of 3dB and above, the proposed model achieved a higher detection rate of 96.23% while maintaining a low false alarm rate below 10%, which complies with the IEEE 802.22standard for Cognitive Radio application. Our PSO-ROHT algorithm is shown to be highly effective for autonomous and full blind signal detection in CR, with strong potentials for application in other areas requiring automatic threshold estimation.
Secure Routing for MANET in Adversarial EnvironmentIJCERT
Collection of mobile nodes is known as ad-hoc network in which wireless communication is used to connect these mobile nodes. A major requirement on the MANET is to provide unidentifiability and unlinkability for mobile nodes. There are various secure routing protocols have been proposed, but the requirement is not satisfied. The existing protocols are unguarded to the attacks of fake routing packets or denial-of-service broadcasting, even the node identities are protected by pseudonyms. We propose a new secure routing protocol which provides anonymity named as authenticated anonymous secure routing (AASR), to satisfy the requirement of mobile networks and defend the attacks. The route request packets are authenticated by a group signature and public key infrastructure, to defend the potential attacks without exposing the node identities. The cocept of key-encrypted onion routing which provides a route secret verification message, to prevent intermediate nodes from inferring a real destination. Simulation results have demonstrated the effectiveness of the proposed AASR protocol with improved performance as compared to the existing protocols.
Single User Eigenvalue Based Detection For Spectrum Sensing In Cognitive Rad...IJMER
Scarcity of spectrum is the issue that wireless communication technology has to deal with.
Primary user is the licensed user of the spectrum. When primary user is idle or not using the spectrum
secondary user can utilize the spectrum. This sharing of spectrum can be enabled by cognitive radio
(CR) technology. The heart of enabling this technology is spectrum sensing. Spectrum sensing involves
detection of primary signal at very low SNR (in the range of -20 dB), under noise and interference
uncertainty. This requirement makes spectrum sensing, a hard nut to crack. Another major issue in
detection is hidden node problem wherein the node in vicinity of primary transmitter also indicates
absence of the primary signal since it is hidden behind the large object. There are various algorithms
for detection viz. energy detection, matched filter detection, feature detection (cyclostationary
detection, eigen-value based detection etc.) These algorithms have limitations of complexity,
requirement of signal knowledge and detection performance. In this paper the performance of
eigenvalue based detection (EBD) method in presence of noise and low SNR of the received signal has
been evaluated for a single user.
Implementation and demonstration of li fi technologyeSAT Journals
Abstract Li-Fi is a wireless communication system in which light is used as a carrier signal instead of traditional radio frequency as in Wi-Fi. Li-Fi is a technology that uses light emitting diodes to transmit data wirelessly. Li-Fi is a form of Visible Light Communication (VLC). VLC uses rapid pulses of light to transmit information wirelessly that cannot be detected by the human eye. This paper demonstrates the working of Li-Fi by simulating a simple circuit which gave us the required output. Li-Fi technology was first demonstrated by Harald Hass, a German Physicist from the University of Edinburgh Keywords—Li-Fi, VLC, Optical Communication, Wireless Communication, LED, Visible Light Spectrum.
As the enormous use of internet increases day by day so as security concern is also raise day by day over
the internet. In this paper we discuss the network security and its related threats and also study the types of
protocols and few issues related to protocols in computer networks. We also simulate the design of 5 node
wired network scenario, its packet drop rate analysis through TCP protocol using NS2 as a simulator.
Analyzed the performance of 5-node network when the packet is drop down by graphical method also
called as Xgraph when rate parameter is in mb and also analyzed the performance of same network by
changing the value of rate parameter at same time so no packets would drop down at same time and also
analyzed the performance by Xgraph method.
Adaptive wavelet thresholding with robust hybrid features for text-independe...IJECEIAES
The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal background model Gaussian mixture model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios.
Machine learning based lightweight interference mitigation scheme for wireles...TELKOMNIKA JOURNAL
The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference.
Analysis of different digital filters for received signal strength indicatorjournalBEEI
Due to high demand in Internet of Things applications, researchers are exploring deeper alternative methods to provide efficiency in terms of application, energy, and cost among other factors. A frequently used technique is the Received Signal Strength Indicator value for different Internet of Things applications. It is imperative to investigate the digital signal filter for the Received Signal Strength Indicator readings to interpret it into more reliable data. A contrasting analysis of three different types of digital filters is presented in this paper, namely: Simple Moving Average filter, Alpha Trimmed Mean filter and Kalman filter. There are three criteria used to observe the performance of these digital filters which are noise reduction, data proximity and delays. Based on the criteria, the choice of digital signal processing filter can be determined in accordance with its implementations in [ractice. For example, Alpha-Trimmed Meanfilter is shown to be more efficient if used in the pre-processing of Received Signal Strength Indicator readings for physical intrusion detection due to its high data proximity. Hence, this paper illustrates the possibilities of the use of Received Signal Strength Indicator in different Internet of Things applications given a proper choice of digital signal processing filter.
OPTIMIZATION OF THE RECURSIVE ONE-SIDED HYPOTHESIS TESTING TECHNIQUE FOR AUTO...ijwmn
In this paper, an optimized Recursive One-Sided Hypothesis Testing (ROHT) threshold estimation algorithm for energy detection based on Cognitive Radio (CR) application is presented. The ROHT algorithm is well known to compute and correct threshold values based on the choice of the parameter
values; namely the coefficient of standard deviation (z-value) and the stopping criteria (). A fixed computational process has been employed in most cases to estimate these parameter values, thus rendering them non-adaptive under different sensing conditions. Also, this fixed (manual tuning) process requires prior knowledge of some noise level to enable pre-configuration of a predefined target false alarm rate.
This renders the parameter estimation process cumbrous and unworkable for real-time purposes, particularly for autonomous CR applications. Furthermore, using wrong parameter values may lead to either too high or too low false alarms or detection rates of the algorithm. Sequel to aforementioned mentioned constraints, we propose a new mechanism for instantaneous parameter optimization of the ROHT algorithm using Particle Swarm Optimization (PSO) algorithm. Our PSO-ROHT model design was extensively tested under different conditions and results were compared to the non-optimized ROHT. The
results obtained show that the proposed design effectively adapts the parameter values of the Recursive One-Sided Hypothesis Testing algorithm in accordance with the input dataset under consideration. Also, that the proposed optimized model outperforms its non-optimized counterpart following the estimated detection probability and false alarm probability of both schemes, particularly in detecting Orthogonal Frequency-Division Multiplexing signals. In detecting the Orthogonal Frequency-Division Multiplexing signals at signal-to-noise ratio of 3dB and above, the proposed model achieved a higher detection rate of 96.23% while maintaining a low false alarm rate below 10%, which complies with the IEEE 802.22standard for Cognitive Radio application. Our PSO-ROHT algorithm is shown to be highly effective for autonomous and full blind signal detection in CR, with strong potentials for application in other areas requiring automatic threshold estimation.
Secure Routing for MANET in Adversarial EnvironmentIJCERT
Collection of mobile nodes is known as ad-hoc network in which wireless communication is used to connect these mobile nodes. A major requirement on the MANET is to provide unidentifiability and unlinkability for mobile nodes. There are various secure routing protocols have been proposed, but the requirement is not satisfied. The existing protocols are unguarded to the attacks of fake routing packets or denial-of-service broadcasting, even the node identities are protected by pseudonyms. We propose a new secure routing protocol which provides anonymity named as authenticated anonymous secure routing (AASR), to satisfy the requirement of mobile networks and defend the attacks. The route request packets are authenticated by a group signature and public key infrastructure, to defend the potential attacks without exposing the node identities. The cocept of key-encrypted onion routing which provides a route secret verification message, to prevent intermediate nodes from inferring a real destination. Simulation results have demonstrated the effectiveness of the proposed AASR protocol with improved performance as compared to the existing protocols.
Single User Eigenvalue Based Detection For Spectrum Sensing In Cognitive Rad...IJMER
Scarcity of spectrum is the issue that wireless communication technology has to deal with.
Primary user is the licensed user of the spectrum. When primary user is idle or not using the spectrum
secondary user can utilize the spectrum. This sharing of spectrum can be enabled by cognitive radio
(CR) technology. The heart of enabling this technology is spectrum sensing. Spectrum sensing involves
detection of primary signal at very low SNR (in the range of -20 dB), under noise and interference
uncertainty. This requirement makes spectrum sensing, a hard nut to crack. Another major issue in
detection is hidden node problem wherein the node in vicinity of primary transmitter also indicates
absence of the primary signal since it is hidden behind the large object. There are various algorithms
for detection viz. energy detection, matched filter detection, feature detection (cyclostationary
detection, eigen-value based detection etc.) These algorithms have limitations of complexity,
requirement of signal knowledge and detection performance. In this paper the performance of
eigenvalue based detection (EBD) method in presence of noise and low SNR of the received signal has
been evaluated for a single user.
Implementation and demonstration of li fi technologyeSAT Journals
Abstract Li-Fi is a wireless communication system in which light is used as a carrier signal instead of traditional radio frequency as in Wi-Fi. Li-Fi is a technology that uses light emitting diodes to transmit data wirelessly. Li-Fi is a form of Visible Light Communication (VLC). VLC uses rapid pulses of light to transmit information wirelessly that cannot be detected by the human eye. This paper demonstrates the working of Li-Fi by simulating a simple circuit which gave us the required output. Li-Fi technology was first demonstrated by Harald Hass, a German Physicist from the University of Edinburgh Keywords—Li-Fi, VLC, Optical Communication, Wireless Communication, LED, Visible Light Spectrum.
As the enormous use of internet increases day by day so as security concern is also raise day by day over
the internet. In this paper we discuss the network security and its related threats and also study the types of
protocols and few issues related to protocols in computer networks. We also simulate the design of 5 node
wired network scenario, its packet drop rate analysis through TCP protocol using NS2 as a simulator.
Analyzed the performance of 5-node network when the packet is drop down by graphical method also
called as Xgraph when rate parameter is in mb and also analyzed the performance of same network by
changing the value of rate parameter at same time so no packets would drop down at same time and also
analyzed the performance by Xgraph method.
Adaptive wavelet thresholding with robust hybrid features for text-independe...IJECEIAES
The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal background model Gaussian mixture model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios.
Machine learning based lightweight interference mitigation scheme for wireles...TELKOMNIKA JOURNAL
The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference.
Comparative Performance Analysis of Wireless Communication Protocols for Inte...chokrio
The systems based on intelligent sensors are currently expanding, due to theirs functions and theirs performances of intelligence: transmitting and receiving data in real-time, computation and processing algorithms, metrology remote, diagnostics, automation and storage measurements…The radio frequency wireless communication with its multitude offers a better solution for data traffic in this kind of systems. The mains objectives of this paper is to present a solution of the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features. The comparison between the different wireless technologies (Wi-Fi, Wi-Max, UWB, Bluetooth, ZigBee, ZigBeeIP, GSM/GPRS) focuses on their performance which depends on the areas of utilization. Furthermore, it shows the limits of their characteristics. Study findings can be used by the developers/ engineers to deduce the optimal mode to integrate and to operate a system that guarantees quality of communication, minimizing energy consumption, reducing the implementation cost and avoiding time constraints.
Ensemble Learning Approach for Digital Communication Modulation’s Classificationgerogepatton
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
Ensemble Learning Approach for Digital Communication Modulation’s Classificationgerogepatton
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
ENSEMBLE LEARNING APPROACH FOR DIGITAL COMMUNICATION MODULATION’S CLASSIFICATIONijaia
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Review of Physical, Data and Network Layer Specification and its Protocols ...Editor IJMTER
Wireless sensor network is a group of smart sensors, each capable of sensing, processing and
communicating the data or messages when any event occurs. When nodes deployed in numbers it form a
network which collectively monitor the state of the phenomenon activity of physical world. Its applications and
potential benefits are remarkable and seem only limited by imagination. The interdisciplinary property makes
the challenges wide and deep for design network protocols, efficient power utilization, programming models
and application areas. This survey paper focuses on the basic of WSN technology and its design factors. It also
focuses on the physical layer issues, data link layer protocols and its services and protocols used in layers.
Optimal Operating Performances of Wireless Protocols for Intelligent Sensors ...chokrio
The systems based on intelligent sensors are currently expanding, due to theirs functions and theirs performances of intelligence: transmitting and receiving data in real-time, computation and processing algorithms, metrology remote, diagnostics, automation and storage measurements…The radio frequency wireless communication with its multitude offers a better solution for data traffic in this kind of systems. The mains objectives of this paper is to present a solution of the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features. The comparison between the different wire-less technologies (Wi-Fi, Wi-Max, UWB, Bluetooth, ZigBee, ZigBeeIP, GSM/GPRS) focuses on their performance which depends on the areas of utilization. Furthermore, it shows the limits of their characteristics. Study findings can be used by the developers/ engineers to deduce the optimal mode to integrate and to operate a system that guarantees quality of communication, minimizing energy consumption, reducing the implementation cost and avoiding time con-straints.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
Wideband Sensing for Cognitive Radio Systems in Heterogeneous Next Generation...CSCJournals
Mobile Next Generation Network (MNGN) is characterized as heterogeneous network where variety of access technologies are meant to coexist. Decisions on choosing an air interface that meets a particular need at a particular time will be shifted from the network’s side to (a more intelligent) user’s side. On top of that network operators and regularities have come to the realization that assigned spectrum bands are not utilized as they should be. Cognitive radio stands out as a candidate technology to address many emerging issues in MNGN such as capacity, quality of service and spectral efficiency. As a transmission strategy, cognitive radio systems depend greatly on sensing the radio environment. In this paper, we present a novel approach for interference characterization in cognitive radio networks based on wideband chirp signal. The results presented show that improved sensing accuracy is maintained at tolerable system complexity.
Differential equation fault location algorithm with harmonic effects in power...TELKOMNIKA JOURNAL
About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.
Similar to Automatic recognition of the digital modulation types using the artificial neural networks (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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
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
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 6, December 2020 : 5871 - 5882
5872
the type of modulation [3]. Obviously, this method of recognition requires a lot of effort versus poor
performance and a high probability of error because it relies heavily on operator skills and experience [1].
The use of transmission recognition methods leads to the use and development of new ART like the input of
the received modulated signal to all possible signal detectors and then recognition of the modulation type by
comparing the output values of all detectors. The difficulty and poor performance of this method are also clear
and are no longer used, especially after the use of digital data [4].
Figure 1. Block diagram for a communications system to recognize the type of modulation
and demodulation [2]
In the mid and late of the 1980s, two important ways of ART, which led to a significant development
in this area. These two methods are the decision theoretic approach (DT) and the static pattern (SP) and both
methods depend on guessing a separation algorithm and choose the appropriate thresholds for them [5, 6].
It is noted here that if these thresholds are not selected very precisely, this will result in a decrease in
the algorithm performance, especially when the noise is present, so there is a need to find other methods to
recognize the type of modulation that is better performing even with noise.
Since the beginning of the 1990s [7, 8], there has been oriented towards industrial intelligence in
the general recognition and classification processes, which has allowed it to be used to recognize the types of
modulation. This step has greatly improved the performance of the system than it was, especially in
the presence of noise. Most of the previously proposed methods were designed to recognize analog modulation
methods, but recent contributions and researches on this subject are more focused on digital communications
because of the increasing use of digital modulation [8, 9].
Most of the existing ART of the modulation type can recognize and classify a few types of digital
modulation methods [8]. Most of which can determine the type of digital modulation from signals with high
SNR, but with increasing the noise, the results will be worst and the system then will be unable to determine
the type of digital modulation. The importance of this research is to adapt artificial intelligence to propose an
ART system capable of recognizing and classifying many different types of digital modulation methods. Using
an Artificial Neural Network with its complex algorithm is to increase both the performance of the system and
the immunity against noise even with signals of low SNR.
2. RESEARCH AIM
This paper depends on the computational simulation method and mathematical modeling by using
MATLAB program for its accuracy and simplicity to convert the models based on it to the real-time
applications and followed the following methodology:
- Study and analyze the types of digital modulation under study.
- Investigation of the most important parameters of the studied digital modulation techniques.
- Analyze and compare these parameters at different values of SNR and examine their immune to
this noise.
- The establishment of the artificial neural network (ANN), which will separate the types of digital
modulation understudy and then learning this network by two vectors (the inputs and targets vectors) that
extracted from the key features with different noise levels. Through these two vectors, the network will be
learned to be able to recognize the digital modulation type used even if the SNR has decreased and reached
a low value.
- Testing the ANN.
3. MODULATION METHODS UNDER STUDY
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The modulation is one of the most important operations in the transmitter of the communications
system. Since we cannot send the RF signal of the message wirelessly directly through the transmission
channel, because it is usually of low frequencies, i.e. with large wavelengths and since the antenna length is
usually equal to half or a quarter of the wavelength, its realization is practically impossible. The frequencies of
various messages are also located within the same bandwidth. If the signals were sent directly, it will be
overlapped making their separation in the receiver impossible. Therefore, these messages or signals
are carried on high-frequency waves called (carriers) and supposed to have the corresponding frequency
characteristics of the transmission channel. This carrying process is called modulation. In digital modulation
techniques, an analog carrier signal is usually modulated by a binary message code, and this is carried out by
changing one of the physical characteristics of the analog carrier (amplitude, frequency, phase or
a combination of them) according to the change in the instantaneous value of the message signal containing
the information [2, 10]. When the signal passes through a channel, it loses some of its energy and this is due to
the impedance of the channel which causes the attenuation [11]. The shape of the signal also changes due to
the distortion. The attenuation and distortion occur when more than one signal arrives through a lousy channel
to the receiver side with different frequencies. Random noise is one of source of transmission losses,
which generated from artificial sources and several natural. These losses in the transmitted signal make
the identification of the modulation type used in the transmission of the signal is very difficult. In this paper,
we will try to recognize the digital modulation type using artificial intelligence, so we'll take a quick look at
the four types of digital modulation presented in this study. Figure 2 illustrate the diference between the theae
modulation types (ASK, FSK, and PSK).
Figure 2. Digital modulation signal with binary ASK, FSK, and PSK
3.1. Amplitude shift keying (ASK)
This method of digital modulation corresponds to the amplitude modulation (AM) in the analog
modulation. ASK is the amplitude modulation of the sinusoidal carrier wave, but with a digital signal,
i.e. consists of ones and zeros. Thus, the carrier's amplitude changes as a result of the modulation between two
values correspond to the Zeros and Ones as shown in Figure 2. Figure 3 show the digital modulation scheme
of the binary ASK [11].
Figure 3. Digital modulation scheme of the binary ASK
3.2. Frequency shift keying (FSK)
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This method of digital modulation corresponds to the frequency modulation (FM) method of analog
modulation. This frequency modulation is relatively simple and has low performance. The carrier frequency
changes here between two values, when the digital modulation signal is a logical zero, the transmitted
frequency is f1, while when the digital modulation signal is a logical one, the transmitted frequency is f2,
as shown in Figure 2 while Figure 4 shows the digital modulation scheme of the binary FSK [11].
3.3. Phase shift keying (PSK)
This method in the digital modulation corresponds to the phase modulation method (PM) in
the analog modulation. The PSK modulation is a form of frequency modulation, and the resulting signal after
the modulation has a limited number of positions. When the number of phase positions of the output phase is
two, the modulation method is called binary phase shift keying (BPSK) as shown in Figure 2 [11].
When phase positions of the output phase are four, the modulation called quadrature phase shift keying
(QPSK). The phase positions of the output phase may become eight, which is called the eight-phase shift keying
that is denoted by (8PSK), and the number of phase angle positions can be sixteen, the modulation being
described as the hexadecimal phase shift keying and denoted by (16PSK) see Figure 5 for the digital modulation
scheme of the binary PSK.
Figure 4. Digital modulation scheme of
the binary FSK
Figure 5. Digital modulation scheme of
the binary PSK
3.4. Orthogonal QAM modulation
Orthogonal QAM modulation is a type of digital modulation, where the information is located in both
amplitude and phase of the signal. It is difficult to distinguish between a quadrilateral and 4PSK, but it is easy
to note the difference between QAM and PSK when the number of levels is higher than 4 or 16 (see Figure 6
that illustrates the digital modulation scheme of QAM) [11].
Figure 6. Digital modulation scheme of QAM
4. KEY FEATURES
The major challenge in the modulation recognition and classification systems is always in the process
of finding a set of key features. These keys must be reduced in order to reduce the number of inputs on
the recognizer or the classifier (artificial neural network in this paper) also its complexity must be low to be
easy to calculate and suitable for real-time applications. At the same time, it must include all the necessary key
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features to ensure a reliable recognition and classification process. So, the ART process can be representing
a balancing process between these two considerations.
Many numerous properties are imposed by different digital signals. Therefore, the appropriate features
should be selected to identify these signals. These features are selected depending on their sensitivity to
the type of digital modulation and on being insensitive to noisy channels and variations of SNR.
The eight key features proposed and studied in this paper can be calculated from the instantaneous
characteristics of the signal (instantaneous amplitude, instantaneous frequency, and non-linear instantaneous
phase) as shown in Figure 7. ASK can be recognized depending based on their amplitude variations,
the FSK may be classified by its fixed instantaneous amplitude, and the PSK information is defined by its
phase [2, 12-14].
Figure 7. Key features
The eight features have been selected and used in this paper to separate the following basic digital
modulation methods (64QAM, 2PSK, 4PSK, 8PSK, 4ASK, 2FSK, 4FSK, 8FSK). These key features, in turn,
are based on three values of the received signal 𝑦(𝑖) which is:
4.1. Instantaneous amplitude 𝒂(𝒊):
Instantaneous amplitude represents the instantaneous value of the signal 𝑦(𝑖). This instantaneous
value can produce two important signals: the modified instantaneous value 𝐴 𝑛(𝑖) and the central modified
instantaneous value 𝐴 𝑐𝑛(𝑖) [12].
Modified instantaneous value 𝐴 𝑛(𝑖):
𝐴 𝑛(𝑖) =
𝐴(𝑖)
𝑚 𝐴
(1)
Central modified instantaneous value 𝐴 𝑐𝑛(𝑖):
𝐴 𝑐𝑛(𝑖) = [
𝐴(𝑖)
𝑚 𝐴
] − 1 (2)
where 𝑚 𝐴 represents the average value of the instantaneous terminal.
4.2. Non-linear instantaneous phase ∅ 𝑵𝑳(𝒊)
The instantaneous phase of the modulated signal consists of two components:
Linear component: This is caused by the carrier. A non-linear component: which is caused by the modulated
signal (information). The non-linear component 𝜑 𝑁𝐿(𝑖) for the phase of the signal y(i) calculated by subtracting
the linear component of the phase from the overall phase 𝜑(𝑖) [12].
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4.3. Instantaneous frequency
The instantaneous frequency represented by:
𝑓(𝑖) =
1
2𝜋
𝑑𝜑(𝑖)
𝑑𝑡
(3)
This means, that the instantaneous frequency represents the instantaneous changes in phase. This instantaneous
frequency can produce an important signal, which is the instantaneous central frequency that is given by:
𝑓𝑁(𝑖) = [
𝑓(𝑖)−𝑚 𝑓
𝑓 𝑑
] (4)
where 𝑚 𝑓 represents the main value of instantaneous frequency and 𝑓𝑑 represent the symbol rate.
4.4. Key features
The first key: (𝜎 𝑝) the standard deviation of the nonlinear instantaneous phase: This key feature helps
to separate between digital modulation methods that carry the information by phase from other modulation
methods. In such methods, there will be a transition of the instantaneous phase with different phase-steps,
depending on the level of the modulation, so the value of this key will be small for these modulations [14].
𝜎 𝑝 = √
1
𝑁 𝑐
(∑ 𝜑 𝑁𝐿
2 (𝑖)𝐴 𝑛(𝑖)>𝐴 𝑡
) −
1
𝑁 𝑐
(∑ |𝜑 𝑁𝐿(𝑖)|𝐴 𝑛(𝑖)>𝐴 𝑡
)
2
(5)
where 𝑁𝑐 is the number of samples in {𝜑 𝑁𝐿} for 𝐴 𝑛(𝑖) > 𝐴 𝑡, where 𝐴 𝑡 is the threshold value of 𝐴 𝑛(𝑖) when
the filter provides the minimum amplitude of the signal sample due to high noise sensitivity and 𝜑 𝑁𝐿(𝑖) is
the nonlinear component of the ith
instantaneous phase of the sample.
The second key: (𝑚 𝑎) the average value of the instantaneous amplitude: The average value of
the instantaneous amplitude for all modulation methods under study except (64QAM, 4ASK) is close to one.
For the 4ASK method, since the absolute value of the signal changes between the two values {1, 1/3},
the average value of the instantaneous amplitude is equal to 2/3, while this value will be greater than one for
the orthogonal amplitude modulation method (64QAM) [14].
The third key: (𝜎𝑓) Standard deviation of instantaneous frequency: The digital modulation methods
that carry the information by frequency are 8FSK, 4FSK, 2FSK and therefore its instantaneous frequency
changes either between two values for the 2FSK method or between four value for the 4FSK method or between
eight values for the method 8FSK. Although the intervals between these values are the same for all these
methods, while for other methods, their instantaneous frequency is equal to zero. This key is therefore used to
differentiate the digital modulation methods (8FSK, 4FSK, 2FSK) from the other modulation methods and this
key can also be used to differentiate between these methods (the instantaneous frequency change area in 8FSK
method is twice the area of frequency change in the 4FSK method, which is four times the area of frequency
changes in the 2FSK method [14].
𝜎𝑓 = √
1
𝑁 𝑐
(∑ 𝑓𝑁
2(𝑖)𝐴 𝑛(𝑖)>𝐴 𝑡
) −
1
𝑁 𝑐
(∑ |𝑓𝑁(𝑖)|𝐴 𝑛(𝑖)>𝐴 𝑡
)
2
(6)
The fourth key: (𝜎 𝑎) The standard deviation of the instantaneous amplitude: It is another key feature
that performs the same work of the key (𝑚 𝑎) and is used for the same features [14].
𝜎 𝑎 = √
1
𝑁 𝑐
(∑ 𝐴 𝑐𝑛
2 (𝑖)𝑁
𝑖=1 ) −
1
𝑁 𝑐
(∑ |𝐴 𝑐𝑛(𝑖)|𝑁
𝑖=1 )2 (7)
The fifth key: (𝛾 𝑚𝑎𝑥 𝑓) The maximum value of the spectral power density of the central instantaneous
frequency: This key helps to differentiate between the digital modulation methods (8FSK, 4FSK, 2FSK) and
any other modulation methods (as (𝜎𝑓) works). As the instantaneous frequency change area of 8FSK is twice
the area of instantaneous frequency change for 4FSK and four times the area of instantaneous frequency change
for 2FSK so the 8FSK method has a higher value than they are in the two cases 4FSK and 2FSK which enables
the differentiation between 8FSK, 4FSK, 2FSK methods. This key can be determined by [14]:
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𝛾max 𝑓 = 𝑚𝑎𝑥|𝐷𝐹𝑇(𝑓𝑐𝑛(𝑖))|
2
(8)
The symbol DFT represents the Discrete Fourier transform.
The sixth key: (𝛾max 𝑎) the maximum value of the spectral power density of the central instantaneous
amplitude: This key helps to differentiate between the digital modulation methods that carry the information
by amplitude i.e. (64QAM, 4ASK) and other methods of modification. This feature is given in the following
relationship [14]:
𝛾max 𝑎 = 𝑚𝑎𝑥|𝐷𝐹𝑇(𝐴 𝑐𝑛(𝑖))|
2
/𝑁𝑠 (9)
where 𝑁𝑠 represents the number of samples in one frame of the signal.
The seventh key: (𝐾𝑢𝑟𝑎) The fourth-degree torque of the central instantaneous amplitude: which
given by:
𝐾𝑢𝑟𝑎 =
𝐸{𝐴 𝑐𝑛
4 (𝑛)}
{𝐸{𝐴 𝑐𝑛
2 (𝑛)}}
2 (10)
where E {} represents the expected value.
The fourth-degree torque represents the kurtosis around the average. It’s found that this key helps to
differentiate between digital modulation methods that carry information in the amplitude, which has a small
kurtosis than other methods [14].
Eighth key: (𝐾𝑢𝑟𝑓) The fourth-degree torque of the central instantaneous frequency: which given by:
𝐾𝑢𝑟𝑓 =
𝐸{𝑓 𝑁
4(𝑛)}
{𝐸{𝑓 𝑁
2(𝑛)}}
2 (11)
This feature helps to differentiate between digital modulation methods that carry information by frequency of
the other methods of modification [14].
5. ARTIFICIAL NEURAL NETWORKS
For the recognition and classification processes, many techniques were found [3], one of these
techniques was the artificial neural network (ANN), which is one of the most appropriate methods in
the signal classification [3, 15]. Artificial neural networks are a collection of algorithms, working as
the human brain neurons. These networks are designed to recognize patterns. The patterns identified by these
networks are numerical, included in vectors, which must be translated into all real-world data, i.e. images,
audio, text or time series. ANNs help to recognize and classify [16-18].
ANN merits in the ART are its high classification rate, reduced computational complexity,
and improved accuracy. While its demerits are ineffective when a large number of input features are used and
any small data set requires long training time for computation. In this paper, depending on neural network
characteristics, we used the feedforward with multi-layered neural networks as a classifier and identifier and
for the optimization of the network weights, we use backpropagation algorithm as the most common
algorithms, using the controlled learning method, with the number of training pairs composed of patterns vector
and targets vector as shown in Figure 8.
The aim of weights optimization is to get a stable state that makes the network properly responsive to
the patterns vector used in the training process and to any new input vector and the training process continues
until the error that exists between the output vector and the target to the smallest value (threshold value) which
is predetermined (see Figure 8).
In this paper, the previous eight features used to build the patterns vector. The patterns vector was got
from the concluding of the key features from a noisy modulated signal, i.e. a signal that crossed
a communication channel, and a known modulation type and so we have another vector which is the targets
vector. The neural network was learned through the patterns and targets vectors to get the ability to recognize
and classify the type of digital modulation. This neural network was subjected to several experiments to change
and modify its structure (changing the number of hidden layers or the number of nodes in these hidden layers
or change the learning algorithm) until it finally got the best results appropriate for this application [19-25].
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Figure 8. The block diagram of the backpropagation algorithm
6. RESULTS AND DISCUSSION
The MATLAB environment was used to write system programs because it is a high-level language
that contains libraries for communication and signal processing systems as well as a library of neural networks.
So, we used parameters of this library to design, learning, and test the back-propagation, artificial neural
network used in our system. Simulation programs to generate digital modulation signals proposed in this paper
at a sampling frequency 𝑓𝑠 = 1200𝑘𝐻𝑧 and the coding rate 𝑓𝑑 = 12.5𝑘𝐻𝑧 to produce a specified number of
frames for each digital modulation method and then partition the signal within a frame of length (4096) sample
that determined to achieve the best results.
The eight key features of each frame were then extracted and grouped into one vector and for the best
performance, a median filter was used with a window of seven samples, a sixth-degree non-recursive filter used
to pass the signal through it before concluding the key features. Figure 9 to Figure 12 show the relationship
between the key features of the digital modulation methods understudy with the signal-to-noise ratio that changes
between (0-20 dB) for an AWGN communication channel. We have included these curves to show how reliable
these parameters are and how it is immune to Noise.
In order to give the learning to the artificial neural network, the key features were calculated at two
values of SNR to eventually form the input vector (pattern vector) and the output vector (target vector) used to
learn the network. The neural network proposed here consisting of an input layer with a number of nodes equal
to the number of key features, two hidden layers and an output layer with a number of nodes equal to
the number of modulation methods that must be recognized. Figure 9 to Figure 12 illustrate that with small
values of SNR, most of the modulation methods are clear and can be detected but when the SNR increases,
the value of key features begins to decrease clearly and converge with each other so that there is a difficulty to
recognize between these methods while the methods that depend on the specified key feature will be
distinguishable.
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(a) (b)
Figure 9. The effect of SNR for, (a) standard deviation of the non-linear instantaneous phase,
(b) the average value of the instantaneous amplitude
(a) (b)
Figure 10. The effect of SNR for, (a) standard deviation of the instantaneous frequency,
(b) the standard deviation of the instantaneous amplitude
(a) (b)
Figure 11. The effect of SNR for, (a) the maximum value of the spectral power density of the central
instantaneous frequency, (b) the maximum value of the spectral power density of the central
instantaneous amplitude
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(a) (b)
Figure 12. The effect of SNR for, (a) the fourth-degree torque of the central instantaneous amplitude,
(b) the fourth-degree torque of instantaneous central frequency attributed
After completing the learning of the artificial neural network, we tested this network to recognize
a testing modulated signals transmitted to the network, see Figure 13. As seen in Table 1, the modulation type
with deferent recognition techniques and deferent SNRs used in some of the previous works are compared with
the results of this paper.
Figure 13. Recognition percentage ratios for each modulation type at different values SNR
Table 1. A comparison of the results of this study with some of the classification methods of digital
modulation used previously
References The used modulation types The used model and technique The obtained success rate (%)
Almohamad et al.
[26]
2ASK, QPSK, 16QAM PCA + SVM
(SNR: 0 - 30 dB)
99.83 (on average)
Ali, and Yangyu
[9]
BPSK, 4QAM, 16QAM,
64QAM
2-Layered deep neural network model
(SNR: 10 dB)
95.40
Dai et al.
[27]
2PAM, 4PAM, 8PAM,
2PSK, 4PSK, 8PSK,
16QAM, 64QAM,
256QAM
A fully connected 2-layer feed-forward
deep neural network (DNN)
(SNR: 15 dB)
94.50
Our work 64QAM, 2PSK, 4PSK,
8PSK, 4ASK, 2FSK,
4FSK, 8FSK
Feedforward + Backpropagation with 2
hidden layers ANN
(SNR: 5, 10, and 15 dB)
68%, 89.1%, 91% respectively
but when we exclude the PSK,
the result will be 75.2%, 98.8%,
99.6% respectively
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7. CONCLUSION
In this study, an artificial neural network with an input-layer of eight nodes, two hidden-layers and an
output-layer of eight nodes too are used to get the best results. The proposed system proves its ability to achieve
a recognition ratio of 68% for a testing signal with SNR=5dB, and 89.1% for a testing signal with SNR=10dB
and 91% is known for a testing signal with SNR=15dB. The channel proposed in this work was the AWGN
and hence the relatively low recognition rate for the SNR=5dB is due to the high sensitivity of some of
the selected determinants of high noise. Some types of modulation understudy got the best recognition ratio,
where the 64QAM was the best one, while another modulation technique which is the 4PSK got the lowest
recognition. Truly, when we exclude all the PSK modulation method studded in this paper, the result will be
75.2%, 98.8%, 99.6% for the SNR= (5dB, 10dB, and 15dB) respectively and that means, the PSK is very
sensitive to the AWGN channel. The proposed ANN recognition algorithm is very efficient in the 64QAM and
4ASK in any SNR ratio. For the M-FSK modulation method, the proposed algorithm can be representing
a good recognition method for the high values of SNR. The study also proved that the ANN algorithm is not
efficient for M-PSK modulation signals, especially for large M.
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BIOGRAPHY OF AUTHOR
Asst. Prof. Dr. Saad Saffah Hasson Hreshee was born in Baghdad, Iraq, in 1974.
He received his BSc degree in Electrical Engineering from the Electrical Engineering
Department, College of Engineering, University of Babylon, Iraq, in 1997. He obtained his MSc
in Electronic Engineering from the Electrical and Electronic Engineering Department,
University of Technology, Iraq, in 2000, while his PhD. in Electronic and Communication
Engineering from the Electrical Engineering Department, College of Engineering, University of
Basrah, Iraq, in 2007. Since 2001, he has been with the staff of the Department of Electrical
Engineering, College of Engineering, University of Babylon, where he is now the head of
the electrical engineering department. His main research interests are antenna, signal processing,
encryption and communication security.