| 1. |
- Ashraf, Adnan, et al.
(författare)
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Online Path Generation and Navigation for Swarms of UAVs
- 2020
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Ingår i: Scientific Programming. - : HINDAWI LTD. - 1058-9244 .- 1875-919X. ; 2020
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Tidskriftsartikel (refereegranskat)abstract
- With the growing popularity of unmanned aerial vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation cannot be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the UAVs and of the successfully detected, static, and moving obstacles to predict and avoid the following: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-to-moving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and complex event processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid predicted collisions in order to ensure safety of the entire swarm. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments and compare our results with the results of four existing approaches. The results show that the proposed system successfully predicts and avoids all three kinds of collisions in an online manner. Moreover, it generates safe and efficient UAV routes, efficiently scales to large-sized problem instances, and is suitable for cluttered flying zones and for scenarios involving high risks of UAV collisions.
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| 2. |
- Crnkovic, Ivica, 1955, et al.
(författare)
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Preface
- 2016
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 9823 LNCS, s. V-VI
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Dongol, B., et al.
(författare)
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Introduction to the Special Section on iFM 2020
- 2022
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Ingår i: Formal Aspects of Computing. - : Association for Computing Machinery (ACM). - 0934-5043 .- 1433-299X. ; 34:1
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Dongol, B., et al.
(författare)
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Preface
- 2020
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - : Springer Science and Business Media Deutschland GmbH.
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Konferensbidrag (refereegranskat)
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| 5. |
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| 6. |
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| 7. |
- Kunnappilly, Ashalatha
(författare)
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Formally Assured Intelligent Systems for Enhanced Ambient Assisted Living Support
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Ambient Assisted Living (AAL) solutions are aimed to assist the elderly in their independent and safe living. During the last decade, the AAL field has witnessed a significant development due to advancements in Information and Communication Technologies, Ubiquitous Computing and Internet of Things. However, a closer look at the existing AAL solutions shows that these improvements are used mostly to deliver one or a few functions mainly of the same type (e.g. health monitoring functions). There are comparatively fewer initiatives that integrate different kinds of AAL functionalities, such as fall detection, reminders, fire alarms, etc., besides health monitoring, into a common framework, with intelligent decision-making that can thereby offer enhanced reasoning by combining multiple events. To address this shortage, in this thesis, we propose two different categories of AAL architecture frameworks onto which different functionalities, chosen based on user preferences, can be integrated. One of them follows a centralized approach, using an intelligent Decision Support System (DSS), and the other, follows a truly distributed approach, involving multiple intelligent agents. The centralized architecture is our initial choice, due to its ease of development by combining multiple functionalities with a centralized DSS that can assess the dependency between multiple events in real time. While easy to develop, our centralized solution suffers from the well-known single point of failure, which we remove by adding a redundant DSS. Nevertheless, the scalability, flexibility, multiple user accesses, and potential self-healing capability of the centralized solution are hard to achieve, therefore we also propose a distributed, agent-based architecture as a second solution, to provide the community with two different AAL solutions that can be applied depending on needs and available resources. Both solutions are to be used in safety-critical applications, therefore their design-time assurance, that is, providing a guarantee that they meet functional requirements and deliver the needed quality-of-service, is beneficial. Our first solution is a generic architecture that follows the design of many commercial AAL solutions with sensors, a data collector, DSS, security and privacy, database (DB) systems, user interfaces (UI), and cloud computing support. We represent this architecture in the Architecture Analysis and Design Language (AADL) via a set of component patterns that we propose. The advantage of using patterns is that they are easily re-usable when building specific AAL architectures. Our patterns describe the behavior of the components in the Behavioral Annex of AADL, and the error behavior in AADL's Error Annex. We also show various instantiations of our generic model that can be developed based on user requirements. To formally assure these solutions against functional, timing and reliability requirements, we show how we can employ exhaustive model checking using the state-of-art model checker, UPPAAL, and also statistical model-checking techniques with UPPAAL SMC, an extension of the UPPAAL model checker for stochastic systems, which can be employed in cases when exhaustive verification does not scale. The second proposed architecture is an agent-based architecture for AAL systems, where agents are intelligent entities capable of communicating with each other in order to decide on an action to take. Therefore, the decision support is now distributed among agents and can be used by multiple users distributed across multiple locations. Due to the fact that this solution requires describing agents and their interaction, the existing core AADL does not suffice as an architectural framework. Hence, we propose an extension to the core AADL language - The Agent Annex, with formal semantics as Stochastic Transition Systems, which allows us to specify probabilistic, non-deterministic and real-time AAL system behaviors. In order to formally assure our multi-agent system, we employ the state-of-art probabilistic model checker PRISM, which allows us to perform probabilistic yet exhaustive verification. As a final contribution, we also present a small-scale validation of an architecture of the first category, with end users from three countries (Romania, Poland, Denmark). This work has been carried out with partners from the mentioned countries. Our work in this thesis paves the way towards the development of user-centered, intelligent ambient assisted living solutions with ensured quality of service.
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| 8. |
- Loni, Mohammad, et al.
(författare)
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Designing compact convolutional neural network for embedded stereo vision systems
- 2018
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Ingår i: Proceedings - 2018 IEEE 12th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538666890 ; , s. 244-251
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Konferensbidrag (refereegranskat)abstract
- Autonomous systems are used in a wide range of domains from indoor utensils to autonomous robot surgeries and self-driving cars. Stereo vision cameras probably are the most flexible sensing way in these systems since they can extract depth, luminance, color, and shape information. However, stereo vision based applications suffer from huge image sizes and computational complexity leading system to higher power consumption. To tackle these challenges, in the first step, GIMME2 stereo vision system [1] is employed. GIMME2 is a high-throughput and cost efficient FPGA-based stereo vision embedded system. In the next step, we present a framework for designing an optimized Deep Convolutional Neural Network (DCNN) for time constraint applications and/or limited resource budget platforms. Our framework tries to automatically generate a highly robust DCNN architecture for image data receiving from stereo vision cameras. Our proposed framework takes advantage of a multi-objective evolutionary optimization approach to design a near-optimal network architecture for both the accuracy and network size objectives. Unlike recent works aiming to generate a highly accurate network, we also considered the network size parameters to build a highly compact architecture. After designing a robust network, our proposed framework maps generated network on a multi/many core heterogeneous System-on-Chip (SoC). In addition, we have integrated our framework to the GIMME2 processing pipeline such that it can also estimate the distance of detected objects. The generated network by our framework offers up to 24x compression rate while losing only 5% accuracy compare to the best result on the CIFAR-10 dataset.
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| 9. |
- Majd, Amin, et al.
(författare)
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A Cloud Based Super-Optimization Method to Parallelize the Sequential Code's Nested Loops
- 2019
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Ingår i: Proceedings 2019 IEEE 13th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSOC 2019). - : IEEE COMPUTER SOC. - 9781728148823 ; , s. 281-287
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Konferensbidrag (refereegranskat)abstract
- Advances in hardware architecture regarding multi-core processors make parallel computing ubiquitous. To achieve the maximum utilization of multi-core processors, parallel programming techniques are required. However, there are several challenges standing in front of parallel programming. These problems are mainly divided into three major groups. First, although recent advancements in parallel programming languages (e.g. MPI, OpenCL, etc.) assist developers, still parallel programming is not desirable for most programmers. The second one belongs to the massive volume of old software and applications, which have been written in serial mode. However, converting millions of line of serial codes to parallel codes is highly time-consuming and requiring huge verification effort. Third, the production of software and applications in parallel mode is very expensive since it needs knowledge and expertise. Super-optimization provided by super compilers is the process of automatically determine the dependent and independent instructions to find any data dependency and loop-free sequence of instructions. Super compiler then runs these instructions on different processors in the parallel mode, if it is possible. Super-optimization is a feasible solution for helping the programmer to get relaxed from parallel programming workload. Since the most complexity of the sequential codes is in the nested loops, we try to parallelize the nested loops by using the idea of super-optimization. One of the underlying stages in the super-optimization is scheduling tiled space for iterating nested loops. Since the problem is NP-Hard, using the traditional optimization methods are not feasible. In this paper, we propose a cloud-based super-optimization method as Software-as-a-Service (SaaS) to reduce the cost of parallel programming. In addition, it increases the utilization of the processing capacity of the multi-core processor. As the result, an intermediate programmer can use the whole processing capacity of his/her system without knowing anything about writing parallel codes or super compiler functions by sending the serial code to a cloud server and receiving the parallel version of the code from the cloud server. In this paper, an evolutionary algorithm is leveraged to solve the scheduling problem of tiles. Our proposed super-optimization method will serve as software and provided as a hybrid (public and private) deployment model.
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| 10. |
- Majd, Amin, et al.
(författare)
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NOMeS : Near-Optimal Metaheuristic Scheduling for MPSoCs
- 2017
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Ingår i: 2017 19TH INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND DIGITAL SYSTEMS (CADS). - : IEEE. - 9781538643792 ; , s. 70-75
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Konferensbidrag (refereegranskat)abstract
- The task scheduling problem for Multiprocessor System-on-Chips (MPSoC), which plays a vital role in performance, is an NP-hard problem. Exploring the whole search space in order to find the optimal solution is not time efficient, thus metaheuristics are mostly used to find a near-optimal solution in a reasonable amount of time. We propose a novel metaheuristic method for near-optimal scheduling that can provide performance guarantees for multiple applications implemented on a shared platform. Applications are represented as directed acyclic task graphs (DAG) and are executed on an MPSoC platform with given communication costs. We introduce a novel multi-population method inspired by both genetic and imperialist competitive algorithms. It is specialized for the scheduling problem with the goal to improve the convergence policy and selection pressure. The potential of the approach is demonstrated by experiments using a Sobel filter, a SUSAN filter, RASTA-PLP and JPEG encoder as real-world case studies.
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