Fastpass: fast timing path search for generalized timing exception handling

This paper presents the timing analysis methodology developed in the European project P-SOCRATES (Parallel Software Framework for Time-Critical Many-core Systems). This timing analysis methodology is defined for parallel applications that must satisfy both performance and real-time requirements and are executed on modern many-core processor architectures. We discuss the motivation and objectives of the project, the timing analysis flow that we proposed, the tool that has been developed to automatize it, and finally we report on some of the preliminary results that we have obtained when applying this methodology to the three application use-cases of the project.

The fastest route search is a problem to find routes having a relatively small or empty number of congestion or density so that the required travel time is faster to get to a location. One of them is the route to the tourist attraction in Jember regency, East Java has many relatively solid lines. Some researchers have done a lot of research on finding the shortest path using Ant Colony System (ACS) method. However, the ACS method has a weakness, where more ants pass through a path, the clearer the footprint but the volume of ants passing through the path is also longer. Therefore in this research use ant adaptive on ACS method to find the fastest path to the tourist location in Jember Regency. Due to the increasing number of densities, it takes longer to get to the food source. In this study, ant adaptive for ACS method is used to determine the optimal number of ants in searching for the fastest path to the tourist attraction in Jember District, East Java. Various trials are done to prevent the search process of resolving the already traced solution space to find the fastest path. The fastest path does not have to have the shortest path but has the fastest time with the paths having relatively little or no density at all. The results showed that ant adaptive for ACS method developed in this research is able to find the optimal number of ants in determining the fastest paths to the tourist object so that the search result of the fastest route of tourist attraction in Jember Regency in accordance with the reality of the passable path and has a density relatively small or almost nonexistent. The success rate was conducted on 8 tourist objects in Jember with 98 paths tested using road data in Jember regency, East Java with the fastest path solution influenced by the closer to the optimum distance, the more number of ants influenced.

The Rapidly-exploring Random Tree Star (RRT*) algorithm is widely applied in robotic manipulator path planning, yet it does not directly consider motion control, where abrupt changes may cause shocks and vibrations, reducing accuracy and stability. To overcome this limitation, this paper proposes the Kinematically Smoothed, dynamically Biased Bidirectional Potential-guided RRT* (KSBB-P-RRT*) algorithm, which unifies path planning and motion control and introduces three main innovations. First, a fast path search strategy on the basis of Bi-RRT* integrates adaptive sampling and steering to accelerate exploration and improve efficiency. Second, a triangle-inequality-based optimization reduces redundant waypoints and lowers path cost. Third, a kinematically constrained smoothing strategy adapts a Jerk-Continuous S-Curve scheme to generate smooth and executable trajectories, thereby integrating path planning with motion control. Simulations in four environments show that KSBB-P-RRT* achieves at least 30% reduction in planning time and at least 3% reduction in path cost, while also requiring fewer iterations compared with Bi-RRT*, confirming its effectiveness and suitability for complex and precision-demanding applications such as agricultural robotics.

Foraging mammals exhibit a familiar yet poorly characterized phenomenon, ‘alternation’, a pause to sniff in the air preceded by the animal rearing on its hind legs or raising its head. Rodents spontaneously alternate in the presence of airflow, suggesting that alternation serves an important role during plume-tracking. To test this hypothesis, we combine fully resolved simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We show that an agent trained to minimize search time in a realistic odor plume exhibits extensive alternation together with the characteristic cast-and-surge behavior observed in insects. Alternation is linked with casting and occurs more frequently far downwind of the source, where the likelihood of detecting airborne cues is higher relative to ground cues. Casting and alternation emerge as complementary tools for effective exploration with sparse cues. A model based on marginal value theory captures the interplay between casting, surging, and alternation.

The centralized methods for connection allocation in a circuit-switched network-on-chip (NoC) based on time-division multiplexing (TDM) may pose serious performance and scalability issues in large-scale networks due to the 1) limited path search speed, 2) increasing allocation request rate at central unit and 3) the increasing communication cost between the central unit and NoC nodes. This paper tackles this problem by proposing a combined centralized-distributed approach that splits the original NoC into multiple non-overlapping logical partitions, each of them served by a dedicated NoC-Manager unit. The NoC-Manager employs fast trellis-search shortest path algorithm enabling local path search inside the associated NoC partition, while a set of NoC-Managers jointly combine the partial results in a distributed manner in order to find the most likely global path. This approach attempts to combine the benefits of distributed and centralized systems, whilst the experimental results demonstrate its high potential regarding performance and scalability improvement.

Fast shortest path search between two points on a road network is essential demand in location based services (LBS). For this purpose, several types of road network distance materialization methods have been studied. The distance materialization approach is quite fast, however, it results in a huge amount of data. This paper proposes a shortest path search algorithm based on materialized-path-view constructed only on partitioned subgraphs, and its three variations referring different levels of distance materialization. The amount of pre-computed data is greatly reduced. The shortest path is retrieved by a best-first-search using a priority queue. The difference between three variations of the algorithm is the materialization level of the distance in the subgraphs. The performance of them is evaluated comparing with A* algorithm and HEPV experimentally. Through the results, we show the proposed algorithm outperforms the conventional methods.

In this presentation, the adaptation of a tactical decision aid (TDA) designed for Anti Submarine Warfare (ASW) to one used for Mine Warfare is discussed. Previous work at the Naval Research Laboratory established a TDA, namely GRASP for the ASW role. Recently at NRL work has been done to extend the TDA model to Mine Warfare. Specifically this presentation examines search path algorithms for the two TDAs. Comparisons will be shown between various search path algorithms for environmentally influenced mine detection: GRASP, edge covering, and greedy algorithms for limited time searches and searches for assured threshold confidence of detection. Oceanic environmental parameters relevant to designing tactical decision aids for mine detection will be noted. In conclusion this presentation discusses how extensively detailed must the environmental survey be in order to make search path analysis sensitive to the data.

Intelligent autonomous mobile robots find diverse applications across various fields. This article delves into the optimization of path search planning and implementation for these robots in specific scenarios. The study enhances the traditional right-hand principle's search strategy, integrating a forward-looking approach grounded in the goal-oriented law with A* algorithm path planning. Emphasizing the algorithm's lightweight and high efficiency, the study also considers its interaction with hardware systems. The real-world application scenario is abstracted into a two-dimensional planar environment. Collaborative efforts between hardware and software systems result in a comprehensive robot system, validated through tests ensuring swift navigation from the starting point to the endpoint along the planned path.

This research paper in its entirety tries to articulate the various types of challenges faced by the Indian Transportation Sector in implementing new age technologies for better control and visibility throughout the entire Supply Chain. It mostly focuses on the road and rail mode of transportation, though briefly it also touch-bases upon the water and air modes. Indian railways and roads position themselves as the third and second largest network respectively worldwide. In addition to a set of questionnaires circulated among individuals working in the logistics domain to capture their point of view, a thorough literature review of the previous works in this field was done to identify the causes – both external and internal to an organization, which affects implementation of digital solutions to present transportation problems. In this paper, we discuss along with others, two of the greatest challenges posed to the digital transformation – firstly, adamant attitude of traditional players operating in this space, who have for long, functioned through traditional processes and legacy systems and secondly, the complexity of the road and rail network running through the length and breadth of the country. Though the average time taken by enterprises to digitize their transportation business has reduced tremendously due to need of remote operations in these unprecedented times during COVID-19, but still there exist some of the challenges which need to be addressed. Some of the major drivers of identifying these challenges are the need to - reduce lead times by setting up a platform/control tower which can automate the management of transportation with minimized touch points between organizations and their customers, need to reduce risk of manipulated data by minimizing human intervention and need to increase customer satisfaction by incorporating functionalities like real-time tracking, automated exception handling and other. The challenges which were identified in this study can be further analysed to improve on it and conceptualize a framework for transforming Indian logistics (majorly the transportation space). This paper is written from the point of view of current scenario of Indian transportation industry.

We propose a system for a regular updating of land-cover maps based on the use of temporal series of remote sensing images. Such a system is composed of an ensemble of partially unsupervised classifiers integrated in a multiple classifier architecture. The updating problem is formulated under the complex constraint that for some images of the considered multitemporal series no ground-truth information is available. With respect to the authors’ previous works on this topic [1–3], the novel contribution of this paper consists in: i) developing partially unsupervised classification algorithms defined in the framework of a cascade-classifier approach; ii) defining a specific strategy for the generation of an ensemble of classifiers, which exploits the peculiarities of the cascade-classifier approach. These novel aspects result in the definition of more robust and accurate classification systems.KeywordsRadial Basis FunctionRadial Basis Function Neural NetworkMultispectral ImageUnsupervised ClassificationMultiple Classifier SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Event-driven systems maintain an ongoing dialog with their environment. Examples include: distributed programs, where each process reacts to received messages by performing computation and sending messages to peers; graphical user interfaces (GUI), where the interface software and the underlying application react to user inputs and web-based applications, where presentation, business logic and storage tier functionality react to user inputs and response from other web-based services. Such applications are difficult to test because the set of possible interaction sequences between the system and its environment can be very large and governed by complex constraints. The exhaustive nature of software model checking techniques offers hope for effectively validating such systems, however, they only work for closed systems. Previously, the authors developed the Bandera Environment Generator, which given an open system, called a unit under analysis, closes it with a model of its environment. The authors' previous work on environment generation has focused on developing broadly applicable mechanisms for modelling environment behaviour. The generality of this approach often makes it difficult to produce environment models that enable precise and efficient system analysis. The authors' experience shows that by exploiting information about the application domain, generated environments can be made both more precise and more efficient for model checking. This study presents the concept of domain-specific environment generation, details techniques for customising environment generation for the domain of event-driven software, and assesses those techniques on the domains of GUI and web-based applications.

Reusability of basic building blocks is an important and effective goal in ASIC design: it speeds up the design, it minimizes the overall bug probability, it eases documentation and maintenance of core functions. Nevertheless, this approach means also a black box management, requiring some extra efforts in the implementation phase. Alcatel and Toshiba proved a solution to cope with the lack of knowledge and control of re-used blocks. This methodology seems to be effective, especially for million gates designs in deep submicron technologies. This paper discusses a real ASIC design case, where classical issues were combined: tough schedule; large glue logic (800 K netlist gates), with high connectivity; many clock domains (65 clocks), with many interactions; 50% reuse of old building blocks, many from netlist. The proposed flow improves by shortening design time, achieving high synthesis efficiency without usage of time consuming CRWC methodology, achieving high layout efficiency bypassing time consuming (and sometimes misleading) floorplanning /hierarchical methodologies.

To solve path planning problem of continuous space-time Markov moving targets for UUV search, an optimal path planning model is established. The search direction of the UUV is set as decision variables. An improved genetic algorithm is adopted to pursue an optimal path for underwater anti-submarine search. The algorithm utilizes an improved real number encoding method to describe the path. The target’s motion is assumed as uniform distribution in direction and normal distribution in velocity around an initial speed. The results show that the search path planning is more reasonable through a certain number of genetic and cross mutation operations. The proposed method has the advantages of high search efficiency, good stability and short reaction period, and is suitable for solving underwater path-searching problems.

Clustering by fast search and find of density peaks (CFSFDP) is a novel density-based fast clustering method, which has been widely studied and applied in many fields. However, when the sample size of data is too large, the algorithm is inefficient, since it consumes a lot of time and storage space. To solve the above problem, a simple random sampling (SRS) method is provided to speed up the optimized CFSFDP algorithm for real data with large sample size. The rate of correct classification of the sample is defined to measure its clustering performance, and we call it as sampling accuracy. We first use SRS method to generate small samples for cluster analysis. Then, we explore the relationship between the sampling rate and the sampling accuracy. Finally, in order to determine the best sample size that can achieve high sampling accuracy with high efficiency, the mean and standard deviation of the sampling accuracy are adopted as two criteria, and the best sample size is determined based on them. A real case study is given to show the implementation and effectiveness of the proposed method.

Immersed boundary method for high-order flux reconstruction based on volume penalization