Publication Date: 2023
Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A (02533839)46(2)pp. 107-117
Wireless channels have a broadcast nature based on which opportunistic routing protocols work. In opportunistic routing protocols, packets are forwarded by the intermediate nodes that hear their transmissions, which are called candidate forwarders. In most of these algorithms, the forwarder list is pre-determined. However, the energy-efficient selection of the forwarder list is a research topic that is not considered well. Energy Efficient OPportunistic Routing algorithm (EEOPR) is presented in this paper in which the forwarders are determined on the packets’ fly. EEOPR is a flexible method that performs the routing process locally, and the candidate forwarders are selected during the routing and for each packet. The process of the candidate nodes’ selection and their packet forwarding are managed by the Genetic algorithm according to the nodes’ remaining energy and their regions. Simulation results show that network performance is improved in EEOPR compared to ROMER and CORP-M in terms of throughput, the number of duplicate packets, and the network nodes’ residual energy. © 2023 The Chinese Institute of Engineers.
Publication Date: 2016
Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A (02533839)39(4)pp. 493-497
Wireless sensor networks (WSNs) consist of small nodes that are capable of sensing, computing, and communication. One of the greatest challenges in WSNs is the limitation of energy resources in nodes. This limitation applies to all of the protocols and algorithms that are used in these networks. Routing protocols in these networks should be designed considering this limitation. Many papers have been published examining low energy consumption networks. One of the techniques that has been used in this context is cross-layering. In this technique, to reduce the energy consumption, layers are not independent but they are related to each other and exchange information with each other. In this paper, a cross-layer design is presented to reduce the energy consumption in WSNs. In this design, the communication between the network layer and medium access layer has been established to help the control of efforts to access the line to reduce the number of failed attempts. In order to evaluate our proposed design, we used the NS2 software for simulation. Then, we compared our method with a cross-layer design based on an Ad-hoc On-demand Distance Vector routing algorithm. Simulation results show that our proposed idea reduces energy consumption and it also improves the packet delivery ratio and decreases the end-to-end delay in WSNs. © 2016 The Chinese Institute of Engineers.
Publication Date: 2025
Journal Of Medical Signals And Sensors (22287477)15(2)
Background: Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance-pH (MII-pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection. Method: In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data. Result: A variety of signal-analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations. Discussion: This article outlines the datasets provided to participants and offers an overview of the competition results. © 2025 Journal of Medical Signals & Sensors.
Publication Date: 2023
Journal of Supercomputing (15730484)79(2)pp. 1426-1450
The need for computation speed is ever increasing. A promising solution for this requirement is parallel computing but the degree of parallelism in electronic computers is limited due to the physical and technological barriers. DNA computing proposes a fascinating level of parallelism that can be utilized to overcome this problem. This paper presents a new computational model and the corresponding design methodology using the massive parallelism of DNA computing. We proposed an automatic design algorithm to synthesis the logic functions on the DNA strands with the maximum degree of parallelism. In the proposed model, billions of DNA strands are utilized to compute the elements of the Boolean function concurrently to reach an extraordinary level of parallelism. Experimental and analytic results prove the feasibility and efficiency of the proposed method. Moreover, analyses and results show that a delay of a circuit in this method is independent of the complexity of the function and each Boolean function can be computed with O(1) time complexity. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2025
Computer Communications (1873703X)241
The emergence of 6th Generation (6G) cellular networks presents an opportunity to redefine Key Performance Indicators (KPIs) necessary for high-quality communications in the 2030s. 6G aims to innovate through novel architectural designs and the utilization of higher frequency bands, alongside incorporating aerial coverage to establish a three-dimensional network framework in contrast to its predecessor, 5G. Central to this innovation are Unmanned Aerial Vehicles (UAVs), which can be used as Drone Base Stations (DBSs). Despite the energy required for UAVs to hover, they can significantly decrease energy consumption and environmental impact by replacing terrestrial cellular infrastructure and switching off underutilized or inefficient Small Base Stations (SBSs) in Ultra-Dense Networks (UDNs). This work presents an energy-efficient UAV-assisted On-Off switching methodology that considers energy usage of DBSs’ backhaul links, in contrast to previous studies. By optimizing DBS placement, user association, and power control, the approach aims to improve energy efficiency. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) optimization, which is then decomposed into three manageable sub-problems that are solved using proposed algorithms. This methodological framework not only alleviates the complexity associated with the original problem but also enables practical implementations in energy-constrained UAV systems, ultimately leading to improved energy efficiency compared to existing approaches. Simulation results demonstrate about 90 % improvement of energy efficiency compared to prior studies even when fewer SBSs are switched off. Furthermore, the proposed approach exhibits 95 % better energy efficiency rather than previous methods when the serving time of UAVs increases. © 2025 Elsevier B.V.
The quality of delivering Internet of Things (IoT) traffic in IP networks is of great importance in IoT era. In this article, a Genetic Algorithm (GA)-based method is proposed to select the routing and scheduling strategy of each IP router to improve the quality of service of IoT traffic. To this aim, we first propose a method based on deep learning to distinguish IoT traffic from none-IoT ones. The trained model results in 99% accuracy on test data. Then, distinct scheduling and routing methods are suggested for these two traffic types in network routers. The aim of GA-based strategy selection is to improve the latency and reliability of IoT traffic without compromising the performance of none-IoT ones. Here, we utilize a set of scheduling algorithms including FIFO, Fair, Weighted Fair, and Priority algorithms to construct GA chromosomes. Also, a set of routing algorithms, i.e., Dijkstra, A∗, BFS, and DFS are used in definition of chromosomes. Simulation results demonstrate that the proposed method leads to a significant improvement in latency and reliability of IoT traffic. © 2023 IEEE.
Publication Date: 2022
Peer-to-Peer Networking and Applications (19366450)15(1)pp. 246-266
In some Vehicular Ad Hoc Networks (VANETs) applications, the geocast routing protocol is used for data transmission from a source vehicle to a group of vehicles located in a common region. Efficient data transmission to the destination region is one of the critical challenges of geocast routing protocols. In this research, the geocast routings are considered that exploit rateless coding to improve the reliability, and so packet delivery ratio. Some of these geocast routing methods use flooding schemes to deliver the messages to the destination region. However, in order to cut high overheads caused by flooding schemes, the routing protocols that use unicast routes for data delivery have been taken into account. In this way, recent geocast routing protocols exploit on-demand unicast routing methods such as Ad-hoc On-Demand Distance Vector (AODV) to deliver the packets to the destination region and then broadcast them in that area. However, the packet delivery ratio and the delay of those methods are respectively lower and higher than flooding-based methods. This paper proposes to exploit the table-driven Optimized Link State Routing (OLSR) protocol to deliver the messages to the destination region. To customize the OLSR protocol for geocasting, we propose a number of modifications to message flows and data exchanges. Compared to on-demand geocast protocols, OLSR imposes lower message delay and delivers more messages to the destination region at a higher overhead expense. To overcome this overhead, we also propose algorithms to adjust the control message intervals of the OLSR protocol in each node. Simulation results show that our OLSR-based protocol demonstrates better performance in terms of delay and packet delivery ratio than those of the traditional AODV-based method and CALAR-DD protocol regarding various vehicles' densities, vehicles' velocities, message sizes, and destination region sizes. Compared to the traditional OLSR, using the tuned OLSR-based method has also significantly reduced the signaling overhead costs. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2013
Computer Communications (1873703X)36(10-11)pp. 1101-1119
Integration of various wireless access technologies is one of the major concerns in recent wireless systems in which multi-technology mobile devices are provided to users to roam between different access networks. Being an essential part in heterogeneous wireless systems, vertical handover is more complex than conventional horizontal handover. As IEEE 802.21 Media Independent Handover (MIH) is the standard addressing a uniform and media-independent framework for seamless handover between different access technologies, many works have been carried out in the literature to employ MIH services in handover management This paper presents a comprehensive survey of the proposed mobility management mechanisms that are using this framework. As a comparative view, the paper categorizes the efforts according to the layer of mobility management and evaluates some of the representative methods discussing about their advantages and disadvantages The paper also looks into recent handover decision and interface management methods that are exploiting MIH Moreover, the extensions and the amendments proposed on MIH are overviewed. © 2013 Elsevier B.V. All rights reserved.
A recommender system is an information filtering tool that copes with the growing volume of information and helps the user to make faster decisions by providing products and services matched with their needs and interests. However, a large number of users are not satisfied with the provided recommendations and do not accept them. Based on the Elaboration Likelihood Model (ELM), If supplementary information about recommendations is provided, those users having the low motivation and capability to analyze the usefulness of the recommended item can be persuaded to accept it. This paper focuses on analyzing the impact of demographic factors on increasing the acceptance of recommendations. This study was conducted by a web-based online survey. The movie's recommender system has been developed along with the explanations based on Cialdini's persuasion strategies as the peripheral cues. The collected data are analyzed through statistical techniques using the SPSS software. The results show that the persuasiveness degree of the persuasion strategies differs related to individuals with the different demographic factors. © 2018 IEEE.
Publication Date: 2014
Multimedia Systems (14321882)20(2)pp. 215-226
In this paper a data hiding method is proposed based on the combination of a secret sharing technique and a novel steganography method using integer wavelet transform. In this method in encoding phase, first a secret image is shared into n shares, using a secret sharing technique. Then, the shares and Fletcher-16 checksum of shares are hidden into n cover images using proposed wavelet based steganography method. In decoding phase, t out of n stego images are required to recover the secret image. In this phase, first t shares and their checksums are extracted from t stego images. Then, by using the Lagrange interpolation the secret image is revealed from the t shares. The proposed method is stable against serious attacks, including RS and supervisory training steganalysis methods, it has the lowest detection rate under global feature extraction classifier examination compared to the state-of-the-art techniques. Experimental results on a set of benchmarks showed that this method outperforms conventional methods in offering a high secure and robust mechanism for joining secret image sharing and steganography. © 2013 Springer-Verlag Berlin Heidelberg.
Image processing softwares, like all softwares, need to be both verified and validated. Synthetic images are very useful during the medical software development process to verify the accuracy of algorithms. In this paper we introduce the process of generating synthetic 2D medical X-ray images in addition to ground truth imaging parameters. First, a 3D model of an organ (e.g., vessels) is made in a 3D-modeling software. Then, this volume model is voxelized based on the specified resolution in order to create a 3D CT image of that organ by assigning proper Hounsfield unit to each voxel. The obtained 3D CT image volume is used in DRR program as the input. Geometry parameters such as internal and external parameters are adjusted to take some images from different views. We demonstrated this process by three examples to confirm its usage in validation of medical image processing applications. © 2014 IEEE.
Publication Date: 2025
Neurocomputing (09252312)616
Open Information Extraction (Open IE) is the task of identifying structured and machine-readable information from natural language text within an open domain context. This research area has gained significant importance in the field of natural language processing (NLP), attracting considerable attention for its potential to extract valuable information from unstructured textual data. Previous investigations heavily relied on manual extraction patterns and various NLP tools. While these methods often produce errors that accumulate and propagate throughout the systems, ultimately affecting the accuracy of the results. Moreover, recent Open IE studies have focused on extracting binary relations involving two entities. However, these binary approaches occasionally lead to the omission of essential information in the text, preventing a deeper comprehension of the content. This limitation arises from the fact that real-world relations often involve multiple entities, but binary approaches may oversimplify these relations and miss additional details crucial for a thorough understanding of text. To address these challenges, our study introduces an innovative system called “Open N-ary Information EXtraction (ONIEX).” This system incorporates two novel techniques: multihead relation attention mechanism and relation embedding. Multihead relation attention, in combination with relation embedding, enables the system to focus on relations extracted through the SpanBERT model and accurately identify associated entities for each relation. The ONIEX system's superior performance is substantiated through extensive experiments conducted on the OpenIE4 and LSOIE datasets, benchmark datasets for Open n-ary Information Extraction (Open n-ary IE). The results demonstrate the superiority of the ONIEX system over the existing state-of-the-art systems. © 2024 Elsevier B.V.
Recent advances in the field of Question Answering (QA) have improved state-of-the-art results. Due to the availability of rich English training datasets for this task, most results reported are for this language. However, due to the lack of Persian datasets, less research has been done for the latter language therefore the results are hard to compare. In the present work, we introduce the Persian Question Answering Dataset (ParSQuAD) translated from the well-known SQuAD 2.0 dataset. Our dataset comes in two versions depending on whether it has been manually or automatically corrected. The result is the first large-scale QA training resource for Persian. We train three baseline models, one of which, achieves an F1 score of 56.66% and an exact match ratio of 52.86% on the test set with the first version and an F1 score of 70.84 % and an exact match ratio of 67.73% with the second version. © 2021 IEEE.
We propose a novel dependency-based reordering model for hierarchical SMT that predicts the translation order of two types of pairs of constituents of the source tree: head-dependent and dependent-dependent. Our model uses the dependency structure of the source sentence to capture the medium- and long-distance reorderings between these pairs of constituents. We describe our reordering model in detail and then apply it to a language pair in which the languages involved follow different word order patterns, English (SVO) and Farsi (free word order being SOV the most frequent pattern). Our model outperforms a baseline (standard hierarchical SMT) by 0.78 BLEU points absolute, statistically significant at p = 0.01. © 2015 The authors.
Publication Date: 2018
Computers and Education (0360-1315)120pp. 75-89
The quality of online information is highly variable because anyone can post data on the internet, and not all online sources are equally reliable, valuable, or accurate. Previous studies reveal problems with online information evaluation skills and a lack of ability in using evaluation criteria, including currency, relevance, authority, accuracy and purpose. The primary purpose of this study is to develop a framework for cooperative and interactive mobile learning to improve students' online information evaluation skills. A mobile learning application is subsequently developed based on the proposed framework. To assess the effectiveness of the developed application, an experiment is conducted on diploma students in a university. A usability questionnaire is conducted on an experimental group to identify students' perceptions regarding the usability of the developed mobile application. The experimental results indicate that the application is significantly more effective with an effect size of 1.91 in improving students’ online information evaluation skills than traditional learning. The results contribute to the extant literature in the context of mobile learning by identifying usability evaluation features and providing a framework for developing cooperative and interactive mobile learning. The implications of the present findings for research and instructional practice are discussed. © 2018 Elsevier Ltd
Publication Date: 2025
European Physical Journal Plus (21905444)140(8)
Researchers and designers should face the challenges caused by memory and energy limitations. Quantum-dot Cellular Automata (QCA) offers a promising alternative with its high speed and low power consumption for dense emerging nano-electronic structures. Applying the approximate computing paradigm, where lower hardware complexity is prioritized over complete accuracy, can reduce power consumption. Integrating approximate computing with QCA reduces energy consumption and enhances system performance, although at the potential cost of reduced accuracy. The arithmetic unit is responsible for binary addition, subtraction, and multiplication. This article proposes a methodology for integrating QCA-based gates with approximate computing to achieve high-speed computation while minimizing resource usage. Additionally, it introduces a novel high-speed and cost-efficient design for a QCA-based approximate full adder, demonstrating improved hardware evaluation metrics, including delay, energy consumption, and acceptable error margins. The cost analysis indicates that the proposed design effectively balances circuit design trade-offs, particularly regarding delay and area. The functionality validation of the proposed circuit is assessed by the QCADesigner-E tool. Compared to the state of the art, the proposed design enhances performance metrics, achieving average improvements of 50% in delay, 26% in the number of QCA cells, and 78% in cost. These advancements are significant for the development of efficient and cost-effective QCA-based systems. Various error evaluation metrics assess the proposed approximate full adder's computational accuracy across three implementation scenarios of the 8-bit approximate adder architecture. Application-level simulation outputs show that the proposed circuits perform well in all scenarios, with the Peak-Signal-to-Noise Ratio (PSNR) exceeding 30 dB. © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Publication Date: 2020
IEEE Access (21693536)8pp. 58585-58593
The huge quantity of nodes and interconnections in modern binary circuits leads to extremely high levels of energy consumption. The interconnection complexity and other issues of binary circuits encourage researchers to consider multiple-valued logic (MVL) alternatives. Features of Carbon Nanotube Field-Effect Transistors (CNTFETs) make this technology a potential candidate to implement MVL circuits. In this article, a new systematic methodology is proposed to design ternary logic block circuits based on CNTFETs. The methodology is applied to the design of two basic logic circuits, a half adder and a 1-digit multiplier, which are evaluated through HSPICE simulations. Simulation results indicate improvements over current equivalents in transistor count and PDP mean with the half adder version of 19.2%, and 74.07% respectively, and with the 1-digit multiplier of 24.67% and 81.12% respectively. © 2013 IEEE.
Publication Date: 2020
International Journal of Network Management (10557148)30(4)
With the daily increase in the number of cloud users and the volume of submitted workloads, load balancing (LB) over clouds followed by a reduction in users' response time is emerging as a vital issue. To successfully address the LB problem, we have optimized workload distribution among virtual machines (VMs). This approach consists of two parts: Firstly, a meta-heuristic method based on biogeographical optimization for workload dispatching among VMs is introduced; secondly, we propose an innovative heuristic algorithm inspired by the “Banker algorithm” that runs in core scheduler to control and avoid VM overloads. The combination of these two (meta-)heuristic algorithms constitutes an LB approach through which we have been able to reduce the value of the makespan to a reasonable time frame. Moreover, an information base repository (IBR) is introduced to maintain the online processing status of physical machines (PMs) and VMs. In our approach, data stored in IBR are retrieved when needed. This approach is compared with well-known (non-)evolutionary approaches, such as round-robin, max-min, MGGS, and TBSLB-PSO. Experimental results reveal that our proposed approach outperforms its counterparts in a heterogeneous environment when the resources are smaller than the workloads. Moreover, the utilization of physical resources gradually increases. Therefore, optimal workload scheduling, as well as the lack of overload occurrence, results in a reduction in makespan. © 2020 John Wiley & Sons, Ltd.
Publication Date: 2017
Journal of Intelligent and Fuzzy Systems (18758967)32(6)pp. 3987-3998
Finding community structures in online social networks is an important methodology for understanding the internal organization of users and actions. Most previous studies have focused on structural properties to detect communities. They do not analyze the information gathered from the posting activities of members of social networks, nor do they consider overlapping communities. To tackle these two drawbacks, a new overlapping community detection method involving social activities and semantic analysis is proposed. This work applies a fuzzy membership to detect overlapping communities with different extent and run semantic analysis to include information contained in posts. The available resource description format contributes to research in social networks. Based on this new understanding of social networks, this approach can be adopted for large online social networks and for social portals, such as forums, that are not based on network topology. The efficiency and feasibility of this method is verified by the available experimental analysis. The results obtained by the tests on real networks indicate that the proposed approach can be effective in discovering labelled and overlapping communities with a high amount of modularity. This approach is fast enough to process very large and dense social networks. © 2017-IOS Press and the authors. All rights reserved.
With widespread use of distributed system in various applications, having fault tolerance structure is a significant property and this is while it makes more sense in designing of real time distributed system. With regard using some middleware like CORBA is used in the designing of such systems but, however, programs capable of having the specification of real time and fault tolerance at the same time are not supported therein. In this paper, FT-CORBA structure as a structure used for supporting fault tolerance programs as well as relative important parameters including replication style and number of replica which play further role in improved performance and making it adaptive to real time distributed system have been reviewed. Studying these specifications have been made a structure adaptive to real time systems with higher performance than FT-CORBA and, finally the implementing of the said structure and determination of the number of replica and the replication style as well as the significance of related parameters have been investigated.
In this paper a steganalysis method is presented for a steganographic routine. The steganography is based on pixel value differencing, PVD, and no attacked has been offered for it yet. By modification of an existing method, that uses χ2 measure, the PVD steganography was successfully and accurately attacked. ©2007 IEEE.
Publication Date: 2022
IEEE Transactions on Fuzzy Systems (1063-6706)30(9)pp. 3918-3927
An inherent property of natural languages is the possibility of distinct meanings for the same word in different sentences. Word sense induction (WSI) is the unsupervised process of discovering the meanings of a word. The meanings form a sense inventory, which is used for word sense disambiguation (WSD). Fuzzy logic's capability at uncertainty representation makes it perfectly applicable for handling the vague information processed in natural languages for WSI and WSD. In this article, a novel fuzzy-based methodology is proposed for extracting meaningful information from ambiguous words, where both word senses and sense inventories are modeled as linguistic variables. The proposed method aims to gather a term set of level-2 fuzzy values for the variables representing words' meanings, to achieve WSI. The values in the term set are, then, used for linguistic approximation using a fuzzy inference system designed for WSD based on word's context. The fuzzy word senses are extracted from an input corpus by word substitution, i.e., predicting words suitable as substitutes for the target word using masked language models. These fuzzy substitute sets are, then, clustered to discover similarities in the semantics they represent. Finally, each cluster is reformed into a sense value and added to the term set for the target word. The experimental results show that the proposed system outperforms the systems submitted to the standard SemEval 2010 and 2013 WSI and WSD tasks and achieves comparable performance with other fuzzy and nonfuzzy state-of-the-art methods. © 1993-2012 IEEE.
Publication Date: 2023
IEEE Transactions on Intelligent Transportation Systems (1524-9050)24(12)pp. 14718-14731
Due to the rapid growth of the Internet of Vehicles (IoV) and the rise of multimedia services, IoV networks' servers and switches are facing resource crises. Multimedia vehicles connected to the Internet of Things are increasing; there are millions of vehicles and heavy multimedia traffic in the IoV network. The network's scarcity of resources results in overload, which, in turn, leads to a degradation of both Quality of Service (QoS) and Quality of Experience (QoE). Conversely, when resources are abundant, it leads to unnecessary energy wastage. Managing IoV network resources optimally while considering constraints such as Energy, Load, QoS, and QoE is a complex challenge. To address this, the study proposes a solution by decomposing the problem and designing a modular architecture named $\textit {ELQ}^{\vphantom {D^{j}}2}$. This architecture enables simultaneous control of the mentioned constraints, effectively reducing overall complexity. To achieve this objective, Network Softwarization and Virtualization concepts are employed. This modern architecture allows dynamically adjusting of the scale of the resources on demand, effectively reducing energy usage. Additionally, this architecture provides some other potentials, such as 'the distribution of multimedia traffic among servers', 'determining the route with high QoS for traffic', and 'selecting a media with high QoE'. A real test field is provided by Floodlight Controller, Open vSwitch, and Kamailio Server tools to evaluate the performance of ${ELQ}^{2}$. The findings suggest that the utilization of ${ELQ}^{2}$ holds promise in reducing the count of active servers and switches via effective resource management. Additionally, it demonstrates enhancements in various QoS and QoE parameters, encompassing throughput, multimedia delay, R Factor, and MOS, accomplished through load balancing strategies. As an illustration, the deployment of flows has achieved a commendable success rate of 95% owing to the utilization of SDN-based and comprehensive management practices encompassing all network resources. © 2000-2011 IEEE.
Publication Date: 2018
Telecommunication Systems (10184864)67(2)pp. 309-322
The extent and diversity of systems, provided by IP networks, have made various technologies approach integrating different types of access networks and convert to the next generation network (NGN). The session initiation protocol (SIP) with respect to facilities such as being in text form, end-to-end connection, independence from the type of transmitted data, and support various forms of transmission, is an appropriate choice for signalling protocol in order to make connection between two IP network users. These advantages have made SIP be considered as a signalling protocol in IP multimedia subsystem (IMS), a proposed signalling platform for NGNs. Despite having all these advantages, SIP protocol lacks appropriate mechanism for addressing overload causing serious problems for SIP servers. SIP overload occurs when a SIP server does not have enough resources to process messages. The fact is that the performance of SIP servers is largely degraded during overload periods because of the retransmission mechanism of SIP. In this paper, we propose an advanced mechanism, which is an improved method of the windows based overload control in RFC 6357. In the windows based overload control method, the window is used to limit the amount of message generated by SIP proxy server. A distributed adaptive window-based overload control algorithm, which does not use explicit feedback from the downstream server, is proposed. The number of confirmation messages is used as a measure of the downstream server load. Thus, the proposed algorithm does not impose any additional complexity or processing on the downstream server, which is overloaded, making it a robust approach. Our proposed algorithm is developed and implemented based on an open source proxy. The results of evaluation show that proposed method could maintain the throughput close to the theoretical throughput, practically and fairly. As we know, this is the only SIP overload control mechanism, which is implemented on a real platform without using explicit feedback. © 2017, Springer Science+Business Media New York.
Publication Date: 2019
International Journal of Communication Systems (10991131)32(18)
Information-centric networking (ICN) has emerged as a promising candidate for designing content-based future Internet paradigms. ICN increases the utilization of a network through location-independent content naming and in-network content caching. In routers, cache replacement policy determines which content to be replaced in the case of cache free space shortage. Thus, it has a direct influence on user experience, especially content delivery time. Meanwhile, content can be provided from different locations simultaneously because of the multi-source property of the content in ICN. To the best of our knowledge, no work has yet studied the impact of cache replacement policy on the content delivery time considering multi-source content delivery in ICN, an issue addressed in this paper. As our contribution, we analytically quantify the average content delivery time when different cache replacement policies, namely, least recently used (LRU) and random replacement (RR) policy, are employed. As an impressive result, we report the superiority of these policies in term of the popularity distribution of contents. The expected content delivery time in a supposed network topology was studied by both theoretical and experimental method. On the basis of the obtained results, some interesting findings of the performance of used cache replacement policies are provided. © 2019 John Wiley & Sons, Ltd.
Publication Date: 2017
International Journal of Remote Sensing (13665901)38(12)pp. 3608-3634
This article proposes a new algorithm for hyperspectral image classification. The proposed method is a spectral–spatial method based on wavelet transforms, kernel minimum noise fraction (KMNF) and spatial–spectral Schroedinger eigenmaps (SSSE). To overcome the computation complexity, one-dimensional discrete wavelet transform (1D-DWT) is applied in spectral domain. To reduce noise, KMNF coefficients are extracted in wavelet space. To solve time-consuming problem, 2D-DWT coefficients are employed in spatial space. Hence, the combination of 1D-DWT, KMNF, and 2D-DWT is suggested to create SSSE features. The classification is carried out by a Support Vector Machine (SVM) classifier. Experimental results show that classification accuracy and time consumption are effectively improved compared to the state-of-the art reported spectral–spatial SVM-based methods. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2020
Applied Soft Computing (1568-4946)91
Evolvable hardware (EH) architectures are capable of changing their configuration and behavior dynamically based on inputs from the environment. In this paper, we investigate the feasibility of using EH to prevent Hardware Trojan Horses (HTHs) from being inserted, activated, or propagated in a digital electronic chip. HTHs are malicious hardware components that intend to leak secret information or cause malfunctioning at run-time in the chip in which they are integrated. We hypothesize that EH can detect internal circuit errors at run-time and reconfigure to a state in which the errors are no longer present. We implement a Virtual Reconfigurable Circuit (VRC) on a Field-Programmable Gate Array (FPGA) that autonomously and periodically reconfigures itself based on an Evolutionary Algorithm (EA). New VRC configurations are generated with an on-chip EA engine. We show that the presented approach is applicable in a scenario in which (1) the HTH-critical areas in the circuit are known in advance, and (2) the VRC is a purely combinatorial circuit, as opposed to the on-chip memory holding the golden reference, which requires one or more cycles to be read/written. We compare two different approaches for protecting the system against HTHs: Genetic Programming (GP) and Cartesian Genetic Programming (CGP). The paper reports on experiments on four benchmark circuits and gives an overview of both the limitations and the added value of the presented approaches. © 2020 Elsevier B.V.
