Super-linear speedup for real-time condition monitoring using image processing and drones

Real-time inspections for the large-scale solar system may take a long time to get the hazard situations for any failures that may take place in the solar panels normal operations, where prior hazards detection is important. Reducing the execution time and improving the system’s performance are the ultimate goals of multiprocessing or multicore systems. Real-time video processing and analysis from two camcorders, thermal and charge-coupling devices (CCD), mounted on a drone compose the embedded system being proposed for solar panels inspection. The inspection method needs more time for capturing and processing the frames and detecting the faulty panels. The system can determine the longitude and latitude of the defect position information in real-time. In this work, we investigate parallel processing for the image processing operations which reduces the processing time for the inspection systems. The results show a super-linear speedup for real-time condition monitoring in large-scale solar systems. Using the multiprocessing module in Python, we execute fault detection algorithms using streamed frames from both video cameras. The experimental results show a super-linear speedup for thermal and CCD video processing, the execution time is efficiently reduced with an average of 3.1 times and 6.3 times using 2 processes and 4 processes respectively.

Resilient Team Formation with Stabilisability of Agent Networks for Task Allocation

Team formation (TF) faces the problem of defining teams of agents able to accomplish a set of tasks. Resilience on TF problems aims to provide robustness and adaptability to unforeseen events involving agent deletion. However, agents are unaware of the inherent social welfare in these teams. This article tackles the problem of how teams can minimise their effort in terms of organisation and communication considering these dynamics. Our main contribution is twofold: first, we introduce the Stabilisable Team Formation (STF) as a generalisation of current resilient TF model, where a team is stabilisable if it possesses and preserves its inter-agent organisation from a graph-based perspective. Second, our experiments show that stabilisability is able to reduce the exponential execution time in several units of magnitude with the most restrictive configurations, proving that communication effort in subsequent task allocation problems are relaxed compared with current resilient teams. To do so, we developed SBB-ST, a branch-and-bound algorithm based on Distributed Constrained Optimisation Problems (DCOP) to compute teams. Results evidence that STF improves their predecessors, extends the resilience to subsequent task allocation problems represented as DCOP, and evidence how Stabilisability contributes to resilient TF problems by anticipating decisions for saving resources and minimising the effort on team organisation in dynamic scenarios.

On Time-sensitive Control Dependencies

We present efficient algorithms for time-sensitive control dependencies (CDs). If statement y is time-sensitively control dependent on statement x , then x decides not only whether y is executed but also how many timesteps after x . If y is not standard control dependent on x , but time-sensitively control dependent, then y will always be executed after x , but the execution time between x and y varies. This allows us to discover, e.g., timing leaks in security-critical software. We systematically develop properties and algorithms for time-sensitive CDs, as well as for nontermination-sensitive CDs. These work not only for standard control flow graphs (CFGs) but also for CFGs lacking a unique exit node (e.g., reactive systems). We show that Cytron’s efficient algorithm for dominance frontiers [ 10 ] can be generalized to allow efficient computation not just of classical CDs but also of time-sensitive and nontermination-sensitive CDs. We then use time-sensitive CDs and time-sensitive slicing to discover cache timing leaks in an AES implementation. Performance measurements demonstrate scalability of the approach.

An analysis between exact and approximate algorithms for the k-center problem in graphs

<span lang="EN-US">This research focuses on the k-center problem and its applications. Different methods for solving this problem are analyzed. The implementations of an exact algorithm and of an approximate algorithm are presented. The source code and the computation complexity of these algorithms are presented and analyzed. The multitasking mode of the operating system is taken into account considering the execution time of the algorithms. The results show that the approximate algorithm finds solutions that are not worse than two times optimal. In some case these solutions are very close to the optimal solutions, but this is true only for graphs with a smaller number of nodes. As the number of nodes in the graph increases (respectively the number of edges increases), the approximate solutions deviate from the optimal ones, but remain acceptable. These results give reason to conclude that for graphs with a small number of nodes the approximate algorithm finds comparable solutions with those founds by the exact algorithm.</span>

How Software Refactoring Impacts Execution Time

Refactoring aims at improving the maintainability of source code without modifying its external behavior. Previous works proposed approaches to recommend refactoring solutions to software developers. The generation of the recommended solutions is guided by metrics acting as proxy for maintainability (e.g., number of code smells removed by the recommended solution). These approaches ignore the impact of the recommended refactorings on other non-functional requirements, such as performance, energy consumption, and so forth. Little is known about the impact of refactoring operations on non-functional requirements other than maintainability. We aim to fill this gap by presenting the largest study to date to investigate the impact of refactoring on software performance, in terms of execution time. We mined the change history of 20 systems that defined performance benchmarks in their repositories, with the goal of identifying commits in which developers implemented refactoring operations impacting code components that are exercised by the performance benchmarks. Through a quantitative and qualitative analysis, we show that refactoring operations can significantly impact the execution time. Indeed, none of the investigated refactoring types can be considered “safe” in ensuring no performance regression. Refactoring types aimed at decomposing complex code entities (e.g., Extract Class/Interface, Extract Method) have higher chances of triggering performance degradation, suggesting their careful consideration when refactoring performance-critical code.

Design, Implementation & Performance of Vedic Multiplier Based on Look Ahead Carry Adder for Different Bit Lengths

This paper proposed the layout of Vedic Multiplier based totally on Urdhva Trigbhyam approach of multiplication. It is most effective Vedic sutras for multiplication. Urdhva triyagbhyam is a vertical and crosswise approach to discover product of two numbers. Multiplication is an essential quintessential feature in arithmetic logic operation. Computational overall performance of a DSP device is limited via its multiplication overall performance and since, multiplication dominates the execution time of most DSP algorithms. Multiplication is one of the simple arithmetic operations and it requires extensively extra hardware assets and processing time than addition and subtraction. Our work is to compare different bit Vedic multiplier structure using carry look ahead adder technique. Keywords: Carry Look Ahead Adder, Urdhva Trigbhyam, DSP algorithms, Vedic Multiplier

L 2 C: Combining Lossy and Lossless Compression on Memory and I/O

In this article, we introduce L 2 C, a hybrid lossy/lossless compression scheme applicable both to the memory subsystem and I/O traffic of a processor chip. L 2 C employs general-purpose lossless compression and combines it with state-of-the-art lossy compression to achieve compression ratios up to 16:1 and to improve the utilization of chip’s bandwidth resources. Compressing memory traffic yields lower memory access time, improving system performance, and energy efficiency. Compressing I/O traffic offers several benefits for resource-constrained systems, including more efficient storage and networking. We evaluate L 2 C as a memory compressor in simulation with a set of approximation-tolerant applications. L 2 C improves baseline execution time by an average of 50% and total system energy consumption by 16%. Compared to the lossy and lossless current state-of-the-art memory compression approaches, L 2 C improves execution time by 9% and 26%, respectively, and reduces system energy costs by 3% and 5%, respectively. I/O compression efficacy is evaluated using a set of real-life datasets. L 2 C achieves compression ratios of up to 10.4:1 for a single dataset and on average about 4:1, while introducing no more than 0.4% error.

An Efficient Execution Framework of Two-Part Execution Scenario Analysis

Response Time Analysis ( RTA ) is an important and promising technique for analyzing the schedulability of real-time tasks under both Global Fixed-Priority ( G-FP ) scheduling and Global Earliest Deadline First ( G-EDF ) scheduling. Most existing RTA methods for tasks under global scheduling are dominated by partitioned scheduling, due to the pessimism of the -based interference calculation where is the number of processors. Two-part execution scenario is an effective technique that addresses this pessimism at the cost of efficiency. The major idea of two-part execution scenario is to calculate a more accurate upper bound of the interference by dividing the execution of the target job into two parts and calculating the interference on the target job in each part. This article proposes a novel RTA execution framework that improves two-part execution scenario by reducing some unnecessary calculation, without sacrificing accuracy of the schedulability test. The key observation is that, after the division of the execution of the target job, two-part execution scenario enumerates all possible execution time of the target job in the first part for calculating the final Worst-Case Response Time ( WCRT ). However, only some special execution time can cause the final result. A set of experiments is conducted to test the performance of the proposed execution framework and the result shows that the proposed execution framework can improve the efficiency of two-part execution scenario analysis by up to in terms of the execution time.

Tries-Based Parallel Solutions for Generating Perfect Crosswords Grids

A general crossword grid generation is considered an NP-complete problem and theoretically it could be a good candidate to be used by cryptography algorithms. In this article, we propose a new algorithm for generating perfect crosswords grids (with no black boxes) that relies on using tries data structures, which are very important for reducing the time for finding the solutions, and offers good opportunity for parallelisation, too. The algorithm uses a special tries representation and it is very efficient, but through parallelisation the performance is improved to a level that allows the solution to be obtained extremely fast. The experiments were conducted using a dictionary of almost 700,000 words, and the solutions were obtained using the parallelised version with an execution time in the order of minutes. We demonstrate here that finding a perfect crossword grid could be solved faster than has been estimated before, if we use tries as supporting data structures together with parallelisation. Still, if the size of the dictionary is increased by a lot (e.g., considering a set of dictionaries for different languages—not only for one), or through a generalisation to a 3D space or multidimensional spaces, then the problem still could be investigated for a possible usage in cryptography.

Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation

This paper aims at ensuring an efficient recommendation. It proposes a new context-aware semantic-based probabilistic situations injection and adaptation using an ontology approach and Bayesian-classifier. The idea is to predict the relevant situations for recommending the right services. Indeed, situations are correlated with the user’s context. It can, therefore, be considered in designing a recommendation approach to enhance the relevancy by reducing the execution time. The proposed solution in which four probability-based-context rule situation items (user’s location and time, user’s role, their preferences and experiences) are chosen as inputs to predict user’s situations. Subsequently, the weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user’s situations. Three context parameters (CPU speed, sensor availability and RAM size) of the current devices are used to ensure adaptive service recommendation. Experimental results show that the proposed approach enhances accuracy rate with a high number of situations rules. A comparison with existing recommendation approaches shows that the proposed approach is more efficient and decreases the execution time.