Faculty of Computer Engineering The Faculty of Computer Engineering at University of Isfahan, established in 1992, has grown into one of Iran premier institutions for computer science education and research, offering comprehensive programs in artificial intelligence, data science, computer networks, and software engineering with a strong emphasis on industry-academia collaboration.
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One of the attacks in the RPL protocol is the Clone ID attack, that the attacker clones the node's ID in the network. In this research, a Clone ID detection system is designed for the Internet of Things (IoT), implemented in Contiki operating system, and evaluated using the Cooja emulator. Our evaluation shows that the proposed method has desirable performance in terms of energy consumption overhead, true positive rate, and detection speed. The overhead cost of the proposed method is low enough that it can be deployed in limited-resource nodes. The proposed method in each node has two phases, which are the steps of gathering information and attack detection. In the proposed scheme, each node detects this type of attack using control packets received from its neighbors and their information such as IP, rank, Path ETX, and RSSI, as well as the use of a routing table. The design of this system will contribute to the security of the IoT network. © 2021 IEEE.
Task allocation, as an important issue in multi-agent systems (MAS), is defined as allocating the tasks to the agents such that maximum tasks are performed in minimum time. The vast range of application domains, such as scheduling, cooperation in crisis management, and project management, deal with the task allocation problem. Despite the plethora of algorithms that are proposed to solve this problem in different application domains, research on proposing a formalism for this problem is scarce. Such a formalism can be used as a way for better understanding and analyzing the behavior of real-world systems. In this paper, we propose a new formalism for specifying capability-based task allocation in MAS. The formalism can be used in different application domains to help domain experts better analyze and test their algorithms with more precision. To show the applicability of the formalism, we consider two algorithms as the case studies and formalize the inputs and outputs of these algorithms using the proposed formalism. The results indicate that our formalism is promising for specifying the capability-based task allocation in MAS at a proper level of abstraction. © 2021 IEEE.
Question Answering is a hot topic in artificial intelligence and has many real-world applications. This field aims at generating an answer to the user's question by analyzing a massive volume of text documents. Answer Selection is a significant part of a question answering system and attempts to extract the most relevant answers to the user's question from the candidate answers pool. Recently, researchers have attempted to resolve the answer selection task by using deep neural networks. They first employed the recurrent neural networks and then gradually migrated to convolutional neural networks. Nevertheless, the use of language models, which is implemented by deep neural networks, has recently been considered. In this research, the DistilBERT language model was employed as the language model. The outputs of the Question Analysis part and Expected Answer Extraction component are also applied with [CLS] token output as the final feature vector. This operation leads to improving the method performance. Several experiments are performed to evaluate the effectiveness of the proposed method, and the results are reported based on the MAP and MRR metrics. The results show that the MAP values of the proposed method improved by 0.6%, and the MRR metric is improved by 0.2%. The results of our research show that using a heavy language model does not guarantee a more reliable method for answer selection problem. It also shows that the use of particular words, such as Question Word and Expected Answer word, can improve the performance of the method. © 2020 IEEE.
Industry 4.0 provides a framework for applying new technologies in industrial environments to boost the efficiency and intelligence. A recently blossomed technology in Industry 4.0 is Internet of Things (IoT), which allows us to create a smart environment by connecting various equipment. One of the main applications of IoT in a smart factory is to design monitoring systems, which helps put the behavior of devices under permanent and comprehensive supervision. However, the rapid growth and change in the monitoring facilities creates a big challenge for people who either want to use that equipment in Industry 4.0, or want to update the systems to benefit from this technology. To address this problem, this paper presents new approach based on model-driven engineering paradigm, for simplifying the design and development of real-Time monitoring systems in an industrial environment. Our approach includes a domain-specific modeling language, a graphical editor, and model-To-code transformations that generate a hardware descriptive code, a mobile application, and a web application for a monitoring system. To evaluate the applicability of our approach, a scenario in the power industry has been designed, which offers user a VHDL code, a mobile application, and a web application for monitoring processes of the plant. © 2020 IEEE.
A modeling language is a way to describe syntax, semantic, and constraints needed for creating models. Defining a Domain Specific Modeling Language (DSML) instead of suing a general-purpose one, increases the productivity of the developer as well as the quality of the resulted model. In this paper, we proposed a DSML for the Mitigation phase of Emergency Response Environments (EREs). We extended the TAO framework based on the TAO provided textual patterns. This paper also involves extending MAS-ML to support the modeling of EREs Mitigation phase. To evaluate this work, a case study is modeled with the proposed modeling language. Higher abstraction level, less effort, and faster development process are results of the proposed modeling language. © 2014 IEEE.
Daher, H. ,
Hoseindoost, S. ,
Zamani, B. ,
Fatemi, A. pp. 35-41
In case of a disaster, planning for pedestrian evacuation from buildings is a major issue since it threatens human lives. To cope with this problem, evacuation plans are developed to ensure efficient evacuation in minimum time. These plans can be very sophisticated according to the complexity of the evacuation environment. This advocates the use of architectures such as Multi-Agent Systems (MAS) to develop the evacuation plans before happening of a real accident. Since developing an evacuation plan using MAS requires considerable effort, finding more efficient approaches is still an open problem. This paper introduces a new approach, based on the model-driven principles, to support developing evacuation plans. The approach includes utilizing a graphical editor for designing evacuation models, automatic generation of the evacuation plan code, as well as running the generated code on a MAS platform. We evaluated our approach using a case study. The results show that our approach provides elevated speed, less effort, high abstraction level, and more flexibility and productivity in developing emergency evacuation plans. © 2020 IEEE.
Authorship Attribution (AA) is a task in which a disputed text is automatically assigned to an author chosen from a list of candidate authors. To this end, a model is trained on a dataset of textual documents with known authors, which can be considered as a multi-class single-label classification task. In this paper, we approach this task differently by extending information retrieval techniques to train an AA model. It is based on weighting the AARR technique, presented in our previous study, to relax the value of term frequency. The efficiency of the proposed solution has been evaluated by conducting several experiments on six datasets. The results show the superiority of the proposed solution by improving the accuracy of IMDB, Gutenberg books, Poetry, Blogs, PAN2011, and Twitter datasets by 33%, 31%, 31%, 19%, 6%, and 1%, respectively, where the average improvement is 19.94% over all datasets. The best accuracy over these datasets is 88%, 82%, 67%, 90%, 65%, and 81% in the same respect. In addition, compared to the baseline system, the computation time of the proposed solution has been improved significantly (21.44X) by employing a dictionary-based indexing technique. © 2021 IEEE.
Hemmat, A. ,
Vadaei, K. ,
Shirian, M. ,
Heydari, M.H. ,
Fatemi, A.
This paper introduces an innovative approach to Retrieval-Augmented Generation (RAG) for video question answering (VideoQA) through the development of an adaptive chunking methodology and the creation of a bilingual educational dataset. Our proposed adaptive chunking technique, powered by CLIP embeddings and SSIM scores, identifies meaningful transitions in video content by segmenting educational videos into semantically coherent chunks. This methodology optimizes the processing of slide-based lectures, ensuring efficient integration of visual and textual modalities for downstream RAG tasks. To support this work, we gathered a bilingual dataset comprising Persian and English mid- to long-duration academic videos, curated to reflect diverse topics, teaching styles, and multilingual content. Each video is enriched with synthetic question-answer pairs designed to challenge pure large language models (LLMs) and underscore the necessity of retrieval-augmented systems. The evaluation compares our CLIP-SSIM-based chunking approach against conventional video slicing methods, demonstrating significant improvements across RAGAS metrics, including Answer Relevance, Context Relevance, and Faithfulness. Furthermore, our findings reveal that the multimodal image-text retrieval scenario achieves the best overall performance, emphasizing the importance of integrating complementary modalities. This research establishes a robust framework for video RAG pipelines, expanding the capabilities of multimodal AI systems for educational content analysis and retrieval. © 2025 IEEE.
Due to the growning use of social networks and the use of viral marketing in these networks, finding influential people to maximize information diffusion is considered. This problem is Influence Maximization Problem on social networks. The main goal of this Problem is to select a set of influential nodes to maximize the influence spread in a social network. Researchers in this field have proposed different algorithms, but finding the influential people in the shortest possible time is still a challenge that has attracted the attention of researchers. Therefore, in this paper, the IMPT-C algorithm is presented with a focus on graph pre-processing in order to reduce the search space based on community structure. The approach of this algorithm is to take advantage of the topological properties of the graph to identify influential nodes. The experiment results indicate that the IMPT-C algorithm has a great influence spread with low run time compared the state-of-the-art algorithms consist least 2.36% improve than PHG in term the influence spread. © 2021 IEEE.
The area of agent-oriented methodologies is maturing rapidly and the time has come to begin drawing together the work of various research groups with the aim of developing the next generation of agent-oriented software engineering methodologies. An important step is to understand the differences between the various key methodologies, and to understand each methodology's strengths, weaknesses, and domains of applicability. In this paper we perform an investigation upon user views, on four well-known methodologies. We extend Tropos, as the most complete one up on users view point, by providing a proper supportive tool for it. © 2006 IEEE.
This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches. © 2021 Owner/Author.
The Internet of Things (IoT) enables smart Things to communicate via the Internet. Things are growing in number, and their need for multiple resources in a complementary manner engenders serious problems in resource allocation. Combinatorial Auctions (CA) are the optimal market mechanism for allocating such indivisible bundles. Since the abundance of bundles in the IoT market makes it impossible to bid on all bundles, Things express their preferences on some (and not all) bundles to make the winner determination amenable. We address the winner determination problem by proposing an allocation mechanism based on social choice methods, which operates on the number of requested resources, the number of bundles, the offered price, and the preferred weight of each bundle. These methods include Borda, Copeland, Average without Misery, Least Misery, and Hare. Finally, we demonstrate the evaluation of these methods in terms of execution time and envy-freeness among the Things. © 2023 IEEE.
Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Previous research has predominantly focused on improving the accuracy and quality of retrieved data chunks to enhance the overall performance of the generation pipeline. However, despite ongoing advancements, the critical issue of retrieving irrelevant information—which can impair a model’s ability to utilize its internal knowledge effectively—has received minimal attention. In this work, we investigate the impact of retrieving irrelevant information in open-domain question answering, highlighting its significant detrimental effect on the quality of LLM outputs. To address this challenge, we propose the Context Awareness Gate (CAG) architecture, a novel mechanism that dynamically adjusts the LLM’s input prompt based on whether the user query necessitates external context retrieval. Additionally, we introduce the Vector Candidates method, a core mathematical component of CAG that is statistical, LLM-independent, and highly scalable. We further examine the distributions of relationships between contexts and questions, presenting a statistical analysis of these distributions. This analysis can be leveraged to enhance the context retrieval process in retrieval-augmented generation (RAG) systems. © 2024 IEEE.
For a long time, culture has been an influencing parameter in negotiations. Growth of international trades and business competitions has increased the importance of negotiations among countries and different cultures. Developing new technologies, particularly the use of artificial intelligence in electronic trading areas, has provided us with the application of intelligent agents to resolve challenges in e- negotiations. In this study, a model is developed and implemented to arm intelligent agents with time-sensitivity cultural parameter in negotiations in electronic commerce context. The seller's proposals are offered based on the estimated value of the buyers' time-sensitivity in delivering the products. It starts from the highest price which satisfies the buyer's time sensitivity. The simulations are based on the Salacuse's Cultural dataset related to five countries, Finland, Mexico, Turkey, India, and the United States of America. The negotiation algorithms were implemented in Java platform and MySQL database for both cases of with and without cultural differences in time sensitivity. The evaluation shows that the cultural-based model starts the negotiation from an offer close to the buyer's desire. This yields less number of rounds and total negotiation time period. The simulation results also show that the buyer's budget as an economic factor can be effective in the negotiation outcomes in some cases. © 2014 IEEE.
This paper describes an encryption system for analog signals based on permutation of samples. The scrambling algorithm is based on the permutation of the samples and provides highly secured scrambled signal by permuting a large number of those samples. The algorithm for generation the permutation matrices is explained. Important items to be considered in designing the system are discussed such as choice and construction of permutation matrices, and configuration of the practical scrambling system. C programming language was used for simulation. The results of simulation and tests shows that proposed scrambling achieve extremely high-level security. The method of choice and generation of permutation matrices, Tompkin-Paig algorithm and maximum length shift register are discussed. Simulations of different parts of the system, include scrambler, descrambler and generation of permutation matrices programs are provided. Miscellaneous methods of objective tests are described. Theoretical and simulation results of these tests are also provided. © 2002 IEEE.
The considerable growth of the number of networked devices in the world has led to the development of various and new programs in the field of IoT, which are often limited to the current network infrastructure, on the other hand, force the network administrator to implement complex network policies manually. Due to this congestion of equipment as well as the increasing complexity of traditional network configuration, Software-Defined Networks (SDNs) facilitate network management by separating the control and data layers and creating network rules. For these facilities, these networks appear to be a good infrastructure for IoT networks will enable network programming to develop new and more efficient services to meet real needs. In addition, the variety of IoT equipment can increase complex and inconsistent network rules in SDN-based switches, making network management difficult. Accordingly, in this paper, we will try to model the behavior of anomaly rules distributed in software-defined networks that have been created by different apps in the Internet of Things. It can identify their relationship with other rules in the network and avoid registering them. © 2021 IEEE.
Although high-performance artificial intelligence (AI) models require substantial computational resources, embedded systems are constrained by limited hardware capabilities, such as memory and processing power. On the other hand, embedded systems have a broad range of applications, making the integration of AI and embedded systems a prominent topic in both hardware and AI research. Creating powerful speech embeddings for embedded systems is challenging, as such models, like Wave2Vec, are typically computationally intensive. Additionally, the scarcity of data for many low-resource languages further complicates the development of high-performance models. To address these challenges, we utilized BERT to generate speech embeddings. BERT was selected because, in addition to producing meaningful embeddings, it is trained on numerous low-resource languages and facilitates the design of efficient decoders. This study introduces a compact speech encoder tailored for low-resource languages, capable of functioning as an encoder across a diverse range of speech tasks. To achieve this, we utilized BERT to generate meaningful embeddings. However, due to the high dimensionality of BERT embeddings, which imposes significant computational demands on many embedded systems, we applied dimensionality reduction techniques. The reduced-dimensional vectors were subsequently used as labels for speech data to train a model composed of convolutional neural networks (CNNs) and fully connected layers. Finally, we demonstrated the encoder's effectiveness through an application in speech command recognition. © 2024 IEEE.
Nowadays, Multi-hop wireless networks have achieved lots of attention due to their ease of development, low cost, and other advantages. Wireless channels have broadcast nature, and a sent packet can be heard by the nodes in the sender's transmission range. This feature is used in opportunistic routing to forward the packets and to enhance the network efficiency. In most of the opportunistic routing algorithms, forwarder nodes are pre-selected by the source nodes. Forwarders should be coordinated for the packet forwarding and one of them is finally selected as the next hop. If the forwarder list is large, coordination's computational overhead will be high. A new Energy efficient opportunistic routing algorithm, named EOpR is presented in this paper that selects the candidate nodes on the packets' fly. This selection is based on the region and the nodes' residual energy. Candidate nodes set a timer and the one whose timer expires first is selected as the next hop. Simulation results showed higher network performance in the terms of network's lifetime and also throughput compared to ROMER. The number of duplicate packets also decreases in EOpR. © 2015 IEEE.
Automated negotiating agents are usually designed and implemented in a general way so that they can negotiate successfully in front of a vast variety of opponents. In the real world, most opponents are single-peaked. Gaussian agents that use such distribution function to rate the negotiation items are important sorts of such opponents. Modeling the opponents is of great importance since it enables us to adjust our next decisions accordingly. This can bring us short-time compromises, ideal eventual utility, more satisfaction, and so on. In negotiating with Gaussian opponents, the estimation of the opponent's peak point is the core. In this regard, we have paid particular attention to how accurate the existing automated agents attended in Automated Negotiating Agents Competition (ANAC) during 2010-2019 can model Gaussian bidders and showed the result of the experiments. © 2021 IEEE.
This study focuses on the generation of Persian named entity datasets through the application of machine translation on English datasets. The generated datasets were evaluated by experimenting with one monolingual and one multilingual transformer model. Notably, the CoNLL 2003 dataset has achieved the highest F1 score of 85.11%. In contrast, the WNUT 2017 dataset yielded the lowest F1 score of 40.02%. The results of this study highlight the potential of machine translation in creating high-quality named entity recognition datasets for low-resource languages like Persian. The study compares the performance of these generated datasets with English named entity recognition systems and provides insights into the effectiveness of machine translation for this task. Additionally, this approach could be used to augment data in low-resource language or create noisy data to make named entity systems more robust and improve them. © 2023 IEEE.
The FFT speech encryption algorithm is tested on speech samples which are recorded using a data acquisition system connected to a PC. The speech samples are read from an input file by the simulation program and the scrambling operation performed on them frame-by-frame. The scrambled speech signal is output through the filter card and the DAC card. The scrambled speech samples are also recorded onto an output file on which the descrambling operations are subsequently performed. The algorithms and results of these simulation tests are provided below. An analog I/O card was used with simulation program. A 12-bit ADC and DAC card was used to capture about 4 seconds of speech at the rate of 8 Ksps. The scrambler and descrambler programs written in C processed the speech file. The main parts of the system are (i) scrambler with permutation and (ii) descrambling with depermutation. Some additional parts such as the ADC, the DAC, the IBC (integer to binary convertor) and the BIC are necessary. The basic functions can, thus be identified as follows: 1) Scrambler includes FFT, permutation and IFFT 2) Descrambler includes FFT, depermutation and IFFT 3) Generation of permutation matrices. © 2002 IEEE.
Information-Centric Networking (ICN) is focused on content itself as the key factor of communication instead of network addresses. As a successful nominee for future architecture on the Internet, ICN provides a networking paradigm shift from host-oriented to content-oriented communication. This means that a user can declare its desired content by the unique name of that content irrespective of the hosting location. ICN provides high performance content distribution framework, stronger security solutions, better mobility support and scalable network architecture. It supports different naming schemes encompassing flat, hierarchical, hybrid, and attribute-value names. These properties construct ICN as an appropriate networking infrastructure for IoT applications such as smart city. ICN can better handle large IoT name spaces with lower processing resource usage. It reduces energy consumption by in-network caching of contents. Considering an NDN-based smart city, the available naming schemes can be classified into hybrid and hierarchical names. The disadvantages of the proposed naming schemes can be summarized as the long length of the names in hierarchical approach, the difficulty of finding unique content in attributed-value naming scheme, not being user-friendly in flat naming method, and complexity in hybrid naming structures. Considering these drawbacks, we presented a hybrid name scheme for the smart city by PURSUIT architecture that provides faster name lookup in IoT communications. © 2020 IEEE.
Feedback is an essential component of learning, as it helps students identify their strengths and weaknesses, and improve their performance. However, the students may not be able to understand how their work has been judged. One way to address this issue is to let the students assess and comment on the work of their peers, Peer Assessment (PA). PA has benefits such as enhancing learning outcomes, developing self-assess and critical thinking skills, and fostering collaboration. However, PA also poses some challenges such as ensuring fairness, anonymity, and reliability. In this study, we designed and implemented an anonymous electronic PA for a few classes participating EU-Iran STEM/UNITEL project. We used several digital tools to simulate a double-blind PA process, and a rubric based on the students' own criteria and weights. The grades collected from 70 students represent positive feedback. We discuss the functionality, advantages, and limitations of our approach. © 2024 IEEE.
Delay and capacity (throughput) are two important parameters to route data packets in Mobile Ad Hoc Networks (MANETs). In this paper, a new connectionless routing algorithm has been proposed to overcome the performance limit. The proposed algorithm is an extension of dynamic virtual route (DVR) algorithm. Mobility degree of nodes' neighborhood is used to calculate two mobility metrics. Mobility metrics are utilized to establish a more stable route between source and destination. Simulation study shows that the proposed algorithm can improve the network throughput and decrease average end-to-end delay significantly. © 2014 IEEE.
This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query response systems for university-related question answering. By systematically extracting data from the university’s official website, primarily in Persian, and employing advanced prompt engineering techniques, we generate accurate and contextually relevant responses to user queries. We developed a comprehensive university benchmark, UniversityQuestionBench (UQB), to rigorously evaluate our system’s performance. UQB focuses on Persian-language data, assessing accuracy and reliability through various metrics and real-world scenarios. Our experimental results demonstrate significant improvements in the precision and relevance of generated responses, enhancing user experiences, and reducing the time required to obtain relevant answers. In summary, this paper presents a novel application of RAG pipelines and LLMs for Persian-language data retrieval, supported by a meticulously prepared university benchmark, offering valuable insights into advanced AI techniques for academic data retrieval and setting the stage for future research in this domain. © 2024 IEEE.
The 13th automated negotiation competition was held in 2 leagues (ANL2022 and SCML2022) in conjunction with the 31st IJCAI conference. The ANL for 2022 is bilateral negotiation under the SOAP protocol. Agents are allowed to learn from their previous negotiations. The agents could have 3 main BOA components: a Bidding strategy that decides which bid and when must be sent to the opponent, an Opponent model that tries to model the opponent's preferences, and an Acceptance strategy that decides whether to accept the opponent's offer or not. This paper explains our LuckyAgent2022's BOA components and its learning methods over negotiation sessions. To improve its utility over sessions, we propose SLM, a LSN Stop-Learning mechanism, to prevent overfitting by adapting it to a multi-armed bandit problem. It finds the best value for variables of a time-dependent bidding strategy for the opponent. © 2022 IEEE.
Wireless applications have become significant in numerous fields [1] such as the auto industry. Indeed, the convergence of telecommunication, computation, wireless technology, and transportation technologies has contributed to the facilitation of our roads and highways as far as communications are concerned. This convergence in a sense is considered as a platform in intelligent transportation systems (ITS) where each vehicle is assumed to be equipped with devices as nodes in order to create contact with other nodes. Mobile ad hoc networks (MANETs) were introduced in Chapter 3. Because the features of a vehicle network are different from those of other types of MANETs, this network is called a vehicular ad hoc network (VANET) [2]. © 2017 by Taylor & Francis Group, LLC.
DASH, or Dynamic Adaptive Streaming over HTTP, relies on a rate adaptation component to decide on which representation to download for each video segment. A plethora of rate adaptation algorithms has been proposed in recent years. The decisions of which bitrate to download made by these algorithms largely depend on several factors: estimated network throughput, buffer occupancy, and buffer capacity. Yet, these algorithms are not informed by a fundamental relationship between these factors and the chosen bitrate, and as a result, we found that they do not perform consistently in all scenarios, and require parameter tuning to work well under different buffer capacity. In this paper, we model a DASH client as an M/D/l/K queue, which allows us to calculate the expected buffer occupancy given a bitrate choice, network throughput, and buffer capacity. Using this model, we propose QUETRA, a simple rate adaptation algorithm. We evaluated QUETRA under a diverse set of scenarios and found that, despite its simplicity, it leads to better quality of experience (7% - 140%) than existing algorithms. © 2017 Association for Computing Machinery.
Fradet, Pascal ,
Girault, Alain ,
Krishnaswamy, Ruby ,
Nicollin, Xavier ,
Shafiei, A.
Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF (and most of its variants) lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment.We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and outline its implementation. © 2019 EDAA.
Scan design is a powerful Design-for-Testability (DFT) technique that enhances controllability and observability of internal nodes of the circuit under test. However, it can increase system vulnerability being a back door to access secret information of a secure chip. In this paper, we present a scan-based design which is robust against scan-based side channel attacks. We use SHA256 secure hash and Blum Blum Shub pseudo random number generator to create a simple challenge/response scheme. The system can be used to enable JTAG instructions for authorized user or control access to IEEE 1687 on-chip instruments. The effectiveness of the proposed method has been verified using NIST statistical test suite. © 2016 IEEE.
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