This work presents an efficient hardware architecture for hyperspectral image rotation, based on a geometric matrix transformation combined with bilinear interpolation to enhance pixel accuracy. These operations are commonly used in hyperspectral image (HSI) registration processes to compensate for motion in the observed scene. The primary objective of this research is to minimize the computational resource usage of the algorithm while optimizing overall efficiency. To achieve this, a reconfigurable hardware architecture based on FPGA is employed, where the rotation algorithm is implemented as an accelerator using the Vitis HLS synthesis tool and the block flow mechanism. The FPGA-based solution with four cores achieved a latency of 0,445 ms and a throughput of 1396,9 MB/s, with an energy consumption of only 1,09 mJ. Additionally, an alternative implementation was carried out on a GPU-based architecture (Jetson Nano), resulting in a higher latency of 12,55 ms and an energy consumption of 62,75 mJ. The evaluation included energy consumption, comparison with other solutions reported in the literature, and quality metrics. The results demonstrate that the FPGA-based solution is more efficient in terms of resource utilization and energy consumption, with minimal error.

The Internet of Things (IoT) is increasingly composed of numerous sensing devices with limited power availability, which rely on energy storage solutions such as batteries or supercapacitors to sustain their operation. To ensure a reliable power supply for continuous functioning and energy replenishment, energy harvesting systems have been proposed. However, energy harvesting is not without limitations, including high equipment costs and limited feasibility in certain environments. This paper investigates a star-connected IoT network, in which the central coordinator node is equipped with a solar energy harvesting system. The harvested energy is used not only to power the coordinator’s own operations but also to activate nearby end devices via Wireless Power Transmission (WPT). Based on the estimated amount of power each end device can receive, determined by its distance from the coordinator, we propose an algorithm to compute optimal task scheduling that maximizes overall quality while satisfying the energy neutrality condition. Simulation results demonstrate the feasibility of integrating energy harvesting with WPT to efficiently power IoT devices.

The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold-Based Algorithm (TBA) and a neural network with low number of parameters based on a Transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity, and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the other approach.

Abstract The automation of insect pest control activities implies the use of classifiers to monitor the temporal and spatial evolution of the population using computer vision algorithms. In this regard, the popularisation of supervised learning methods represents a breakthrough in this field. However, their claimed effectiveness is reduced regarding working in real-life conditions. In addition, the efficiency of the proposed models is usually measured in terms of their accuracy, without considering the actual context of the sensing platforms deployed at the edge, where image processing must occur. Hence, energy consumption is a key factor in embedded devices powered by renewable energy sources such as solar panels, particularly in energy harvesting platforms, which are increasingly popular in smart farming applications. In this work, we perform a two-fold performance analysis (accuracy and energy efficiency) of three commonly used methods in computer vision (e.g., HOG+SVM, LeNet-5 CNN, and PCA+Random Forest) for object classification, targeting the detection of the olive fly in chromatic traps. The training and testing of the models were carried out using pictures captured in various realistic conditions to obtain more reliable results. We conducted an exhaustive exploration of the solution space for each evaluated method, assessing the impact of the input dataset and configuration parameters on the learning process outcomes. To determine their suitability for deployment on edge embedded systems, we implemented a prototype on a Raspberry Pi 4 and measured the processing time, memory usage, and power consumption. The results show that the PCA-Random Forest method achieves the highest accuracy of 99%, with significantly lower processing time (approximately 6 and 48 times faster) and power consumption (approximately 10 and 44 times lower) compared with its competitors (LeNet-5-based CNN and HOG+SVM).

The proliferation of Internet of Things (IoT) technologies in various industrial sectors has brought forth new challenges that demand attention for achieving technological maturity. One such challenge is the lack of tools for emulating the diverse components present in IoT architectures, leading to the continuous verification of each component in the chain, which proves to be a complex task. This paper addresses the verification problem in Edge/Fog IoT platforms through comprehensive end-to-end testing. To tackle this challenge, we have developed a modular IoT simulator (SimIoT) capable of efficiently emulating thousands of IoT devices in realistic scenarios, including hospitals, airports, and smart cities. The simulator allows testing of Edge platforms without the need for programming expertise. Furthermore, we demonstrate the feasibility of our simulator by presenting a use case involving the profiling of an open-source IoT platform.

Energy neutrality of Internet of Things devices powered with energy harvesting is a concept introduced to let these devices operate uninterruptedly. A method to achieve it is by letting the device scheduling different tasks characterized by different energy costs (and quality), depending on the current energy production of the energy harvesting subsystem and on the residual battery charge. In this context, we propose a novel scheduling problem that aims at keeping the energy neutrality of the scheduling while maximizing the overall quality of the executed tasks and minimizing the leaps of quality among consecutive tasks, so to improve the stability of the output of the device itself. We propose for this problem an algorithm based on a dynamic programming approach that can be executed even on low-power devices. By simulation we show that, with respect to the state of the art, the scheduling by our algorithm greatly improve the stability of the device with a minor penalty in terms of overall quality.

The increasing trend in the percentage of older people among the European population, and in the number of older people living alone, poses a number of challenges, some of which can be addressed from digital solutions monitoring and supporting comfort, physical activity and health. Nowadays, research efforts seek to ensure, through technology-based solutions, the quality of life as people age and the needs that arise during the process. The home environment can host modern home-automation solutions supporting information gathering, monitoring and early intervention to prevent risks to lead to major consequences. Smart homes can thus make a positive contribution to the Active and Healthy Ageing paradigm. However, one of the biggest limitations preventing smart homes from being a reality is the lack of interoperability among different-vendor devices or the fact that most of the commer-tial solutions rely on cloud-based solutions. This work faces these limitations and proposes an architecture that, based on the use of commercial sensors, ensures that information is gathered and processed locally, independently of the commercial solution provided by the manufacturer of the device. The architecture has been experimentally validated by implementing a monitoring system based on environmental and security sensors interconnected by means of the ZigBee wireless communication protocol. The proposed system also supports caregivers caring for their older people who will be granted access to collected data. Thus, the system offers a remote monitoring system based on an information storage and visualisation solution. This system is intended to provide a solution that improves the quality of life and extends the time of independent living at home for older people.

The general aim of a cognitive Cyber Physical System of Systems (CPSoS) is to provide managed access to data in a smart fashion such that sensing and actuation capabilities are connected. Whilst there is significant funding and research devoted to this area, focus remains purely on creating bespoke systems. This paper presents a novel approach, based on a set of components to leverage Situational Awareness and Smart Actuation in large manufacturing industries with the focus on enabling predictive maintenance for asset and abnormal situation management. This paper presents a novel generic platform, named AtiCoS, that combines case-based and common-sense reasoning, as the enabling methodologies for enhancing CPSoS with cognitive capabilities.

The “smart” behaviour that characterises commercial Smart Home systems is due to the relations that users manually establish between devices and actions through automation rules. Therefore, the intelligence of these systems is entirely (or mostly) predefined by humans. To address this problem, several Artificial Intelligence techniques have been applied in this field to try to improve the smartness of Smart Home systems by enhancing features such as self-adaptation, self-evolution and self-awareness. This work in progress article presents an approach to provide smart homes with these features through a reasoning system able to deduce, define, monitor and update the automation rules that drive their behaviour, in an autonomous manner and with a knowledge management at different levels of abstraction. That is to say, the rules normally defined by humans are now created and modified automatically. The proposal is based in a commonsense knowledge model that represents how the world works and enables to infer what resources can be used to accomplish a specific objective. Automations are generated according to general, commonsense and context (topology, users, sensors, etc.) knowledge, and they are more or less sophisticated depending on the available devices. The set of behavioural rules is updated when any of these resources change. Scone is the high-performance and open-source knowledge base system where the knowledge model has been implemented. Finally, to evaluate and demonstrate the potential of the model, a set of automation rules is built for a concrete use case. Results are shown in a virtual smart home on the Home Assistant platform.

Nowadays, the amount of elder people living alone is increasing, with all the risks that it involves, maybe the most dangerous threat that they face is fall off with nobody around to help them. A fall at an advanced age usually leads to consequences like bones and hip fracture, which in addition to the low mobility that these people present, make to stand up impossible for them. This situation can get worse if after the fall a person loses consciousness, making it impossible to contact to a third party for help by means of a mobile phone or something similar. Different solutions have been developed in order to accomplish this problem, but some of them are not realistic enough, for example some video solutions invade our privacy, and those which are based on mobile phones expect the user to go everywhere with it. In this paper, it is proposed a low cost solution based on an 9-axis IMU (Inertial Measure Unit), which counts with an accelerometer, gyroscope and magnetometer that will give us the needed information to build a fall detector supported by machine learning. This system will include a gateway, which will be responsible of the data collection and the most complex computations.

The active and healthy ageing paradigm is gaining momentum as technology is turning the futuristic scenarios envisioning smart homes into reality. An ecosystem of services and devices devoted to risk prevention, prediction and early intervention will support older people in extending the time they live independently. Despite the similarities found between the field of Active and Healthy Ageing and the Internet of Things, there are two main aspects that justify a dissociated analysis. First, most of the targeted users can be somehow considered vulnerable, either because of their lack of technological literacy or because they may even suffer some kind of cognitive impairments (although this might be in a very early stage). Second, such ecosystems are thought to be deployed in one of the most private spheres of a human being, as it is the home. For this reason, special protection should be granted to the devices and services deployed in this domain. However, this contrast with the current state of the cybersecurity for Active and Healthy Ageing. There are no specific studies, standards or guidelines available to assist manufacturers and developers in their work. This does not apply to ethical or general security concerns that have been adequately addressed. In this sense, it is necessary to complement existing ethical and data privacy and security guidelines, with other aspects related to the security of IT systems for this particular application field. This paper aims to fill this gap by presenting a general analysis of the main issues that require special attention from a cybersecurity perspective.

Unmanned aerial vehicles (UAVs) are autonomous devices employed as data collectors in precision agriculture to support a large number of applications. These UAVs gather data from on-the-ground wireless sensor networks, especially in scenarios that lack any kind of fixed communication infrastructure or where the available infrastructure does not fit the application requirements. Sensors on the ground can store sensing data, and in scenarios that do not require a real-time observation and analysis of the data, like in smart farming, a drone can be used maybe once or two times each day to collect and report the data to a command-and-control center that use them directly, without any other infrastructure (cloud or edges). In this article, we study analytically how close the drone, that uses a LoRa radio, needs to fly over the sensors to collect data with a given quality of data collection. This can be used to properly spacing the sensors on the field at deployment time, to select among different type of drones, and to properly solve some tradeoff related to field size vs autonomy of the drones and the path used by the latter when collecting the data.

Detecting emotions in a learning environment can make the student-learning process more efficient, avoiding \nstressful situations that might eventually lead to failure, frustation and demotivation. The work presented here \ndescribes a perceptive desktop devised to capture the sensations of any person facing learning activities. To \nthis end, we propose a perceptive environment enhanced with capabilities to perform an analysis of electroen cephalography, facial expression, eye tracking and particularly a very distinctive indicator of stress as it is \nthe galvanic response of the skin. This work focuses on the galvanic response of the skin, comparing the \nperformance of two devices in the context of the perceptive desktop. One of the devices was very attractive to \nour environment as it was a mouse that fit very well to our computer-based desktop, equipped with low-cost \nsensors to detect the galvanic response. The other device is more tedious to place and more expensive but we \nuse it as a reference to know if the mouse is accurate. Four people were exposed to an experiment with the \ntwo devices connected, and observing the results it can be concluded that there is no correlation between the \ncaptures of both devices. Therefore, we could not select the mouse for our environment even though at first it \nlooks like a very promising device.

The Internet of Things (IoT) paradigm poses a great variety of application domains where million of devices work uninterruptedly to improve some aspect of our lives. To support the continuous execution of the applications working on the devices, energy harvesting systems enable to extract the energy found naturally in the environment (for instance from the sun or from the wind) and convert it into energy able to either sustain the device's operation and recharge its batteries which, in conjunction with an appropriate scheduling strategy, led to the device to an electrically sustainable state (i.e. an energy-neutral state). Most of the works found in literature oriented to achieve energy neutrality are however evaluated by means of simulation which means that, in spite of precisely modeling hardware features and energy productions, lack of the realism that we find in a real deployment. A minor part of the works are based on a real deployment but do not share the collected data that permit to replicate the analysis. With this purpose in mind, in this article we describe a testbed designed for outdoor monitoring purposes in the IoT context, equipped with several sensors for weather conditions monitoring and with a solar panel to provide application lifetimes potentially infinite. The testbed was deployed on the roof of a building and it executed uninterruptedly an application able to generate a dataset with the collected information over a period of more than two months. This dataset has been online published to be used for different researching purposes, as for instance, prediction models of the energy production.

To make sustainable large IoT deployments in smart cities, a promising approach is to develop a new generation of solar energy harvesting IoT devices based on the concept of energy neutrality. Key to this concept are the models for the forecast of energy production, which provide input to the energy-neutral schedulers governing the activities of the IoT devices. The development of such models however need to be validated against real-world conditions. To this purpose we propose a testbed aimed at the collection of real-world dataset about the energy parameters of energy harvesting IoT devices, and, on the base of such a dataset, we perform a comparative assessment of state of the art and novel energy production forecast models.

Scheduling tasks appropriately in an IoT device powered by multiple energy-harvesting sources is a challenging problem. In this paper, we model this problem, and we present a scheduling algorithm that optimally sets the overall node power consumption based on the utility, and on the energy required by tasks. The algorithm schedules high-level tasks, it uses the weather forecast informations available at the beginning of each scheduling period (typically a day), and the level of the battery, to define an optimal schedule. The main goal is to find a schedule that is energy neutral on average, over a period longer than the single scheduling window, for example a week. We test our scheduler on a simulated platform with the same specs of an Arduino node, equipped with a small (portable) solar panel, and attached to a small wind turbine. We see from the simulations that the scheduler performs as expected and that the utility of the scheduling improves as the error between the expected forecast and the real harvested energy is reduced.

This paper presents an architecture for smart buildings based on common-sense reasoning using the IndoorGML standard. The main objective is the construction of a knowledge model that allows inferring information that is not explicitly defined in that model. The main challenges addressed are: 1) the modeling of the concepts presented by the standard in the knowledge base used, known as Scone; 2) the automation of the introduction of knowledge in Scone with the proposed model; and 3) the implementation of a service-oriented architecture that makes possible to introduce knowledge in a transparent way. The semantic knowledge we present here, which shows more advanced and flexible capabilities, will make it possible in the future to carry out reasonings that not only depend on the structure of a building, but on many other aspects of the IoT such as sensors or actuators that are difficult to contemplate if a common sense approach is not followed.

Outdoor Internet of Things (IoT) applications usually exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices. In this context, we propose a new dynamic programming algorithm for the optimization of the scheduling of the tasks in IoT devices that harvest energy by means of a solar panel. We show that the problem is NP-hard and that the algorithm finds the optimum solution in a pseudo-polynomial time. Furthermore, we show that the algorithm can be executed with a small overhead on three popular IoT platforms (namely TMote, Raspberry PI, and Arduino) and, by simulation, we show the behavior of the algorithm with different settings and at different conditions of energy production.

This paper proposes and analyzes the use of the Arduino Zero board as the lab platform for the Computer Structure course that constitutes an essential part of Computer Science studies. The understanding of the main functional blocks of a computer, addressing the main concepts included in the course syllabus, is reinforced by mean of the hands-on experience acquired in the lab sessions and the completion of a project based on a mobile robot. Special care has been devoted to link the theoretical concepts with their practical application. The inclusion of a debugging chip (EDBG) in the Arduino Zero board is one of the main assets to enable exploring the architecture and analyze the execution of programs down to the assembler instructions level.

The Internet of Things (IoT) paradigm envisions a world of interconnected objects in which every object or thing can be univocally addressed and accessed, independently of its inherent technology and location. To the date, most of the state-of-the-art solutions proposed to realize the IoT vision employ a cloud-based approach. This means that, independently on where objects are physically located, their interconnection relies on a cloud-based solution. In a daily life situation it is like requesting a call-center operator to get us in contact to someone that is nearby us. Common sense tells us that it is not only simpler, but more efficient, to directly talk to him/her minimizing delays, failing points, and unnecessary intermediaries. This paper presents a protocol for IoT, named IDM (Inter-Domain Messaging), intended to overcome the limitations of cloud-based solutions for IoT. The IDM approach consists in offering an abstraction layer upon which different technologies can interact in a seamless and efficient manner. This paper also provides an experimental validation consisting in the implementation of a prototype interconnecting five different domains, with technologies like ZigBee, Bluetooth, RS485 or WiFi.

Smart environments, enabled by the Internet of Thing (IoT) paradigm, advocate for more intelligent and interconnected systems, electronic devices, tools, and appliances. While most efforts are nowadays addressed to provide connectivity or smartness to IoT devices, unfortunately, few have realised the importance of supporting automatic service composition and service reconfiguration capabilities at middleware level. Due to the openness that characterise such environments, the range of possible interactions and available services and devices cannot be totally defined nor prescribed in advanced. This uncertainty demands mechanisms to dynamically adapt existing systems, and their functionality, to address unforeseen needs. In order to do so, a general understanding of contexts, services, and device capabilities is needed. From that understanding, new responses can be automatically devised on run-time. This paper presents a semantic middleware specifically devised to support automatic and autonomous service reconfiguration and composition. The novelty consists in moving the semantics, traditionally held at the programming-interface level, to a common-sense knowledge-base system, with higher expressive power and reasoning capabilities.

The occurrence of emergency situations in high-rise buildings, daily hosting hundreds of people, may force the massive evacuation of their occupants with the ultimate goal of preventing the loss of lives. In this paper, we propose an adaptive algorithm for dynamically computing safe evacuation routes, while the load of people is balanced between the accesses of each floor of the building, thus avoiding the accumulation of people in hotspots. The combination of this algorithm with sensors to detect the events of interest and a navigation system to guide the evacuees turns this solution into a specially suited approach for evacuation in high-rise buildings. Simulation results demonstrate that the yield evacuation routes drive all people outdoor with a similar average path length for the different risk scenarios addressed.

Partial Reconfiguration is one of the most attractive features of FPGAs. This feature provides new computing possibilities, such as the reduction of the total area required in a FPGA by means of functioning overlapping, or the modification of the design after its deployment, where a complete configuration is not needed. However, the design of partially reconfigurable systems is still a complex task. This work focuses on facilitating the design process and proposes a new development framework for dynamically configurable systems from high level UML/MARTE models which, starting from dynamically reconfigurable systems high level UML/MARTE models. Simulation and VHDL code are generated from those models, according to the specification requirements of the reconfigurable hardware captured in the specifications. To demonstrate this approach, a edge detection-based use case has been implemented with the developed framework.

Partial Reconfiguration is one of the most attractive features of FPGAs. This feature provides new computing possibilities, for instance we can change a part of the initial functionality after its deployment, where a complete configuration is not needed, and the total area required is reduced. However, the design of partially reconfigurable systems has been a complex task yet. This work try to facilitate the design process and proposes a new development flow, which reduces mistakes during first stages of the design and makes the building of partial reconfiguration projects easier. In addition, we provide a dedicated hardware component, which manages bit streams nd dynamic areas. This component speed up the reconfiguration time, accomplishing a speed about 180MB/s.

FPGA-based embedded systems are gaining relevance for implementing a wide range of applications. Part of their success is due to their balanced compromise between performance and flexibility, but also because of their capability for exploiting the dynamic reconfiguration. However, the costly reconfiguration process and the lack of management support have prevented a broader use of the FPGAs. In order to contribute to solve these issues, in this paper we propose a software/hardware dynamic resource management system that combines scheduling and placement tasks, providing a complete management flow for supporting dynamically reconfigurable hardware designs. One of the advantages of the proposed model is the capability for running its scheduling and placement tasks in different nodes, as part of a distributed network. The results of our experiments demonstrate that our placement policy, specially designed for reconfigurable systems, achieves good results, in terms of reusability and performance, compared to other management approaches.

The design of multimedia systems is becoming more and more challenging due to their growing complexity and strict performance requirements. New implementations fulfilling new market requirements must be included in increasingly complex multimedia systems. In this context, the OpenMAX standard provides abstraction facilities needed in order to design multimedia streaming applications with large amounts of data processing. This paper presents a methodology which provides a UML/MARTE methodology for the specification of multimedia systems according to the OpenMAX specification requirements for system level-design activities and a framework which automates the extraction of the corresponding SystemC executable specification, enabling the execution and the verification of the multimedia system.

This paper presents a methodology for adapting an advanced communication system for deaf people in a new domain. This methodology is a user-centered design approach consisting of four main steps: requirement analysis, parallel corpus generation, technology adaptation to the new domain, and finally, system evaluation. In this paper, the new considered domain has been the dialogues in a hotel reception. With this methodology, it was possible to develop the system in a few months, obtaining very good performance: good speech recognition and translation rates (around 90%) with small processing times.

This document presents a persistence management model for reconfigurable SoC. This model provides an efficient mechanism for persistence to preserve data information of hardware components that are swapped out of dynamically reconfigurable areas, in order to allow the reinsertion of these components and to restart the execution path from the same point where they were interrupted when reinserted. This mechanism allows state management of components instantiated not only in reconfigurable areas, but also for those instantiated in static areas, that are feasible to be stopped and replaced for new versions instantiated in hardware or implemented in software migrating their state to the new ones.

Abstract This paper provides an analysis of the design space available to middleware developers in the context of wireless sensor networks (WSN). We identify the weaknesses of current communication abstraction layers and propose alternative implementation techniques that preserve most of the useful features but minimize the implementation cost in resource constrained wireless sensor nodes. Our proposal includes a whole WSN development framework based on standard distributed objects and a set of specific services designed to support highly dynamic and scalable WSN applications. Copyright © 2007 John Wiley & Sons, Ltd.

HW-SW interoperability by means of standard distributed object middlewares has been proved to be useful in the design of new and challenging applications for ubiquitous computing, and ambient intelligence environments. Wireless sensor networks are considered to be essential for the proper deployment of these applications, but they impose new constraints in the design of the corresponding communication infrastructure: low-cost middleware implementations that can fit into tiny wireless devices are needed. In this paper, a novel approach for the development of pervasive environments based on an ultra low-cost implementation of standard distributed object middlewares (such as CORBA or ICE) is presented. A fully functional prototype supporting full interoperability with ZeroC ICE is described in detail. Available implementations range from the smallest microcontrollers in the market, to the tiniest embedded Java virtual machines, and even a low-end FPGA

For decades, software middlewares have tackled with the heterogeneity and interconnectivity problems in computer networks with success. These problems are recurrent in the design of complex systems-on chip with a large number of components of different nature (including SW and HW modules). In this paper we present an object- oriented communication engine (OOCE) based on the architectural concepts used in software middlewares that unifies the inter-communication interface for both HW and SW elements. The infrastructure provided by the OOCE introduces a low-overhead abstraction layer that can be easily used to implement several parallel programming models. An implementation of this approach has been made for the Xilinx-V2Pro platform.

In this paper we propose a contribution to reduce the complexity of management of dynamic reconfiguration. The model for Dynamic Reconfiguration presented here is based on the object-oriented paradigm and a clear separation between the functionality inside the reconfigured cores and the communication infrastructure. This infrastructure will be the responsible not only for managing data transferences between objects but also for transparently handling other aspects such as state storage and retrieval or controlled object stopping and resuming.

Increasing reuse opportunities is a well-known problem for software designers as well as for hardware designers. Nonetheless, current software and hardware engineering practices have embraced different approaches to this problem. Software designs are usually modelled after a set of proven solutions to recurrent problems called design patterns. This approach differs from the component-based reuse usually found in hardware designs: design patterns do not specify unnecessary implementation details. Several authors have already proposed translating structural design patterns concepts to hardware design. In this paper we extend the discussion to behavioural design patterns. Specifically, we describe how the hardware version of the Iterator can be used to enhance model reuse.

This paper presents an in-depth study of several system partitioning procedures. It is based on the appropriate formulation of a general system model, being therefore independent of either the particular co-design problem or the specific partitioning procedure. The techniques under study are a knowledge-based system and three classical circuit partitioning algorithms (Simulated Annealing, Kernighan&Lin and Hierarchical Clustering). The former has been entirely proposed by the authors in previous works while the later have been properly extended to deal with system level issues. We will show how the way the problem is solved biases the results obtained, regarding both quality and convergence rate. Consequently it is extremely important to choose the most suitable technique for the particular co-design problem that is being confronted.

Summary form only given. Current embedded systems are made of multiple heterogeneous devices interconnected. These devices present a great variation of functionality, performance, and interfaces. Therefore, it is difficult to build applications for these platforms. In this paper we present some techniques to introduce component-based methodologies into hardware-software codesign. We place special emphasis on the use of simple, homogeneous interfaces to hide the inherent complexity of current designs. A key contribution is the definition of a HW-SW Operating System that makes system resources available to application developers in a clean, homogeneous way. This greatly simplifies the task of designing complex heterogeneous embedded systems.

This work presents a new I/sub DDQ/-based test criterion supported bp the characteristics of a set of experimental testing measurements realized over different samples of industrial ICs and by the definition of the corresponding simulation model. Comparing the current consumptions of a specific circuit a significant correlation between measurements can be observed. The current behaviour can be divided into two parts: (1) a circuit dependent one, which has a major contribution, and affects equally all the devices in a given die, and (2) a smaller die dependent fraction due to variations, defective and non-defective, of each of the devices of a specific die. In this paper a current model is defined introducing the effects of manufacturing variations in the basic equations of the sub-threshold current to explain that double behaviour. The results show how it is possible to obtain a lot of information from I/sub DDQ/ measurements and how other test selection criteria can be applied to increase the I/sub DDQ/ testing sensitivity and quality.

This paper describes how optimization techniques can be applied to efficiently solve the constrained co-design problem. This is performed by the formulation of different cost functions which will drive the hardware-software partitioning process. The use of complex cost functions allows us to capture more aspects of the design. Besides, the appropriate formulation of this kind of functions has a great impact on the results that can be obtained regarding both quality and algorithm convergence rate. A strong point of the proposed formulation is its generality. Therefore, it does not depend on the problem and can be easily extended for considering new design constraints.

Presents a new specification technique for complex hardware-sofware systems, based on standard high-level programming languages, such as C, C++, Java, Scheme, or Ada, without extensions or semantic changes. Unlike previous approaches, the designer may choose the model of computation and the specification language that best suits her needs, while still being able to formally verify the correctness of the specification. The details of the available hardware and software resources, and the implementation of the different models of computation are encapsulated in libraries to maximize reuse in system specifications.

A novel library layer, called the "design space layer," is proposed, aimed at supporting both IP-based and traditional "in-house" design methodologies, during early design space exploration. Strategies for effectively pruning the large design spaces characteristic of system-on-a-chip designs, and for transparently retrieving information on cores adequate for implementing the system components, are supported by the proposed layer. The layer is self-documented and highly compartmentalized into hierarchies of classes of design objects, and is thus easily scalable. A design space layer developed for encryption applications is presented and discussed in some detail.

This paper presents SHAPES, a tool for hardware-software partitioning. It is based on two main paradigms: the implementation of the partitioning tool by means of an expert system, and the use of fuzzy logic to model the parameters involved in the process.

Taking maximum advantage of dynamic reconfiguration in the implementation of digital systems poses a number of challenging research problems. Specifically, techniques are needed to partition the system behavioral description into segments of computation (or "scheduling units"), and to define a reconfiguration schedule with respect to those units, so as to maximize the performance of the dynamically reconfigurable system, subject to the area constraints of the FPGA. We propose a methodology to: (1) perform a coarse-grained partitioning of the system behavioral description into a set of tasks, (2) determine which sub-set of tasks is to remain resident in the FPGA, and which sub-set is to be non resident, (3) generate a reconfiguration schedule for the non-resident tasks by specifying when such tasks should be loaded on to and erased from the FPGA.