Abolfazl Razi - Academia.edu (original) (raw)

Papers by Abolfazl Razi

arXiv: Signal Processing, 2018

An important paradigm in smart health is developing diagnosis tools and monitoring a patient'... more An important paradigm in smart health is developing diagnosis tools and monitoring a patient's heart activity through processing Electrocardiogram (ECG) signals is a key example, sue to high mortality rate of heart-related disease. However, current heart monitoring devices suffer from two important drawbacks: i) failure in capturing inter-patient variability, and ii) incapability of identifying heart abnormalities ahead of time to take effective preventive and therapeutic interventions. This paper proposed a novel predictive signal processing method to solve these issues. We propose a two-step classification framework for ECG signals, where a global classifier recognizes severe abnormalities by comparing the signal against a universal reference model. The seemingly normal signals are then passed through a personalized classifier, to recognize mild but informative signal morphology distortions. The key idea is to develop a novel deviation analysis based on a controlled nonlinear ...

Electrocardiogram (ECG) signals are widely used to check heart rhythms and general health conditi... more Electrocardiogram (ECG) signals are widely used to check heart rhythms and general health conditions using low-cost and relatively accurate equipment. However, the majority of commercial off-the-shelf ECG kits are generic and their normal ranges are set based on an average normal hearth signal hence ignorant of extreme variations among different people’s normal heart signals. As such, many false alarms are generated if the global thresholds are selected too strict. On the other hand, loosely selected thresholds may result in missing many true alarms. Furthermore, the kits output report typically includes a limited number of basic parameters such as heart rate and hence negligent to a rich set of information exploitable from signal morphology. In this paper, we developed a prototype for patientspecific heart monitoring kit, which learns the properties of a patient’s normal ECG signal over time and reports significant deviations from this normal behavior. In order to reduce the false ...

IEEE Access

Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic objects ... more Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic objects from their 2D holograms. One of the technical challenges in the reconstruction stage is eliminating the twin image originating from the phase-conjugate wavefront. The twin image removal is typically formulated as a non-linear inverse problem since the scattering process involved in generating the hologram is irreversible. Conventional phase recovery methods rely on multiple holographic imaging at different distances from the object plane along with iterative algorithms. Recently, end-to-end deep learning (DL) methods are utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from the single-shot in-line digital hologram. However, massive data pairs are required to train the utilize DL model for an acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography do not exist. The trained models are also highly influenced by the objects' morphological properties, hence can vary from one application to another. Therefore, data collection can be prohibitively laborious and time-consuming, as a critical drawback of using DL methods for DH. In this article, we propose a novel DL method that takes advantages of the main characteristic of auto-encoders for blind single-shot hologram reconstruction solely based on the captured sample and without the need for a large dataset of samples with available ground truth to train the model. The simulation results demonstrate the superior performance of the proposed method compared to the stateof-the-art methods used for single-shot hologram reconstruction. INDEX TERMS Digital holography, phase reconstruction, twin image removal, deep learning, digital microscopy.

False alarm is one of the main concerns in intensive care units and can result in care disruption... more False alarm is one of the main concerns in intensive care units and can result in care disruption, sleep deprivation, and insensitivity of care-givers to alarms. Several methods have been proposed to suppress the false alarm rate through improving the quality of physiological signals by filtering, and developing more accurate sensors. However, significant intrinsic correlation among the extracted features limits the performance of most currently available data mining techniques, as they often discard the predictors with low individual impact that may potentially have strong discriminatory power when grouped with others. We propose a model based on coalition game theory that considers the inter-features dependencies in determining the salient predictors in respect to false alarm, which results in improved classification accuracy. The superior performance of this method compared to current methods is shown in simulation results using PhysionNet's MIMIC II database.

Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for micros... more Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for microscopic objects. One of the technical challenges that arise in the signal processing stage is removing the twin image that is caused by the phase-conjugate wavefront from the recorded holograms. Twin image removal is typically formulated as a non-linear inverse problem due to the irreversible scattering process when generating the hologram. Recently, end-to-end deep learning-based methods have been utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from a single-shot in-line digital hologram. However, massive data pairs are required to train deep learning models for acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography does not exist. Also, the trained model highly influenced by the morphological properties of the object and hence can vary for differe...

Current automated heart monitoring tools use supervised learning methods to recognize heart disor... more Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symmetry in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations1...

Battery-free wireless sensors developed at the University of Maine under a cooperative agreement ... more Battery-free wireless sensors developed at the University of Maine under a cooperative agreement with NASA enable a myriad of structural health monitoring (SHM) applications. Embedding these sensors in structures without the need for changing batteries, their rugged design to withstand harsh environments, and coded communication with multiple access features makes this new technology a desirable candidate for a variety of aerospace and civil infrastructure monitoring applications. This paper presents sensor characteristics, communication schemes, and multi tier networking strategies developed to deliver a reliable wireless sensor system for SHM. A large scale inflatable lunar habitat structure built by NASA and instrumented by UMaine is used as test-bed for technology demonstration. Various aspects of this system have been studied and results are published in conferences and journals as presented in the references section. These aspects are summarized in this paper and include: dist...

2018 Annual American Control Conference (ACC)

2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)

In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanne... more In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operational frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized to two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence 1 .

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach a... more In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service faster than the current approaches of using satellite images, manned aircraft and remotely controlled drones. Furthermore, using autonomous drones facilitates minimizing human intervention in risky wildfire zones. In particular, to develop a fully autonomous system, we propose a distributed leader-follower coalition formation model to cluster a set of drones into multiple coalitions that collectively cover the designated monitoring field. The coalition leader is a drone that employs observer drones potentially with different sensing and imaging capabilities to hover in circular paths and collect imagery information from the impacted areas. The objectives of the proposed system include: i) to cover the entire fire zone with a minimum number of drones, and ii) to minimize the energy consumption and latency of the available drones to fly to the fire zone. Simulation results confirm that the performance of the proposed system-without the need for inter-coalition communications-approaches that of a centrally-optimized system. 1 .

Lecture Notes in Computer Science

Convolutional layers in Artificial Neural Networks (ANN) treat the channel features equally witho... more Convolutional layers in Artificial Neural Networks (ANN) treat the channel features equally without feature selection flexibility. While using ANNs for image denoising in real-world applications with unknown noise distributions, particularly structured noise with learnable patterns, modeling informative features can substantially boost the performance. Channel attention methods in real image denoising tasks exploit dependencies between the feature channels, hence being a frequency component filtering mechanism. Existing channel attention modules typically use global statics as descriptors to learn the inter-channel correlations. This method deems inefficient at learning representative coefficients for re-scaling the channels in frequency level. This paper proposes a novel Sub-band Pyramid Attention (SPA) based on wavelet subband pyramid to recalibrate the frequency components of the extracted features in a more fine-grained fashion. We equip the SPA blocks on a network designed for real image denoising. Experimental results show that the proposed method achieves a remarkable improvement than the benchmark naive channel attention block. Furthermore, our results show how the pyramid level affects the performance of the SPA blocks and exhibits favorable generalization capability for the SPA blocks.

IEEE Transactions on Network Science and Engineering

2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)

2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

The emerging Flying Ad-Hoc Networks (FANETs) provide efficient infrastructure solutions for a wid... more The emerging Flying Ad-Hoc Networks (FANETs) provide efficient infrastructure solutions for a wide range of military and commercial applications. These networks are typically composed of high-speed Unmanned Aerial Vehicles (UAVs) and form dynamic network topologies. As such, conventional routing algorithms do not efficiently accommodate these continued topology changes and exhibit poor delay performances. In this paper, we introduce an optimal routing algorithm for UAV networks with queued communication systems based on Dijkstra's shortest path algorithm as a primary step towards developing a fully predictive communication platform. The core idea is to incorporate the anticipated locations of intermediate nodes during a transmission session into the path selection criterion. We further evaluate the impact of measurement uncertainties on choosing the optimal path and on the resulting end-to-end delay. The simulation results confirm the superior delay performance of the proposed algorithm compared to the conventional routing algorithms. The enhancement is more significant, when the network size is larger, the relative node velocities are higher, and the average waiting times in transmit buffers are longer.

IEEE Access

Recently, physically unclonable functions (PUFs) have received considerable attention from the re... more Recently, physically unclonable functions (PUFs) have received considerable attention from the research community due to their potential use in security mechanisms for applications such as the Internet of things (IoT). The concept generally employs the fabrication variability and naturally embedded randomness of device characteristics for secure identification. This approach complements and improves upon the conventional cryptographic security algorithms by covering their vulnerability against counterfeiting, cloning attacks, and physical hijacking. In this work, we propose a new identification/authentication mechanism based on a specific implementation of optical PUFs based on electrochemically formed dendritic patterns. Dendritic tags are built by growing unique, complex, and unclonable nano-scaled metallic patterns on highly nonreactive substrates using electrolyte solutions. Dendritic patterns with 3D surfaces are technically impossible to reproduce, hence they can be used as the fingerprints of objects. Current optical PUF-based identification mechanisms rely on image processing methods that require high-complexity computations and massive storage and communication capacity to store and exchange high-resolution image databases in largescale networks. To address these issues, we propose a lightweight identification algorithm that converts the images of dendritic patterns into representative graphs and uses a graph-matching approach for device identification. More specifically, we develop a probabilistic graph matching algorithm that makes linkages between the similar feature points in the test and reference graphs while considering the consistency of their local subgraphs. The proposed method demonstrates a high level of accuracy in the presence of imaging artifacts, noise, and skew compared to existing image-based algorithms. The computational complexity of the algorithm grows linearly with the number of extracted feature points and is therefore suitable for largescale networks.

EURASIP Journal on Wireless Communications and Networking, Apr 8, 2020

Passive time difference location is an important method for passive location. There are fuzzy pos... more Passive time difference location is an important method for passive location. There are fuzzy positioning, no solution, and low positioning accuracy with the spherical coordinate conversion method in the four-station TDOA positioning algorithm. Focusing on these problems, we proposed a combination of TDOA and iterative Newton's method. The positioning method uses the result obtained by the four-station TDOA location algorithm as the initial value of the iterative Newton's method and solves the problem of no solution and fuzzy positioning caused by the four-station TDOA location algorithm by using the spherical coordinates conversion method. By simulating the target at a height of 5 km and traveling at a constant speed for 40 km, the positioning accuracy of the root mean square error is less than 45 m, which can achieve the same positioning accuracy of TDOA based on the least square algorithm. As the baseline length increases, the positioning accuracy is better than the least square algorithm.

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Apr 1, 2019

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach a... more In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service faster than the current approaches of using satellite images, manned aircraft and remotely controlled drones. Furthermore, using autonomous drones facilitates minimizing human intervention in risky wildfire zones. In particular, to develop a fully autonomous system, we propose a distributed leader-follower coalition formation model to cluster a set of drones into multiple coalitions that collectively cover the designated monitoring field. The coalition leader is a drone that employs observer drones potentially with different sensing and imaging capabilities to hover in circular paths and collect imagery information from the impacted areas. The objectives of the proposed system include: i) to cover the entire fire zone with a minimum number of drones, and ii) to minimize the energy consumption and latency of the available drones to fly to the fire zone. Simulation results confirm that the performance of the proposed system-without the need for inter-coalition communications-approaches that of a centrally-optimized system. 1 .

arXiv: Signal Processing, 2018

An important paradigm in smart health is developing diagnosis tools and monitoring a patient'... more An important paradigm in smart health is developing diagnosis tools and monitoring a patient's heart activity through processing Electrocardiogram (ECG) signals is a key example, sue to high mortality rate of heart-related disease. However, current heart monitoring devices suffer from two important drawbacks: i) failure in capturing inter-patient variability, and ii) incapability of identifying heart abnormalities ahead of time to take effective preventive and therapeutic interventions. This paper proposed a novel predictive signal processing method to solve these issues. We propose a two-step classification framework for ECG signals, where a global classifier recognizes severe abnormalities by comparing the signal against a universal reference model. The seemingly normal signals are then passed through a personalized classifier, to recognize mild but informative signal morphology distortions. The key idea is to develop a novel deviation analysis based on a controlled nonlinear ...

Electrocardiogram (ECG) signals are widely used to check heart rhythms and general health conditi... more Electrocardiogram (ECG) signals are widely used to check heart rhythms and general health conditions using low-cost and relatively accurate equipment. However, the majority of commercial off-the-shelf ECG kits are generic and their normal ranges are set based on an average normal hearth signal hence ignorant of extreme variations among different people’s normal heart signals. As such, many false alarms are generated if the global thresholds are selected too strict. On the other hand, loosely selected thresholds may result in missing many true alarms. Furthermore, the kits output report typically includes a limited number of basic parameters such as heart rate and hence negligent to a rich set of information exploitable from signal morphology. In this paper, we developed a prototype for patientspecific heart monitoring kit, which learns the properties of a patient’s normal ECG signal over time and reports significant deviations from this normal behavior. In order to reduce the false ...

IEEE Access

Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic objects ... more Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic objects from their 2D holograms. One of the technical challenges in the reconstruction stage is eliminating the twin image originating from the phase-conjugate wavefront. The twin image removal is typically formulated as a non-linear inverse problem since the scattering process involved in generating the hologram is irreversible. Conventional phase recovery methods rely on multiple holographic imaging at different distances from the object plane along with iterative algorithms. Recently, end-to-end deep learning (DL) methods are utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from the single-shot in-line digital hologram. However, massive data pairs are required to train the utilize DL model for an acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography do not exist. The trained models are also highly influenced by the objects' morphological properties, hence can vary from one application to another. Therefore, data collection can be prohibitively laborious and time-consuming, as a critical drawback of using DL methods for DH. In this article, we propose a novel DL method that takes advantages of the main characteristic of auto-encoders for blind single-shot hologram reconstruction solely based on the captured sample and without the need for a large dataset of samples with available ground truth to train the model. The simulation results demonstrate the superior performance of the proposed method compared to the stateof-the-art methods used for single-shot hologram reconstruction. INDEX TERMS Digital holography, phase reconstruction, twin image removal, deep learning, digital microscopy.

False alarm is one of the main concerns in intensive care units and can result in care disruption... more False alarm is one of the main concerns in intensive care units and can result in care disruption, sleep deprivation, and insensitivity of care-givers to alarms. Several methods have been proposed to suppress the false alarm rate through improving the quality of physiological signals by filtering, and developing more accurate sensors. However, significant intrinsic correlation among the extracted features limits the performance of most currently available data mining techniques, as they often discard the predictors with low individual impact that may potentially have strong discriminatory power when grouped with others. We propose a model based on coalition game theory that considers the inter-features dependencies in determining the salient predictors in respect to false alarm, which results in improved classification accuracy. The superior performance of this method compared to current methods is shown in simulation results using PhysionNet's MIMIC II database.

Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for micros... more Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for microscopic objects. One of the technical challenges that arise in the signal processing stage is removing the twin image that is caused by the phase-conjugate wavefront from the recorded holograms. Twin image removal is typically formulated as a non-linear inverse problem due to the irreversible scattering process when generating the hologram. Recently, end-to-end deep learning-based methods have been utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from a single-shot in-line digital hologram. However, massive data pairs are required to train deep learning models for acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography does not exist. Also, the trained model highly influenced by the morphological properties of the object and hence can vary for differe...

Current automated heart monitoring tools use supervised learning methods to recognize heart disor... more Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symmetry in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations1...

Battery-free wireless sensors developed at the University of Maine under a cooperative agreement ... more Battery-free wireless sensors developed at the University of Maine under a cooperative agreement with NASA enable a myriad of structural health monitoring (SHM) applications. Embedding these sensors in structures without the need for changing batteries, their rugged design to withstand harsh environments, and coded communication with multiple access features makes this new technology a desirable candidate for a variety of aerospace and civil infrastructure monitoring applications. This paper presents sensor characteristics, communication schemes, and multi tier networking strategies developed to deliver a reliable wireless sensor system for SHM. A large scale inflatable lunar habitat structure built by NASA and instrumented by UMaine is used as test-bed for technology demonstration. Various aspects of this system have been studied and results are published in conferences and journals as presented in the references section. These aspects are summarized in this paper and include: dist...

2018 Annual American Control Conference (ACC)

2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)

In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanne... more In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operational frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized to two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence 1 .

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach a... more In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service faster than the current approaches of using satellite images, manned aircraft and remotely controlled drones. Furthermore, using autonomous drones facilitates minimizing human intervention in risky wildfire zones. In particular, to develop a fully autonomous system, we propose a distributed leader-follower coalition formation model to cluster a set of drones into multiple coalitions that collectively cover the designated monitoring field. The coalition leader is a drone that employs observer drones potentially with different sensing and imaging capabilities to hover in circular paths and collect imagery information from the impacted areas. The objectives of the proposed system include: i) to cover the entire fire zone with a minimum number of drones, and ii) to minimize the energy consumption and latency of the available drones to fly to the fire zone. Simulation results confirm that the performance of the proposed system-without the need for inter-coalition communications-approaches that of a centrally-optimized system. 1 .

Lecture Notes in Computer Science

Convolutional layers in Artificial Neural Networks (ANN) treat the channel features equally witho... more Convolutional layers in Artificial Neural Networks (ANN) treat the channel features equally without feature selection flexibility. While using ANNs for image denoising in real-world applications with unknown noise distributions, particularly structured noise with learnable patterns, modeling informative features can substantially boost the performance. Channel attention methods in real image denoising tasks exploit dependencies between the feature channels, hence being a frequency component filtering mechanism. Existing channel attention modules typically use global statics as descriptors to learn the inter-channel correlations. This method deems inefficient at learning representative coefficients for re-scaling the channels in frequency level. This paper proposes a novel Sub-band Pyramid Attention (SPA) based on wavelet subband pyramid to recalibrate the frequency components of the extracted features in a more fine-grained fashion. We equip the SPA blocks on a network designed for real image denoising. Experimental results show that the proposed method achieves a remarkable improvement than the benchmark naive channel attention block. Furthermore, our results show how the pyramid level affects the performance of the SPA blocks and exhibits favorable generalization capability for the SPA blocks.

IEEE Transactions on Network Science and Engineering

2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)

2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

The emerging Flying Ad-Hoc Networks (FANETs) provide efficient infrastructure solutions for a wid... more The emerging Flying Ad-Hoc Networks (FANETs) provide efficient infrastructure solutions for a wide range of military and commercial applications. These networks are typically composed of high-speed Unmanned Aerial Vehicles (UAVs) and form dynamic network topologies. As such, conventional routing algorithms do not efficiently accommodate these continued topology changes and exhibit poor delay performances. In this paper, we introduce an optimal routing algorithm for UAV networks with queued communication systems based on Dijkstra's shortest path algorithm as a primary step towards developing a fully predictive communication platform. The core idea is to incorporate the anticipated locations of intermediate nodes during a transmission session into the path selection criterion. We further evaluate the impact of measurement uncertainties on choosing the optimal path and on the resulting end-to-end delay. The simulation results confirm the superior delay performance of the proposed algorithm compared to the conventional routing algorithms. The enhancement is more significant, when the network size is larger, the relative node velocities are higher, and the average waiting times in transmit buffers are longer.

IEEE Access

Recently, physically unclonable functions (PUFs) have received considerable attention from the re... more Recently, physically unclonable functions (PUFs) have received considerable attention from the research community due to their potential use in security mechanisms for applications such as the Internet of things (IoT). The concept generally employs the fabrication variability and naturally embedded randomness of device characteristics for secure identification. This approach complements and improves upon the conventional cryptographic security algorithms by covering their vulnerability against counterfeiting, cloning attacks, and physical hijacking. In this work, we propose a new identification/authentication mechanism based on a specific implementation of optical PUFs based on electrochemically formed dendritic patterns. Dendritic tags are built by growing unique, complex, and unclonable nano-scaled metallic patterns on highly nonreactive substrates using electrolyte solutions. Dendritic patterns with 3D surfaces are technically impossible to reproduce, hence they can be used as the fingerprints of objects. Current optical PUF-based identification mechanisms rely on image processing methods that require high-complexity computations and massive storage and communication capacity to store and exchange high-resolution image databases in largescale networks. To address these issues, we propose a lightweight identification algorithm that converts the images of dendritic patterns into representative graphs and uses a graph-matching approach for device identification. More specifically, we develop a probabilistic graph matching algorithm that makes linkages between the similar feature points in the test and reference graphs while considering the consistency of their local subgraphs. The proposed method demonstrates a high level of accuracy in the presence of imaging artifacts, noise, and skew compared to existing image-based algorithms. The computational complexity of the algorithm grows linearly with the number of extracted feature points and is therefore suitable for largescale networks.

EURASIP Journal on Wireless Communications and Networking, Apr 8, 2020

Passive time difference location is an important method for passive location. There are fuzzy pos... more Passive time difference location is an important method for passive location. There are fuzzy positioning, no solution, and low positioning accuracy with the spherical coordinate conversion method in the four-station TDOA positioning algorithm. Focusing on these problems, we proposed a combination of TDOA and iterative Newton's method. The positioning method uses the result obtained by the four-station TDOA location algorithm as the initial value of the iterative Newton's method and solves the problem of no solution and fuzzy positioning caused by the four-station TDOA location algorithm by using the spherical coordinates conversion method. By simulating the target at a height of 5 km and traveling at a constant speed for 40 km, the positioning accuracy of the root mean square error is less than 45 m, which can achieve the same positioning accuracy of TDOA based on the least square algorithm. As the baseline length increases, the positioning accuracy is better than the least square algorithm.

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Apr 1, 2019

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach a... more In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service faster than the current approaches of using satellite images, manned aircraft and remotely controlled drones. Furthermore, using autonomous drones facilitates minimizing human intervention in risky wildfire zones. In particular, to develop a fully autonomous system, we propose a distributed leader-follower coalition formation model to cluster a set of drones into multiple coalitions that collectively cover the designated monitoring field. The coalition leader is a drone that employs observer drones potentially with different sensing and imaging capabilities to hover in circular paths and collect imagery information from the impacted areas. The objectives of the proposed system include: i) to cover the entire fire zone with a minimum number of drones, and ii) to minimize the energy consumption and latency of the available drones to fly to the fire zone. Simulation results confirm that the performance of the proposed system-without the need for inter-coalition communications-approaches that of a centrally-optimized system. 1 .