This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear c...This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.展开更多
Pervasive wireless computing and communication have created an ever-increasing demand for more radio spectrum. Since, most of the spectrum is underutilized, it motivated the introduction of the concept of cognitive ra...Pervasive wireless computing and communication have created an ever-increasing demand for more radio spectrum. Since, most of the spectrum is underutilized, it motivated the introduction of the concept of cognitive radios, a dynamic spectrum access enabling technology. The first stage of cognitive radio is to sense the environment and determine which parts of the spectrum are available. This is achieved through spectrum sensing. However, spectrum sensing poses the most fundamental challenge in cognitive radios. Moreover, cognitive radios suffer from many vulnerabilities and the security attacks can severely degrade the performance of cognitive radios. This paper surveys state-of-theart research on spectrum sensing and security threats in cognitive radios. Lastly, we also consider the analysis of issues related to spectrum handoffs in cognitive radios.展开更多
Dear Editor： There is accumulating evidence that human blood electronic circuit components and their application circuits become more and more important to cyborg implant/engineering, man-machine interface, hu- man ...Dear Editor： There is accumulating evidence that human blood electronic circuit components and their application circuits become more and more important to cyborg implant/engineering, man-machine interface, hu- man disease detection and healing, and artificial brain evolutionusl. Here, we report the first development of human plasma-based amplifier circuit in the dis- crete as well as integrated circuit （IC） configuration mode. Electrolytes in the human blood contain an enormous number of charge carriers such as positive and negative molecule/atom ions, which are electri- cally conducting media and therefore can be utilized for developing electronic circuit components and their application circuits. These electronic circuits obvi- ously have very high application impact potential towards bio-medical engineering and medical science and technology.展开更多
In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile u...In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of applications.Spectrum prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio spectrum.Long short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum access.The proposed scheme provides an average maximum waiting time gain of 2.88 ms.The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.展开更多
This paper extends the Non-Circular MUltiple SIgnal Classification(MUSIC)(NC-MUSIC) method for the common array geometries including Uniform Circular Arrays(UCAs) and Uniform Rectangular Arrays(URAs),which enables the...This paper extends the Non-Circular MUltiple SIgnal Classification(MUSIC)(NC-MUSIC) method for the common array geometries including Uniform Circular Arrays(UCAs) and Uniform Rectangular Arrays(URAs),which enables the algorithm to estimate 2-D Direction Of Arrival(DOA).A comparison between UCAs and URAs of NC-MUSIC is made in this paper.The simulations show that the NC-MUSIC method doubles the maximum estimation number of standard MUSIC.Using non-circular signals,the performance of URAs is improved remarkably while the improvement of UCAs is not so significantly.Moreover,the influence of arrays structures on the NC-MUSIC method is discussed.展开更多
A double stage AC/DC sequential high voltage reactor has been developed to study the decomposition of benzene in the air stream at atmospheric pressure. The removal efficiency was measured as a function of ozonation, ...A double stage AC/DC sequential high voltage reactor has been developed to study the decomposition of benzene in the air stream at atmospheric pressure. The removal efficiency was measured as a function of ozonation, flow recycling, and flow recirculation. Ozonation in the inlet, and recycling of the exhaust stream increased the removal of benzene, also with increasing of specific input energy(J l^-1) the effect of inlet flow ozonation on benzene decomposition was enhanced. The highest removal efficiency was obtained up to >99% in recirculation six times,while CO2 selectivity reached 99.9% and energy efficiency was 0.59 g kWh^-1. O3 production/decomposition > production of OH radicals > electronic and ionic collisions were indicated as the main mechanisms influencing benzene abatement in this research.展开更多
Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered ...Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in Iran.First we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training data.The ANN was tested for both synthetic and real data.For real data some suitable features are derivate from residual anomalies and applied to展开更多
In this paper an effective size reduction technique using fractal structure is suggested. The proposed technique has been applied on a band stop and a low pass filter separately. This technique provides 42% reduction ...In this paper an effective size reduction technique using fractal structure is suggested. The proposed technique has been applied on a band stop and a low pass filter separately. This technique provides 42% reduction of size for the band stop filter and about 26% for the low pass counterpart. Both the designed structures are fabricated and the measured results are compared with the simulated results. The proposed technique does not require any recalculation or optimization of dimensions of the filter, and is straightforward to implement. The band stop filter is designed for the center frequency of 3 GHz where as the cut-off frequency for low pass filter is 2.5 GHz. A good agreement between the simulated and measured results is observed. A comprehensible explanation of the proposed technique is also provided.展开更多
Measurement of both oxygen saturation and blood flow in the retinal vessels has proved to give important information about the eye health and the onset of eye pathologies such as diabetic retinopathy.In this study,we ...Measurement of both oxygen saturation and blood flow in the retinal vessels has proved to give important information about the eye health and the onset of eye pathologies such as diabetic retinopathy.In this study,we present the implementation,on a commercially available fundus camera,of a retinal imager and a retina blood flow velocimeter.The retinal imager uses division of aperture to acquire nine wavelength-dependent sub-images of the retina.Careful consideration is taken to improve image transfer by measuring the optical properties of the fundus camera and modeling the optical train in Zemax.This part of the setup is calibrated with optical phantoms of known optical properties that are also used to build a lookup table(LUT)linking phantom optical properties to measured reflectance.The retina blood flow velocimeter relies on tracking clusters of erythrocytes and uses a fast acquisition camera attached to a zoom lens,with a green illumination LED-engine.Calibration is provided using a calibrated quartz capillary tube and human blood at a known flow rate.Optical properties of liquid phantoms are retrieved from measured reflectance using the LUT,and blood flow measurements in the retina are presented.展开更多
NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of servic...NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.展开更多
The multi-hop wireless networks that provide the feasible means of communication and information access in real time services are named as Mobile Ad-hoc Networks (MANETS). The Dual Busy-Tone Multiple Access (DBTMA) me...The multi-hop wireless networks that provide the feasible means of communication and information access in real time services are named as Mobile Ad-hoc Networks (MANETS). The Dual Busy-Tone Multiple Access (DBTMA) mechanism concedes the RTS-CTS scheme to establish communication between two nodes and medium access for applications with a high QoS requirement by assigning two narrow band busy-tones to notify the on-going transmission. In this paper, we obtained results relative to the interest of AODV based reactive routing protocol for MANETS and DBTMA mechanism. The performance is governed under real time sound traffic through simulation using NS-2. The performance of the protocol is measured in terms of various QoS metrics that include route discovery time, throughput, delay and hops per route which are calculated, and graphs have been plotted. A simulation result shows that very substantial improvements in terms of AODV performance parameters and minimum delay are attained due to increased routing responsiveness.展开更多
Deep learning is a machine learning technique that allows the computer to process things that occur naturally to humans.Today,deep learning techniques are commonly used in computer vision to classify images and videos...Deep learning is a machine learning technique that allows the computer to process things that occur naturally to humans.Today,deep learning techniques are commonly used in computer vision to classify images and videos.As a result,for challenging computer vision problems,deep learning provides state of the art solutions to it.Coral reefs are an essential resource of the earth.A new study finds the planet has lost half of its coral reefs since 1950.It is necessary to restore and prevent damage to coral reefs as they play an important role in maintaining a balance in the marine ecosystem.This proposed work helps to prevent the corals from bleaching and restore them to a healthy condition by identifying the root cause of the threats.In the proposed work,using deep learning CNN techniques,the images are classified into Healthy and Stressed coral reefs.Stressed coral reefs are an intermediate state of coral reef between healthy and bleached coral reefs.The pre-trained models Resnet50 and Inception V3 are used in this study to classify the images.Also,a proposed CNN model is built and tested for the same.The results of Inception V3 and Resnet50 are improved to 70%and 55%by tuning the hypermeters such as dropouts and batch normalisation.Similarly,the proposed model is tuned as required and obtains a maximum of up to 90%accuracy.With large datasets,the optimum amount of neural networks and tuning it as required brings higher accuracy than other methods.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, t...Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition(EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions(IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper a new EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region. Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithm identifies and extracts the reference signals against various ambient noises. Significant SNR improvement is also achieved for underwater acoustic signals.展开更多
Dear Editor,In this letter,we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data.Globally,more than 373 millio...Dear Editor,In this letter,we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data.Globally,more than 373 million people have been diagnosed with COVID-19 and 5.66 million have died from this disease by 2022.It continues to have a negative impact on human daily life and the global economic development till now,due to the lack of effective treatment of COVID-19 induced issues and prevention of transmission methods.展开更多
Active phased array antennas enhances the performance of modern radars by using multiple low power transmit/receive modules in place of a high power transmitter in conventional radars. Fully distributed phased array r...Active phased array antennas enhances the performance of modern radars by using multiple low power transmit/receive modules in place of a high power transmitter in conventional radars. Fully distributed phased array radars demand the distribution of various signals in radio frequency(RF) and digital domain for real time operation. This is normally achieved through complex and bulky coaxial distribution networks. In this work, we intend to tap the inherent advantages of fiber links with wavelength division multiplexed(WDM) technology and a feasibility study to adapt these links for radar applications is carried out. This is done by analysing various parameters like amplitude, delay, frequency and phase variation response of various radar waveforms over WDM links. This also includes performance evaluation of non-linear frequency modulation(NLFM) signals, known for better signal to noise ratio(SNR) to specific side lobe levels. NLFM waveforms are further analysed using pulse compression(PC) technique. Link evaluation is also carried out using a standard simulation environment and is then experimentally verified with other waveforms like RF continuous wave(CW), pulsed RF and digital signals. Synchronization signals are generated from this variable duty cycle digital signals during real time radar operation. During evaluation of digital signals, variable transient effects for different duty cycles are observed from an amplifier configuration. A suppression method is proposed to eliminate this transient effects. Further, the link delay response is investigated using different lengths of fiber spools. It can be inferred from the experimental results that WDM links are capable of handling various signals significant to radar applications.展开更多
This paper proposes a practical algorithm for systematically generating strong Boolean functions (f:GF(2) n →GF(2)) with cryptographic meaning. This algorithm takes bent function as input and directly outputs the res...This paper proposes a practical algorithm for systematically generating strong Boolean functions (f:GF(2) n →GF(2)) with cryptographic meaning. This algorithm takes bent function as input and directly outputs the resulted Boolean function in terms of truth table sequence. This algorithm was used to develop two classes of balanced Boolean functions, one of which has very good cryptographic properties:nl(f)=2 2k?1?2k+2k?2 (n=2k), with the sum-of-squares avalanche characteristic off satisfying σf=24k+23k+2+23k-2 and the absolute avalanche characteristic off satisfying σf=24k+23k+2+23k-2. This is the best result up to now compared to existing ones. Instead of bent sequences, starting from random Boolean functions was also tested in the algorithm. Experimental results showed that starting from bent sequences is highly superior to starting from random Boolean functions. Key words Boolean functions - Bent sequences - Nonlinearity - GAC - PC - Balancedness Document code A CLC number TP301.6展开更多
文摘This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.
文摘Pervasive wireless computing and communication have created an ever-increasing demand for more radio spectrum. Since, most of the spectrum is underutilized, it motivated the introduction of the concept of cognitive radios, a dynamic spectrum access enabling technology. The first stage of cognitive radio is to sense the environment and determine which parts of the spectrum are available. This is achieved through spectrum sensing. However, spectrum sensing poses the most fundamental challenge in cognitive radios. Moreover, cognitive radios suffer from many vulnerabilities and the security attacks can severely degrade the performance of cognitive radios. This paper surveys state-of-theart research on spectrum sensing and security threats in cognitive radios. Lastly, we also consider the analysis of issues related to spectrum handoffs in cognitive radios.
文摘Dear Editor： There is accumulating evidence that human blood electronic circuit components and their application circuits become more and more important to cyborg implant/engineering, man-machine interface, hu- man disease detection and healing, and artificial brain evolutionusl. Here, we report the first development of human plasma-based amplifier circuit in the dis- crete as well as integrated circuit （IC） configuration mode. Electrolytes in the human blood contain an enormous number of charge carriers such as positive and negative molecule/atom ions, which are electri- cally conducting media and therefore can be utilized for developing electronic circuit components and their application circuits. These electronic circuits obvi- ously have very high application impact potential towards bio-medical engineering and medical science and technology.
文摘In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of applications.Spectrum prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio spectrum.Long short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum access.The proposed scheme provides an average maximum waiting time gain of 2.88 ms.The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.
文摘This paper extends the Non-Circular MUltiple SIgnal Classification(MUSIC)(NC-MUSIC) method for the common array geometries including Uniform Circular Arrays(UCAs) and Uniform Rectangular Arrays(URAs),which enables the algorithm to estimate 2-D Direction Of Arrival(DOA).A comparison between UCAs and URAs of NC-MUSIC is made in this paper.The simulations show that the NC-MUSIC method doubles the maximum estimation number of standard MUSIC.Using non-circular signals,the performance of URAs is improved remarkably while the improvement of UCAs is not so significantly.Moreover,the influence of arrays structures on the NC-MUSIC method is discussed.
文摘A double stage AC/DC sequential high voltage reactor has been developed to study the decomposition of benzene in the air stream at atmospheric pressure. The removal efficiency was measured as a function of ozonation, flow recycling, and flow recirculation. Ozonation in the inlet, and recycling of the exhaust stream increased the removal of benzene, also with increasing of specific input energy(J l^-1) the effect of inlet flow ozonation on benzene decomposition was enhanced. The highest removal efficiency was obtained up to >99% in recirculation six times,while CO2 selectivity reached 99.9% and energy efficiency was 0.59 g kWh^-1. O3 production/decomposition > production of OH radicals > electronic and ionic collisions were indicated as the main mechanisms influencing benzene abatement in this research.
文摘Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in Iran.First we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training data.The ANN was tested for both synthetic and real data.For real data some suitable features are derivate from residual anomalies and applied to
文摘In this paper an effective size reduction technique using fractal structure is suggested. The proposed technique has been applied on a band stop and a low pass filter separately. This technique provides 42% reduction of size for the band stop filter and about 26% for the low pass counterpart. Both the designed structures are fabricated and the measured results are compared with the simulated results. The proposed technique does not require any recalculation or optimization of dimensions of the filter, and is straightforward to implement. The band stop filter is designed for the center frequency of 3 GHz where as the cut-off frequency for low pass filter is 2.5 GHz. A good agreement between the simulated and measured results is observed. A comprehensible explanation of the proposed technique is also provided.
基金the Coulter Foundation and NIH grant#EY017577-01A11.
文摘Measurement of both oxygen saturation and blood flow in the retinal vessels has proved to give important information about the eye health and the onset of eye pathologies such as diabetic retinopathy.In this study,we present the implementation,on a commercially available fundus camera,of a retinal imager and a retina blood flow velocimeter.The retinal imager uses division of aperture to acquire nine wavelength-dependent sub-images of the retina.Careful consideration is taken to improve image transfer by measuring the optical properties of the fundus camera and modeling the optical train in Zemax.This part of the setup is calibrated with optical phantoms of known optical properties that are also used to build a lookup table(LUT)linking phantom optical properties to measured reflectance.The retina blood flow velocimeter relies on tracking clusters of erythrocytes and uses a fast acquisition camera attached to a zoom lens,with a green illumination LED-engine.Calibration is provided using a calibrated quartz capillary tube and human blood at a known flow rate.Optical properties of liquid phantoms are retrieved from measured reflectance using the LUT,and blood flow measurements in the retina are presented.
基金the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabiafundedby Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia。
文摘NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.
文摘The multi-hop wireless networks that provide the feasible means of communication and information access in real time services are named as Mobile Ad-hoc Networks (MANETS). The Dual Busy-Tone Multiple Access (DBTMA) mechanism concedes the RTS-CTS scheme to establish communication between two nodes and medium access for applications with a high QoS requirement by assigning two narrow band busy-tones to notify the on-going transmission. In this paper, we obtained results relative to the interest of AODV based reactive routing protocol for MANETS and DBTMA mechanism. The performance is governed under real time sound traffic through simulation using NS-2. The performance of the protocol is measured in terms of various QoS metrics that include route discovery time, throughput, delay and hops per route which are calculated, and graphs have been plotted. A simulation result shows that very substantial improvements in terms of AODV performance parameters and minimum delay are attained due to increased routing responsiveness.
文摘Deep learning is a machine learning technique that allows the computer to process things that occur naturally to humans.Today,deep learning techniques are commonly used in computer vision to classify images and videos.As a result,for challenging computer vision problems,deep learning provides state of the art solutions to it.Coral reefs are an essential resource of the earth.A new study finds the planet has lost half of its coral reefs since 1950.It is necessary to restore and prevent damage to coral reefs as they play an important role in maintaining a balance in the marine ecosystem.This proposed work helps to prevent the corals from bleaching and restore them to a healthy condition by identifying the root cause of the threats.In the proposed work,using deep learning CNN techniques,the images are classified into Healthy and Stressed coral reefs.Stressed coral reefs are an intermediate state of coral reef between healthy and bleached coral reefs.The pre-trained models Resnet50 and Inception V3 are used in this study to classify the images.Also,a proposed CNN model is built and tested for the same.The results of Inception V3 and Resnet50 are improved to 70%and 55%by tuning the hypermeters such as dropouts and batch normalisation.Similarly,the proposed model is tuned as required and obtains a maximum of up to 90%accuracy.With large datasets,the optimum amount of neural networks and tuning it as required brings higher accuracy than other methods.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
文摘Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition(EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions(IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper a new EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region. Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithm identifies and extracts the reference signals against various ambient noises. Significant SNR improvement is also achieved for underwater acoustic signals.
基金This work was funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia(GCV19-37-1441).
文摘Dear Editor,In this letter,we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data.Globally,more than 373 million people have been diagnosed with COVID-19 and 5.66 million have died from this disease by 2022.It continues to have a negative impact on human daily life and the global economic development till now,due to the lack of effective treatment of COVID-19 induced issues and prevention of transmission methods.
文摘Active phased array antennas enhances the performance of modern radars by using multiple low power transmit/receive modules in place of a high power transmitter in conventional radars. Fully distributed phased array radars demand the distribution of various signals in radio frequency(RF) and digital domain for real time operation. This is normally achieved through complex and bulky coaxial distribution networks. In this work, we intend to tap the inherent advantages of fiber links with wavelength division multiplexed(WDM) technology and a feasibility study to adapt these links for radar applications is carried out. This is done by analysing various parameters like amplitude, delay, frequency and phase variation response of various radar waveforms over WDM links. This also includes performance evaluation of non-linear frequency modulation(NLFM) signals, known for better signal to noise ratio(SNR) to specific side lobe levels. NLFM waveforms are further analysed using pulse compression(PC) technique. Link evaluation is also carried out using a standard simulation environment and is then experimentally verified with other waveforms like RF continuous wave(CW), pulsed RF and digital signals. Synchronization signals are generated from this variable duty cycle digital signals during real time radar operation. During evaluation of digital signals, variable transient effects for different duty cycles are observed from an amplifier configuration. A suppression method is proposed to eliminate this transient effects. Further, the link delay response is investigated using different lengths of fiber spools. It can be inferred from the experimental results that WDM links are capable of handling various signals significant to radar applications.
文摘This paper proposes a practical algorithm for systematically generating strong Boolean functions (f:GF(2) n →GF(2)) with cryptographic meaning. This algorithm takes bent function as input and directly outputs the resulted Boolean function in terms of truth table sequence. This algorithm was used to develop two classes of balanced Boolean functions, one of which has very good cryptographic properties:nl(f)=2 2k?1?2k+2k?2 (n=2k), with the sum-of-squares avalanche characteristic off satisfying σf=24k+23k+2+23k-2 and the absolute avalanche characteristic off satisfying σf=24k+23k+2+23k-2. This is the best result up to now compared to existing ones. Instead of bent sequences, starting from random Boolean functions was also tested in the algorithm. Experimental results showed that starting from bent sequences is highly superior to starting from random Boolean functions. Key words Boolean functions - Bent sequences - Nonlinearity - GAC - PC - Balancedness Document code A CLC number TP301.6
