As embedded systems are required to satisfy increasing functional and non-functional requirements, heterogeneous systems-on-chip architectures are progressively adopted. While these complex systems-on-chip deliver high performance, they require efficient coordination of the tasks they carry out. To tackle this challenge, designers often resort to runtime mechanisms allowing the dynamic alignment of application requirements with platform services. In turn, runtime mechanisms require the adoption of on-chip monitoring systems. The integration of on-chip monitoring systems into a system-on-chip results in a monitored system-on-chip. Notwithstanding, this integration risks driving a re-design and a re-implementation of the whole system-on-chip, potentially driving to a time-to-market deadline miss. In the literature, HW/SW co-design approaches for monitored systems-on-chip have been proposed to overcome the problem. However, the existing HW/SW co-design approaches prevent performing a system-level design-space exploration that involves all the monitoring requirements, and they also prevent adequate reuse of existing on-chip monitoring systems. This article proposes an approach for efficient HW/SW co-design of monitored systems-on-chip, aiming to comprehensively capture all monitoring requirements at the system-level and to perform a system-level design-space exploration to satisfy them, enforcing the reuse of existing on-chip monitoring systems. The proposed approach is validated through two experimental activities, which demonstrate a 24% reduction in total development time for a monitored system-on-chip implemented on FPGA, compared to the customary approach. The results also show that the approach reduces the risk of missing time-to-market deadlines, supports flexible design choices, and enables the reuse of on-chip monitoring systems.

Electronic Design Automation (EDA) is a crucial research area related to the development of electronic systems. In particular, High-Level Synthesis (HLS) simplifies HW design by automatically translating C/C++/System C specifications into HW description languages. However, HLS for large systems can be time-consuming. In recent years, Machine Learning (ML) has emerged as a prominent topic in EDA, with numerous studies demonstrating its potential to enhance EDA methods covering nearly all phases of the HW design flow. In such a context, this work presents an approach and related frameworks to collect datasets (i.e., SLIDE-x) useful for performing HLS timing and resource estimation through ML techniques (i.e., SLIDE-x-ML), introducing a data-driven component for feature creation that enhances predictions through various input representations and ML methods.

The objective of the FRACTAL project is to create a novel approach to reliable edge computing. The FRACTAL computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The node will also have the capability of learning how to improve its performance against the uncertainty of the environment. In such a context, this paper presents in detail one of the key use cases: an Internet-of-Things solution, represented by intelligent totems for advertisement and wayfinding services, within advanced ICT-based shopping malls conceived as a sentient space. The paper outlines the reference scenario and provides an overview of the architecture and the functionality of the demonstrator, as well as a roadmap for its development and evaluation.

Conventional wisdom on model transformations in Model-Driven Engineering (MDE) suggests that they are crucial components in modeling environments to achieve superior automation, whether it be refactoring, simulation, or code generation. While their relevance is well-accepted, model transformations are challenging to design, implement, and verify because of the inherent complexity that they must encode. Thus, defining transformations by chaining existing ones is key to success for enhancing their reusability. This paper proposes an approach, based on well-established algorithms, to support modellers when multiple transformation chains are available to bridge a source metamodel with a target one. The all-important goal of selecting the optimal chain has been based on the quality criteria of coverage and information loss. The feasibility of the approach has been demonstrated by means of experiments operated on chains obtained from transformations borrowed from a publicly available repository.

The distributed setting of computational mobile entities, called robots, thathave to perform tasks without global coordination has been extensively studied in the literature. A well-known scenario is that in which robots operate in Look-Compute-Move (LCM) cycles. During each cycle, a robot acquires asnapshot of the surrounding environment (Look phase), then executes an appropriate algorithm by using the obtained snapshot as input (Computephase), and finally moves toward a desired destination, if any (Movephase). Look-Compute-Move cycles might be subject to different temporal constraints dictated by the considered schedule. The classic models for theactivation and synchronization of mobile robots are the well-known fully-synchronous, semi-synchronous, and asynchronous models. A first comprehensive evaluation of the computational power of robots operating in the LCM model and moving within the Euclidean plane, under different levels of synchronization, has been proposed in [Das et al., Int.'l Conf. on Distributed Computing Systems, 2012]. In detail, the authors provide a series of results that prove relations between classic models and variations of them, which consider the possibility that robots are endowed with a visible light, i.e. they are luminous, or with the capability to store some past snapshots, or combinations of them. In this paper, we are interested in similar settings but for robots moving on graphs. In particular, we propose a characterization of the computational power of mobile robots on graphs as follows. First, we show the relations among the three classic activation and synchronization models. Second, we compare the models where robots are endowed with lights against the models without lights. Third, we highlight the relations among the different models concerning luminous robots. Finally, we provide a detailed comparison of the proposed results with the case of robots moving in the Euclidean plane.

Many efforts have been done in the last years to model public transport timetables in order to find optimal routes. The proposed models can be classified into two types: those representing the timetable as an array, and those representing it as a graph. The array-based models have been shown to be very effective in terms of query time, while the graph-based models usually answer queries by computing shortest paths, and hence they are suitable to be used in combination with speed-up techniques developed for road networks. In this paper, we focus on the dynamic behavior of graph-based models considering the case where transportation systems are subject to delays with respect to the given timetable. We make three contributions: (i) we give a simplified and optimized update routine for the well-known time-expanded model along with an engineered query algorithm; (ii) we propose a new graph-based model tailored for handling dynamic updates; (iii) we assess the effectiveness of the proposed models and algorithms by an experimental study, which shows that both models require negligible update time and a query time which is comparable to that required by some array-based models.

Best connections in real networks are usually found by applying Dijkstra's shortest paths algorithm. Unfortunately, networks deriving from real-world applications are huge, yielding unsustainable times to compute shortest paths. Therefore, considerable research has been conducted in recent years to accelerate Dijkstra's algorithm on typical instances of transportation and communication networks, such as road networks. These efforts have led to the development of many so called speed-up techniques, as for example Arc-Flags. The main drawback of many of these techniques, including Arc-Flags, is that they do not work well in the realistic dynamic scenarios where the networks change over time. In this article, we introduce a new data structure, named Road-Signs, which is used to update the Arc-Flags of a graph in fully dynamic scenarios. Road-Signs can be used to compute Arc-Flags, can be efficiently updated and does not require large space consumption for sparse networks. We develop a fully dynamic algorithm for updating Arc-Flags, by updating Road-Signs, each time that a modification occurs on an edge of the network. We show that this algorithm is better than recomputation from scratch of Arc-Flags in terms of the affected parameters of the input, which makes this solution suitable to be efficient in practice. However, it is not better than recomputation from scratch in the worst case. We also propose an experimental study to evaluate the practical performance of the new dynamic algorithm. To this aim, we use real-world road networks subject to sequences of weight change operations. Our experiments show a significant speed-up in the updating phase with respect to the recomputation from scratch of Arc-Flags.Copyright © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 63(3), 243–259 2014

This paper presents a system supporting tuning and evaluation of a Content-Based Image Retrieval (CBIR) engine for vector images, by a graphical interface providing query-by-sketch and query-by-example interaction with query results, and analysis of result quality. Vector images are first modelled as an inertial system and then they are associated with descriptors representing visual features invariant to affine transformation. To support requirements of different application domains, the engine offers a variety of moment sets as well as difierent metrics for similarity computation. The graphical interface offers tools that helps in the selection of criteria and parameters necessary to tune the system to a specific application domain.

We present results on the design of a visual interaction environment for geographic information systems. In our model a database is a spatially related collection of regions with features, hierarchically organized as a nested partition. We envision an exploration of the database where, starting from an abstract view, more detailed information on subregions and/or features is successively disclosed on demand and visualized in a fish-eye view fashion. The interaction process is a sequence of views on the database, displayed by maps preserving topological properties. Efficient navigation requires the adoption of topological invariants for representing such partitions. Since each interaction step modifies only a portion of the current invariant, the definition of our interaction primitives is based on an efficient incremental approach (the current topological invariant is updated by exploiting knowledge coming from previous steps, instead of recomputing always everything from scratch).