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Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.
Azharul Islam; Kyunghi Chang. Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources. Applied Sciences 2021, 11, 6237 .
AMA StyleAzharul Islam, Kyunghi Chang. Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources. Applied Sciences. 2021; 11 (13):6237.
Chicago/Turabian StyleAzharul Islam; Kyunghi Chang. 2021. "Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources." Applied Sciences 11, no. 13: 6237.
Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.
Nafis Ahmed; Chaitali Pawase; Kyunghi Chang. Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization. Applied Sciences 2021, 11, 3417 .
AMA StyleNafis Ahmed, Chaitali Pawase, Kyunghi Chang. Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization. Applied Sciences. 2021; 11 (8):3417.
Chicago/Turabian StyleNafis Ahmed; Chaitali Pawase; Kyunghi Chang. 2021. "Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization." Applied Sciences 11, no. 8: 3417.
The goal of automatic parking system is to accomplish the vehicle parking to the specified space automatically. It mainly includes parking space recognition, parking space matching, and trajectory generation. It has been developed enormously, but it is still a challenging work due to parking space recognition error and trajectory generation for vehicle nonparallel initial state with parking space. In this study, the authors propose multi-sensor information ensemble for parking space recognition and adaptive trajectory generation method, which is also robust to vehicle nonparallel initial state. Both simulation and real vehicle experiments are conducted to prove that the proposed method can improve the automatic parking system performance.
Changhao Piao; Jun Zhang; Kyunghi Chang; Yan Li; Mingjie Liu. Multi-Sensor Information Ensemble-Based Automatic Parking System for Vehicle Parallel/Nonparallel Initial State. Sensors 2021, 21, 2261 .
AMA StyleChanghao Piao, Jun Zhang, Kyunghi Chang, Yan Li, Mingjie Liu. Multi-Sensor Information Ensemble-Based Automatic Parking System for Vehicle Parallel/Nonparallel Initial State. Sensors. 2021; 21 (7):2261.
Chicago/Turabian StyleChanghao Piao; Jun Zhang; Kyunghi Chang; Yan Li; Mingjie Liu. 2021. "Multi-Sensor Information Ensemble-Based Automatic Parking System for Vehicle Parallel/Nonparallel Initial State." Sensors 21, no. 7: 2261.
Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we proposed a fast and robust lane detection method by combining a semantic segmentation network and an optical flow estimation network. Specifically, the whole research was divided into three parts: lane segmentation, lane discrimination, and mapping. In terms of lane segmentation, a robust semantic segmentation network was proposed to segment key frames and a fast and slim optical flow estimation network was used to track non-key frames. In the second part, density-based spatial clustering of applications with noise (DBSCAN) was adopted to discriminate lanes. Ultimately, we proposed a mapping method to map lane pixels from pixel coordinate system to camera coordinate system and fit lane curves in the camera coordinate system that are able to provide feedback for autonomous driving. Experimental results verified that the proposed method can speed up robust semantic segmentation network by three times at most and the accuracy fell 2% at most. In the best of circumstances, the result of the lane curve verified that the feedback error was 3%.
Sheng Lu; Zhaojie Luo; Feng Gao; Mingjie Liu; Kyunghi Chang; Changhao Piao. A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation. Sensors 2021, 21, 400 .
AMA StyleSheng Lu, Zhaojie Luo, Feng Gao, Mingjie Liu, Kyunghi Chang, Changhao Piao. A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation. Sensors. 2021; 21 (2):400.
Chicago/Turabian StyleSheng Lu; Zhaojie Luo; Feng Gao; Mingjie Liu; Kyunghi Chang; Changhao Piao. 2021. "A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation." Sensors 21, no. 2: 400.
This research classifies the modulation and coding rate for link adaptation in Underwater Acoustic Communications Networks (UACNs). Recently, the UACN has become a promising technology for military, commercial, and civilian applications, as well as scientific research. However, we should minimize the dataset dimension for real-time implementation due to the sensor nodes’ energy limitations in the underwater environment. We used an Incheon sea trial’s measured dataset of 18 features, applying Principal Component Analysis (PCA) to select the dominant eigenvalue components in order to reduce the curse of dimensionality, and then selected 11 parameters. After that, we applied Machine Learning (ML) algorithms with different combinations of the parameters to separately classify the modulation and the coding rate and measured both individual and overall classification accuracy. The findings are compared with two Taean sea trial datasets with 11 features to finalize the selected parameters for link adaptation. For modulation classification, we observed 96.83% accuracy with the K-nearest Neighbors (KNN) algorithm in three-parameter and two-parameter cases. In coding rate classification, we found 100% accuracy with the KNN algorithm using the same three-parameter case. However, for the best fit among the three datasets, we finalized another three parameters at the expense of accuracy. To find the optimum threshold values for all modulation and coding rate labels, we used Rule-based (RB) 2D and 3D analysis. However, with a hard limit on non-overlapping data, at best, 35.51% classification accuracy was found for a 1/3 coding rate (Turbo code) with QPSK modulation, which showed much less reliability for RB analysis in a UACN, so it is not useful in this regard. Besides, our analysis shows data independence in the Doppler Spread (DS) and the Frequency Shift (FS) , mitigating the time-variability channel’s challenge. We use the Gaussian distribution plot, a confusion matrix, multi-dimensional scatter plots, interpolated plots to analyze the data.
Mst. Najnin Sultana; Kyunghi Chang. ML Algorithm Performance to Classify MCS Schemes During UACN Link Adaptation. IEEE Access 2020, 8, 226461 -226483.
AMA StyleMst. Najnin Sultana, Kyunghi Chang. ML Algorithm Performance to Classify MCS Schemes During UACN Link Adaptation. IEEE Access. 2020; 8 ():226461-226483.
Chicago/Turabian StyleMst. Najnin Sultana; Kyunghi Chang. 2020. "ML Algorithm Performance to Classify MCS Schemes During UACN Link Adaptation." IEEE Access 8, no. : 226461-226483.
The Republic of Korea has played a leading role in the development of next-generation long-term evolution (LTE) public safety networks. The LTE-based public safety (PS-LTE) network, the LTE-based high-speed railway (LTE-R) network, and the LTE-based maritime (LTE-M) network use the same 700 MHz frequency band. That results in severe co-channel interference (CCI), so there is a dire need for practical research into resolving the CCI issue. Moreover, unplanned deployment of the mobile personal cell (mPC) generates serious user-association issues owing to its movement, which leads to severe co-channel interference in coexisting PS-LTE and LTE-R networks. Indeed, it is important to satisfy users’ quality of service (QoS) requirements during resource allocation in specific public safety situations. Therefore, we address the CCI issues through wise deployment of the mPC for user association and load balancing in overlapping PS-LTE and LTE-R networks. In this paper, we propose a QoS mPC deployment (QoS_mPCD) scheme for priority-based load balancing and interference reduction in coexisting PS-LTE and LTE-R networks. The proposed scheme efficiently manages the user-association and load-balancing problems, and allocates the best resources to high-priority users based on defined service priority levels. Moreover, we employ an enhanced inter-cell interference coordination (eICIC) scheme that further reduces the interference with the users offloaded onto an mPC. System-level simulations are performed to evaluate the proposed QoS_mPCD scheme by considering important performance matrices such as user equipment (UE) throughput, UE received interference, and UE outage probabilities.
Ishtiaq Ahmad; Jinyoung Jang; Kyunghi Chang. QoS Priority-Based Mobile Personal Cell Deployment with Load Balancing for Interference Reduction between Users on Coexisting Public Safety and Railway LTE Networks. Electronics 2020, 9, 2136 .
AMA StyleIshtiaq Ahmad, Jinyoung Jang, Kyunghi Chang. QoS Priority-Based Mobile Personal Cell Deployment with Load Balancing for Interference Reduction between Users on Coexisting Public Safety and Railway LTE Networks. Electronics. 2020; 9 (12):2136.
Chicago/Turabian StyleIshtiaq Ahmad; Jinyoung Jang; Kyunghi Chang. 2020. "QoS Priority-Based Mobile Personal Cell Deployment with Load Balancing for Interference Reduction between Users on Coexisting Public Safety and Railway LTE Networks." Electronics 9, no. 12: 2136.
Cellular-based vehicle-to-everything (C-V2X) is one of the emerging and promising techniques to support vehicular communications by enabling both safety-critical and non-safety services. C-V2X communications is a core solution to manage and advance future traffic safety and mobility. In this paper, we design a cellular-based vehicle-to-infrastructure (V2I) communications model for a Manhattan dense urban environment that has many roads deployed in the cell edge region, which results in severe co-channel interference (CCI). Thus, we aim to utilize two cooperative interference management schemes such as dynamic inter-cell interference coordination (ICIC) and coordinated multipoint (CoMP) to mitigate interference and to improve communication reliability. We evaluate the performance of the interference management schemes based on various performance indexes, such as vehicle UE average throughput, vehicle UE received interference, and vehicle UE outage probability. By effectively implementing the dynamic ICIC scheme, we achieve an immense reduction in CCI, which results in the improvement of user (UE) received signal quality. Moreover, we employ coordinated scheduling (CS) CoMP scheme to further mitigate the interference among cells. Finally, by implementing both dynamic ICIC and CS CoMP schemes simultaneously, a meaningful level of performance enhancement is achieved.
Umair Ahmad Mughal; Jiao Xiao; Ishtiaq Ahmad; Kyunghi Chang. Cooperative resource management for C-V2I communications in a dense urban environment. Vehicular Communications 2020, 26, 100282 .
AMA StyleUmair Ahmad Mughal, Jiao Xiao, Ishtiaq Ahmad, Kyunghi Chang. Cooperative resource management for C-V2I communications in a dense urban environment. Vehicular Communications. 2020; 26 ():100282.
Chicago/Turabian StyleUmair Ahmad Mughal; Jiao Xiao; Ishtiaq Ahmad; Kyunghi Chang. 2020. "Cooperative resource management for C-V2I communications in a dense urban environment." Vehicular Communications 26, no. : 100282.
Interest in the study of next-generation underwater sensor networks for ocean investigations has increased owing to developing concerns over their utilization in areas such as oceanography, commercial operations in maritime areas, and military surveillance. Underwater acoustic communications (UAC) network channels are fast-varying (spatially and temporally) according to environmental conditions. It is tempting to use adaptive modulation and coding (AMC) for UAC networks to improve the system efficiency by matching transmission parameters to channel variations. This paper focuses on analyzing a measured sea trial dataset by using a rule-based strategy (i.e., three-dimensional analysis, modulation-wise analysis, and a fixed SNR strategy) to find the suitable link adaptation procedure depending on the channel quality. Hence, we plot a scenario of the measured UAC network data rate vs Signal to Noise Ratio (SNR) and/or Bit Error Rate (BER) to pick the best AMC combinations in the context of adaptivity/ adaptability to the channel. Due to non-reversibility limitation of rule-based strategy, the work further extends to use machine learning (ML) algorithms to classify the AMC by investigating the channel characteristics. Boosted regression tree, from among the four ML algorithms we adopted for the analysis, shows formidable accuracy of 99.97% in classifying AMC. This ensemble of trees learns from the uplink data of the buoy and the base station and relates the AMC to channel metrics and signal characteristics especially subject to SNR and BER constraints.
M.S.M. Alamgir; Mst. Najnin Sultana; Kyunghi Chang; Alamgir M S M. Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach. IEEE Access 2020, 8, 73957 -73971.
AMA StyleM.S.M. Alamgir, Mst. Najnin Sultana, Kyunghi Chang, Alamgir M S M. Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach. IEEE Access. 2020; 8 (99):73957-73971.
Chicago/Turabian StyleM.S.M. Alamgir; Mst. Najnin Sultana; Kyunghi Chang; Alamgir M S M. 2020. "Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach." IEEE Access 8, no. 99: 73957-73971.
This paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.
Junfeng Guo; Ishtiaq Ahmad; Kyunghi Chang. Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming. EURASIP Journal on Wireless Communications and Networking 2020, 2020, 1 -19.
AMA StyleJunfeng Guo, Ishtiaq Ahmad, Kyunghi Chang. Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming. EURASIP Journal on Wireless Communications and Networking. 2020; 2020 (1):1-19.
Chicago/Turabian StyleJunfeng Guo; Ishtiaq Ahmad; Kyunghi Chang. 2020. "Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming." EURASIP Journal on Wireless Communications and Networking 2020, no. 1: 1-19.
The use of the unmanned aerial vehicle (UAV) has been regarded as a promising technique in both military and civilian applications. However, due to the lack of relevant laws and regulations, the misuse of illegal drones poses a serious threat to social security. In this paper, we develop a trajectory planner based on particle swarm optimization and a proposed surveillance area importance updating mechanism aimed at deriving three-dimensional (3D) optimal surveillance trajectories for multiple monitoring drones. We also propose a multi-objective fitness function in accordance with energy consumption, flight risk, and surveillance area priority in order to evaluate the trajectories generated by the proposed trajectory planner. Simulation results show that the trajectories generated by the proposed trajectory planner can preferentially visit important areas while obtaining a high fitness value in various practical situations.
Hu Teng; Ishtiaq Ahmad; Alamgir Msm; Kyunghi Chang. 3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority. IEEE Access 2020, 8, 86316 -86327.
AMA StyleHu Teng, Ishtiaq Ahmad, Alamgir Msm, Kyunghi Chang. 3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority. IEEE Access. 2020; 8 (99):86316-86327.
Chicago/Turabian StyleHu Teng; Ishtiaq Ahmad; Alamgir Msm; Kyunghi Chang. 2020. "3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority." IEEE Access 8, no. 99: 86316-86327.
The Republic of Korea became the first country in the world to have next-generation long term evolution (LTE) public safety networks, and South Korea’s evolution in critical communications has taken the lead as well. However, the same 700 MHz frequency band is allocated to the LTE-based public safety (PS-LTE) network, the LTE-based high-speed railway (LTE-R) network, and the LTE-maritime (LTE-M) network, and hence, extensive interest and practical research into co-channel interference management schemes are immediately required. This paper focuses on multi-layer next-generation public safety networks and employs efficient mission-critical user priority-based resource allocation schemes to resolve major challenges, such as co-channel interference, mission-critical user requirements, quality of service (QoS) prioritization, etc. We utilize coordinated scheduling (CS), coordinated multipoint (CoMP), and intercell interference cancellation (ICIC) under the radio access network (RAN)-sharing environment to analyze the overlapping next-generation PS-LTE, LTE-R, and LTE-M networks. CS CoMP is implemented between the three LTE public safety networks to boost cell-edge-user performance by muting the neighboring interfering base stations. This accomplishes a tremendous enhancement in system throughput for coexisting next-generation public safety networks. By employing ICIC with CS CoMP under the RAN-sharing environment, the best interference and outage performance can be attained for coexisting PS-LTE, LTE-R, and LTE-M networks.
Ishtiaq Ahmad; Kyunghi Chang. Mission-critical user priority-based cooperative resource allocation schemes for multi-layer next-generation public safety networks. Physical Communication 2019, 38, 100926 .
AMA StyleIshtiaq Ahmad, Kyunghi Chang. Mission-critical user priority-based cooperative resource allocation schemes for multi-layer next-generation public safety networks. Physical Communication. 2019; 38 ():100926.
Chicago/Turabian StyleIshtiaq Ahmad; Kyunghi Chang. 2019. "Mission-critical user priority-based cooperative resource allocation schemes for multi-layer next-generation public safety networks." Physical Communication 38, no. : 100926.
The increasing interest in next-generation underwater acoustic communications networks is due to vast investigation of oceans for oceanography, commercial operations in maritime areas, military surveillance, and more. A surface buoy or underwater base station controller (UBSC) communicates with either transceivers or underwater base stations (UBSs) via acoustic links. Transceivers further communicate with underwater sensor nodes using acoustic links. In this paper, we employ a downlink (DL) power allocation (PA) strategy using an orthogonal frequency-division multiple access (OFDMA) technique for underwater acoustic communications (UAC) networks. First, we present an approach to power offsets using three kinds of pilot spacing and apply the power boosting (PB) concept on orthogonal frequency-division multiplexing (OFDM) symbols for the UAC network. Secondly, we draw the block error rate (BLER) curves from link-level simulation (LLS) and analyze the signal-to-noise ratio (SNR) for both PA and non-PA strategies. Lastly, we adopt the best PB for system-level simulation (SLS) and compare the throughput and outage performance for PA and non-PA strategies. Hence, the simulation results confirm the effectiveness of the DL PA strategy for UAC networks.
Ishtiaq Ahmad; Kyunghi Chang. Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks. Electronics 2019, 8, 1297 .
AMA StyleIshtiaq Ahmad, Kyunghi Chang. Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks. Electronics. 2019; 8 (11):1297.
Chicago/Turabian StyleIshtiaq Ahmad; Kyunghi Chang. 2019. "Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks." Electronics 8, no. 11: 1297.
Yujing He; Wan Chen; Ishtiaq Ahmad; Lin Shi; Kyung Hi Chang. Compressive sensing based random access for machine type communications considering tradeoff between link performance and latency. EURASIP Journal on Wireless Communications and Networking 2019, 2019, 1 .
AMA StyleYujing He, Wan Chen, Ishtiaq Ahmad, Lin Shi, Kyung Hi Chang. Compressive sensing based random access for machine type communications considering tradeoff between link performance and latency. EURASIP Journal on Wireless Communications and Networking. 2019; 2019 (1):1.
Chicago/Turabian StyleYujing He; Wan Chen; Ishtiaq Ahmad; Lin Shi; Kyung Hi Chang. 2019. "Compressive sensing based random access for machine type communications considering tradeoff between link performance and latency." EURASIP Journal on Wireless Communications and Networking 2019, no. 1: 1.
Interest in the study of next-generation underwater sensor networks for ocean investigation has increased owing to developing concerns over its utilization in areas, such as oceanography, commercial operations in maritime areas, and military surveillance. The underwater base station controller, in the form of a surface buoy, communicates with underwater base stations (UBSs) while the UBSs transceiver information with underwater sensor nodes via acoustic communications. This paper provides a link-level and system-level study of downlinks using an orthogonal frequency-division multiple access techniques for underwater acoustic communications (UAC) networks. We present an approach to link-level-to-system-level (L2S) mapping at the link level that provides an abstraction model of the link-level performance to be accessed by system-level simulation (SLS). In this paper, an exponential effective signal-to-noise ratio (SNR) mapping (EESM) method is adopted, which elaborates on how multi-state channels are integrated into a single state in an SLS, and why an effective SNR can represent the characteristics of a multiple subcarrier SNR. Moreover, we explain the beta calibration procedure in detail for a UAC network. The simulation results are provided to verify the beta calibration of the UAC network. Furthermore, we employ a link adaptation strategy by evaluating system throughput based on a proportional fair (PF) scheduler at the system level. Hence, the simulation results confirm the effectiveness of link adaptation strategy for UAC networks.
Ishtiaq Ahmad; Kyunghi Chang. Effective SNR Mapping and Link Adaptation Strategy for Next-Generation Underwater Acoustic Communications Networks: A Cross-Layer Approach. IEEE Access 2019, 7, 44150 -44164.
AMA StyleIshtiaq Ahmad, Kyunghi Chang. Effective SNR Mapping and Link Adaptation Strategy for Next-Generation Underwater Acoustic Communications Networks: A Cross-Layer Approach. IEEE Access. 2019; 7 ():44150-44164.
Chicago/Turabian StyleIshtiaq Ahmad; Kyunghi Chang. 2019. "Effective SNR Mapping and Link Adaptation Strategy for Next-Generation Underwater Acoustic Communications Networks: A Cross-Layer Approach." IEEE Access 7, no. : 44150-44164.
The development of drones has captured the attention of hobbyists and investors alike; drones now have greater commercial and military applications owing to their relatively small size and ability to fly without an on-board pilot. However, certain drone applications may pose serious threats to public safety. The most important problem to be addressed is the recognition of drones in security-sensitive areas. This paper presents an approach to recognize drones through sounds emitted by their propellers using Mel frequency cepstral coefficients (MFCCs) technique for feature extraction and the hidden Markov model (HMM) approach for classification. In the feature extraction stage, two schemes for feature vectors (one using twenty-four MFCCs and the other using the proposed thirty-six MFCCs) are applied, where additional dynamic information of the features is added in the latter. The classifier based on HMMs is then trained using the extracted features according to different training datasets in order to validate the effect of the number of sound types in each cluster on the recognition rate performance. We perform experiments for drone sound recognition utilizing various training datasets for the purpose of classifier optimization, as well as for the two MFCC schemes that are applied in each trial, using the same training datasets for a fair comparison. The experimental results finally validate the feasibility and effectiveness of our proposed methods with relatively high recognition rates, even in noisy environments.
Lin Shi; Ishtiaq Ahmad; Yujing He; Kyunghi Chang. Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments. Journal of Communications and Networks 2018, 20, 509 -518.
AMA StyleLin Shi, Ishtiaq Ahmad, Yujing He, Kyunghi Chang. Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments. Journal of Communications and Networks. 2018; 20 (5):509-518.
Chicago/Turabian StyleLin Shi; Ishtiaq Ahmad; Yujing He; Kyunghi Chang. 2018. "Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments." Journal of Communications and Networks 20, no. 5: 509-518.
ITU-R M.1842-1, which is one of the renowned specification for VHF data exchange system (VDES) devoted to maritime mobile applications, has been standardized wireless transmission protocols according to the peculiar characteristics of a maritime communications scenario. Generally, in the Annex-4 of ITU-R M.1842-1, the VHF data link protocols for maritime mobile services are described along with a TDMA frame structure. Physical layer parameters of ship-adhoc network (SANET) are contrived to meet the requirements of the specification. In order to increase the link throughput of real-time services, in this paper, we investigate the performance of the SANET with the multiple antennas, where transmit diversity and multiplexing techniques are employed. Based on the analysis of the packet error rate and throughput under the maritime channel model in a coastline area, we select the efficient multiple antenna schemes for SANET to improve the link reliability.
Ishtiaq Ahmad; Kyunghi Chang. Analysis on MIMO transmit diversity and multiplexing techniques for ship ad-hoc networks under a maritime channel model in coastline areas. 2017 International Conference on Information and Communication Technology Convergence (ICTC) 2017, 945 -947.
AMA StyleIshtiaq Ahmad, Kyunghi Chang. Analysis on MIMO transmit diversity and multiplexing techniques for ship ad-hoc networks under a maritime channel model in coastline areas. 2017 International Conference on Information and Communication Technology Convergence (ICTC). 2017; ():945-947.
Chicago/Turabian StyleIshtiaq Ahmad; Kyunghi Chang. 2017. "Analysis on MIMO transmit diversity and multiplexing techniques for ship ad-hoc networks under a maritime channel model in coastline areas." 2017 International Conference on Information and Communication Technology Convergence (ICTC) , no. : 945-947.
A third-generation partnership project long-term evolution system uses the concept of a 2-tier heterogeneous network, where low-power and short-range femtocells are laid under macrocells to fulfill the quality of service (QoS) requirements of users and to boost overall network throughput. However, cochannel deployment gives rise to the problem of cross-tier interference that significantly deteriorates the performance of the cellular network. The coordinated multipoint (CoMP) transmission scheme is proposed as a promising solution to alleviate cross-tier interference by doing cooperation among the neighbor base stations. In this paper, we propose a QoS priority-based coordinated scheduling and hybrid spectrum access (QoS-CSaHSA) scheme for downlink CoMP transmission in 2-tier networks. The proposed QoS-CSaHSA scheme dynamically reduces the femtocells' power requirements by considering the neighbor cell interference, and it also balances the macrocell load by switching the femtocells to HA mode. Moreover, it can reduce unnecessary muting of the base stations because it only enables the coordinated scheduling CoMP when there is no other possibility to reduce the cross-tier interference. System-level simulation results prove the validity of the proposed QoS-CSaHSA scheme in a heterogeneous network environment. Compared with the existing schemes that operate the femtocells in a closed-subscriber group without considering users' QoS requirements, the proposed scheme almost doubles the cell-edge user throughput and reduces the packet loss rate and call-blocking probability.
Zeeshan Kaleem; Kyunghi Chang. QoS priority-based coordinated scheduling and hybrid spectrum access for femtocells in dense cooperative 5G cellular networks. Transactions on Emerging Telecommunications Technologies 2017, 29, e3207 .
AMA StyleZeeshan Kaleem, Kyunghi Chang. QoS priority-based coordinated scheduling and hybrid spectrum access for femtocells in dense cooperative 5G cellular networks. Transactions on Emerging Telecommunications Technologies. 2017; 29 (1):e3207.
Chicago/Turabian StyleZeeshan Kaleem; Kyunghi Chang. 2017. "QoS priority-based coordinated scheduling and hybrid spectrum access for femtocells in dense cooperative 5G cellular networks." Transactions on Emerging Telecommunications Technologies 29, no. 1: e3207.
The unmanned air-vehicle (UAV) or mini-drones equipped with sensors are becoming increasingly popular for various commercial, industrial, and public-safety applications. However, drones with uncontrolled deployment poses challenges for highly security-sensitive areas such as President house, nuclear plants, and commercial areas because they can be used unlawfully. In this article, to cope with security-sensitive challenges, we propose point-to-point and flying ad-hoc network (FANET) architectures to assist the efficient deployment of monitoring drones (MDr). To capture amateur drone (ADr), MDr must have the capability to efficiently and timely detect, track, jam, and hunt the ADr. We discuss the capabilities of the existing detection, tracking, localization, and routing schemes and also present the limitations in these schemes as further research challenges. Moreover, the future challenges related to co-channel interference, channel model design, and cooperative schemes are discussed. Our findings indicate that MDr deployment is necessary for caring of ADr, and intensive research and development is required to fill the gaps in the existing technologies.
Zeeshan Kaleem; Mubashir Husain Rehmani. Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions. 2017, 1 .
AMA StyleZeeshan Kaleem, Mubashir Husain Rehmani. Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions. . 2017; ():1.
Chicago/Turabian StyleZeeshan Kaleem; Mubashir Husain Rehmani. 2017. "Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions." , no. : 1.
This paper addresses the issues of resource allocation and co-channel interference management for coexistence of, and cooperation between, two long term evolution (LTE) networks. In the Republic of Korea, the LTE-based public safety (PS-LTE) network is being built for the 700-MHz frequency band. However, the same band is also allocated to the LTE-based high-speed railway (LTE-R) network, so immense interest and useful researches into co-channel interference management schemes are immediately needed. In this paper, we focus on the downlink system of coexisting PS-LTE and LTE-R networks by considering LTE-R radio access network (RAN) sharing and non-RAN sharing by PS-LTE users equipment (UEs) to analyze the co-channel interference. We also utilize cooperative communications schemes, such as coordinated multipoint (CoMP) and inter-cell interference coordination (ICIC) in order to resolve the problem of co-channel interference. We categorize the coexistence of PS-LTE and LTE-R networks into five different scenarios, and evaluate the performance of each scenario based on various performance indexes, such as UE average throughput, UE received interference, and UE outage probability. Moreover, users can achieve high throughput as well as obtain a better channel condition by using RAN sharing. In addition, we always provide the higher priority to railway user while allocating the resources for coexisting public safety and railway networks using LTE-R RAN sharing by PS-LTE UEs, because train control signal needs more reliable communication as well as low latency in order to fulfil its mission-critical service (MCS) demands. By employing coordinated scheduling (CS) CoMP, the highest throughput performance can be attained with RAN sharing. Furthermore, the dynamic ICIC enhances cell-edge UE performance and reduces UE received interference, as well as the outage probability, by using the partial reuse band and bonus band allocation.
Ishtiaq Ahmad; Wan Chen; Kyunghi Chang. LTE-Railway User Priority-Based Cooperative Resource Allocation Schemes for Coexisting Public Safety and Railway Networks. IEEE Access 2017, 5, 7985 -8000.
AMA StyleIshtiaq Ahmad, Wan Chen, Kyunghi Chang. LTE-Railway User Priority-Based Cooperative Resource Allocation Schemes for Coexisting Public Safety and Railway Networks. IEEE Access. 2017; 5 ():7985-8000.
Chicago/Turabian StyleIshtiaq Ahmad; Wan Chen; Kyunghi Chang. 2017. "LTE-Railway User Priority-Based Cooperative Resource Allocation Schemes for Coexisting Public Safety and Railway Networks." IEEE Access 5, no. : 7985-8000.
Ishtiaq Ahmad; Kyunghi Chang. Analysis on MIMO Transmit Diversity Techniques for Ship Ad-hoc Network under a Maritime Channel Model in Coastline Areas. The Journal of Korean Institute of Communications and Information Sciences 2017, 42, 383 -385.
AMA StyleIshtiaq Ahmad, Kyunghi Chang. Analysis on MIMO Transmit Diversity Techniques for Ship Ad-hoc Network under a Maritime Channel Model in Coastline Areas. The Journal of Korean Institute of Communications and Information Sciences. 2017; 42 (2):383-385.
Chicago/Turabian StyleIshtiaq Ahmad; Kyunghi Chang. 2017. "Analysis on MIMO Transmit Diversity Techniques for Ship Ad-hoc Network under a Maritime Channel Model in Coastline Areas." The Journal of Korean Institute of Communications and Information Sciences 42, no. 2: 383-385.