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Author

Junaid Imtiaz

Other affiliations: Hanyang University
Bio: Junaid Imtiaz is an academic researcher from Bahria University. The author has contributed to research in topics: Cognitive radio & Computer science. The author has an hindex of 5, co-authored 17 publications receiving 86 citations. Previous affiliations of Junaid Imtiaz include Hanyang University.

Papers
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Proceedings ArticleDOI
08 Jun 2012
TL;DR: A method for accurate blood vessel detection which can be used for detection of neovascularization is presented and a new method for vessel segmentation using a multilayered thresholding technique is presented.
Abstract: Retinal image analysis is very effective in early detection and diagnosis of diabetic retinopathy. Diabetic retinopathy is a progressive disease and is broadly classify into two stages i.e. Non proliferative diabetic retinopathy (NPDR) and Proliferative diabetic retinopathy (PDR). A sign of PDR is the appearance of new blood vessels in fundus area and inside optic disc known as neovascularization. The study of blood vessel is very important for detection of neovascularization. In this paper, we present a method for accurate blood vessel detection which can be used for detection of neovascularization. The paper presents a new method for vessel segmentation using a multilayered thresholding technique. The method is tested using two publicly available retinal image databases and experimental results show the significance of proposed work.

31 citations

Journal ArticleDOI
18 Feb 2019-Sensors
TL;DR: Enhanced three layer hybrid clustering mechanism is proposed that limits the exchange of control packets between nodes after every round for lower layer head selection and outperforms HHCA technique in network lifetime based on Half of the Nodes Alive (HNA) by 18 percent.
Abstract: Recently, different routing techniques were proposed for three layer clustering topology in Wireless Sensor Network (WSN) which outperform the basic two layer clustering hierarchy. The problem that remains in these approaches is the heavy control packet exchange between nodes after every round in order to choose efficient lower layer heads. Among these techniques is Hybrid Hierarchical Clustering Approach (HHCA), in which a distributed approach is proposed. According to HHCA, the upper layer heads are centrally selected by base station, while sensor nodes only have to select lower layer heads distributively. In this paper, enhanced three layer hybrid clustering mechanism is proposed that limits the exchange of control packets between nodes after every round for lower layer head selection. The energy of nodes are divided into levels upon which it is decided when nodes of a cluster need to enter into new cluster head selection phase. The proposed mechanism helps to limit control packet exchange between nodes to a large extent, at the same time keeping energy consumption between nodes balanced. Moreover, it is focused that higher layer heads are selected by base station in a manner that reduces backward transmission in the network as much as possible. Simulation results show that nodes in the proposed mechanism stay alive for a longer time as compared to other approaches, and it outperforms HHCA technique in network lifetime based on Half of the Nodes Alive (HNA) by 18 percent.

30 citations

Journal ArticleDOI
TL;DR: In this article, a fractional-order sliding mode control (FOSMC) was proposed for a D-STATCOM to compensate the low power distribution system by injecting/absorbing a specific extent of the reactive power under disturbances.
Abstract: At present, the disturbances like the voltage fluctuations, resulting from the grid’s complexities and unbalanced load conditions, create severe power quality concerns like total harmonic distortion (THD) and voltage unbalance factor (VUF) of the grid voltage. Though the custom power devices such as distribution-static compensators (D-STATCOMs) improve these power quality concerns, however, the accompanying controller plays the substantial role. Therefore, this paper proposes a fractional-order sliding mode control (FOSMC) for a D-STATCOM to compensate the low power distribution system by injecting/absorbing a specific extent of the reactive power under disturbances. FOSMC is a non-linear robust control in which the sliding surface is designed by using the Riemann-Liouville ( RL ) function and the chattering phenomenon is minimized by using the exponential reaching law. The stability of FOSMC is evidenced by employing the Lyapunov stability criteria. Moreover, the performance of the proposed FOSMC is further accessed while doing its parametric variations. The complete system is demonstrated with a model of 400V, 180kVA radial distributor along with D-STATCOM under two test scenarios in MATLAB/Simulink environment. The results of the proposed controller are compared with the fixed frequency sliding mode control (FFSMC) and conventional proportional-integral (PI) control. The results validate the superiority of the proposed controller in terms of rapid tracking, fast convergence, and overall damping with very low THD and VUF.

22 citations

Journal ArticleDOI
13 Feb 2019-Energies
TL;DR: In this article, a finite control set model predictive control (FCS-MPC) technique-based controller is proposed for the inverter of the uninterrupted power supply (UPS) system.
Abstract: In this paper, the finite control set model predictive control (FCS–MPC) technique-based controller is proposed for the inverter of the uninterrupted power supply (UPS) system. The proposed controller uses the mathematical model of the system to forecast the response of voltage for each possible switching state for every sampling instant. Following this, the cost function was used to determine the switching state, applied to the next sampling instant. First, the proposed control strategy was implemented for the single inverter of the UPS system. Finally, the droop control strategy was implemented for parallel inverters to guarantee actual power sharing among a multiple-parallel UPS system. To validate the performance of the proposed controller under steady-state conditions and dynamic-transient conditions, extensive simulations were conducted using MATLAB/Simulink. The proposed work shows a low computational burden, good steady state performance, fast transient response, and robust results against parameter disturbances as compared to linear control. The simulation results showed that total harmonic distortion (THD) for the linear load was 0.9% and THD for the nonlinear load was 1.42%.

21 citations

Proceedings ArticleDOI
23 Jul 2011
TL;DR: A new technique for fingerprint segmentation using morphological operations and modified gradient based technique that gives high accuracy for Fingerprint segmentation even for low quality fingerprint images is presented.
Abstract: For personal identification the use of fingerprint identification systems is mostly common. First step for an Automated Fingerprint Identification System (AFIS) is the segmentation of fingerprint from the acquired image. Extraction of region of interest (ROI) from the desired fingerprint impression is the main purpose of segmentation. In this paper, we present a new technique for fingerprint segmentation using morphological operations and modified gradient based technique. The distinct feature of our technique is that it gives high accuracy for fingerprint segmentation even for low quality fingerprint images. The proposed algorithm is applied on standard fingerprint databases, FVC2002 and FVC2004. Experimental results demonstrate the improved performance of the proposed scheme.

5 citations


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Journal ArticleDOI
TL;DR: Compared to state-of-art IoT-based farming methods, the CL-IoT reduces energy consumption, communication overhead, and end-to-end delay up to a certain extent and maximizes the network throughput.
Abstract: Internet of Things (IoT) for Intelligent Manufacturing of Smart Farming gained significant attention from researchers to automate various farming applications called Smart Farming (SF). The sensors and actuators deployed across the farm using which farmers receive periodic farm information related to temperature, soil moisture, light intensity, and water used, etc. The clustering-based methods are proven energy-efficient solutions for Wireless Sensor Networks (WSNs). However, by considering long-distance communications and scalable networks of IoT enabled SF; the present clustering solutions cannot be feasible and having higher delay and latency for various SF applications. To focus on requirements SF applications, an efficient and scalable protocol for remote monitoring and decision making of farms in rural regions called CL-IoT protocol proposed. A cross-layer-based clustering and routing algorithms have designed to reduce network communication delay, latency, and energy consumption. The cross-layer-based optimal Cluster Head (CH) selection solution proposed to overcome the energy asymmetry problem in WSN. The parameters of different layers like a physical, medium access control (MAC), and network layer of each sensor used to evaluate and select optimal CH and efficient data transmission. The nature-inspired algorithm proposed with a novel probabilistic decision rule functions as a fitness function to discover the optimal route for data transmission. The performance of the CL-IoT protocol analyzed using NS2 by considering the energy-efficiency, computational-efficiency, and QoS-efficiency factors. Compared to state-of-art IoT-based farming methods, the CL-IoT reduces energy consumption, communication overhead, and end-to-end delay up to a certain extent and maximizes the network throughput.

97 citations

Journal ArticleDOI
TL;DR: The paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of the online database compared to other public eye fundus image databases for diabetic retinopathy purposes.
Abstract: Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

75 citations

Journal ArticleDOI
TL;DR: A novel Nature-Inspired algorithm-based Cross-layer Clustering (NICC) protocol is designed to find a reasonably better solution for clustering and routing in SF applications and explores the idea of a nature-inspired optimization algorithm called Bacterial Foraging Optimization with optimal fitness function, which models the trade-off among the energy efficiency and optimal data transmission.
Abstract: The Internet of Things (IoT) is a subclass of the Industry 4.0 standard. The functionality of IoT depends on the Wireless Sensor Networks (WSNs) design. The IoT-empowered WSNs received the researcher's attention for the Smart Farming (SF) applications. SF nowadays is required to enhance farm productivity while minimizing the cost and resources. The agriculture sensors devices disposed over the farm collect the on-field farm data and transfer it wirelessly to the base station for decision-making and agriculture monitoring. As the nodes are resource restrained, the process of periodic farm data gathering and multi-hop delivery needs to be effective in terms of Quality of Service (QoS) and energy-efficiency of information transmission by reflecting the long-distance transmission difficulties of SF applications. To enhance the network lifetime substantially of densely deployed WSN for periodically monitoring of farm conditions, we propose a novel Nature-Inspired algorithm-based Cross-layer Clustering (NICC) protocol. We design NICC to find a reasonably better solution for clustering and routing in SF applications. NICC explores the idea of a nature-inspired optimization algorithm called Bacterial Foraging Optimization (BFO) with optimal fitness function, which models the trade-off among the energy efficiency and optimal data transmission. We design a BFO algorithm to select the optimal sensor node for clustering and routing problems based on cross-layer parameters-based fitness value computation. The cross-layer parameter includes the sensor parameters from layers like network layer, physical layer, and Medium Access Control (MAC). The numerical results show the superiority of the NICC protocol for various WSN-assisted SF scenarios against state-of-art clustering techniques.

70 citations

Journal ArticleDOI
TL;DR: A novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of Retinopathy, which are microaneurysms is presented.
Abstract: Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms.

53 citations

Journal ArticleDOI
TL;DR: The proposed SecDL approach achieves security, QoS and energy efficiency in dynamic cluster-based WSN-IoT networks and also concentrates on IoT-user security since the sensory data can be accessed by IoT users.
Abstract: In WSN-assisted IoT, energy efficiency and security which play pivotal role in Quality of Service (QoS) are still challenging due to its open and resource constrained nature Although many research works have been held on WSN-IoT, none of them is able to provide high-level security with energy efficiency This paper resolves this problem by designing a novel Secure Deep Learning (SecDL) approach for dynamic cluster-based WSN-IoT networks To improve energy efficiency, the network is designed to be Bi-Concentric Hexagons along with Mobile Sink technology Dynamic clusters are formed within Bi-Hex network and optimal cluster heads are selected by Quality Prediction Phenomenon (QP2) that ensure QoS and also energy efficiency Data aggregation is enabled in each cluster and handled with a Two-way Data Elimination then Reduction scheme A new One Time-PRESENT (OT-PRESENT) cryptography algorithm is designed to achieve high-level security for aggregated data Then, the ciphertext is transmitted to mobile sink through optimal route to ensure high-level QoS For optimal route selection, a novel Crossover based Fitted Deep Neural Network (Co-FitDNN) is presented This work also concentrates on IoT-user security since the sensory data can be accessed by IoT users This work utilizes the concept of data mining to authenticate the IoT users All IoT users are authenticated by Apriori based Robust Multi-factor Validation algorithm which maps the ideal authentication feature set for each user In this way, the proposed SecDL approach achieves security, QoS and energy efficiency Finally, the network is modeled in ns-326 and the results show betterment in network lifetime, throughput, packet delivery ratio, delay and encryption time

44 citations