163,929 research outputs found
Methodology for object-oriented real-time systems analysis and design: Software engineering
Successful application of software engineering methodologies requires an integrated analysis and design life-cycle in which the various phases flow smoothly 'seamlessly' from analysis through design to implementation. Furthermore, different analysis methodologies often lead to different structuring of the system so that the transition from analysis to design may be awkward depending on the design methodology to be used. This is especially important when object-oriented programming is to be used for implementation when the original specification and perhaps high-level design is non-object oriented. Two approaches to real-time systems analysis which can lead to an object-oriented design are contrasted: (1) modeling the system using structured analysis with real-time extensions which emphasizes data and control flows followed by the abstraction of objects where the operations or methods of the objects correspond to processes in the data flow diagrams and then design in terms of these objects; and (2) modeling the system from the beginning as a set of naturally occurring concurrent entities (objects) each having its own time-behavior defined by a set of states and state-transition rules and seamlessly transforming the analysis models into high-level design models. A new concept of a 'real-time systems-analysis object' is introduced and becomes the basic building block of a series of seamlessly-connected models which progress from the object-oriented real-time systems analysis and design system analysis logical models through the physical architectural models and the high-level design stages. The methodology is appropriate to the overall specification including hardware and software modules. In software modules, the systems analysis objects are transformed into software objects
A network for multiscale image segmentation
Detecting edges of objects in their images is a basic problem in computational vision. The scale-space technique introduced by Witkin [11] provides means of using local and global reasoning in locating edges. This approach has a major drawback: it is difficult to obtain accurately
the locations of the 'semantically meaningful' edges. We have refined the definition of scale-space, and introduced a class of algorithms for implementing it based on using anisotropic diffusion [9]. The algorithms involves simple, local operations replicated over the image making parallel
hardware implementation feasible. In this paper we present the
major ideas behind the use of scale space, and anisotropic diffusion for edge detection, we show that anisotropic diffusion can enhance edges, we suggest a network implementation of anisotropic diffusion, and provide
design criteria for obtaining networks performing scale space, and edge detection. The results of a software implementation are shown
Obstacle Avoidance Subsystem for an Autonomous Robot
This research project details the design and implementation of the Obstacle Avoidance Subsystem for the Tigertron autonomous robot. This subsystem is designed to function as a smaller part of the whole Software Architecture and has the purpose of detecting, through use of a Laser Rangefinder, obstacles in the vehicle’s environment. Once the hardware is set up and configured, the Tigertron’s central software control architecture requests data from the Laser Rangefinder through a serial communication channel. This data is converted into objects that represent obstacles in the form of polar coordinates. These objects are stored in a container so the central control architecture can determine the best route to avoid these obstacles while still navigating to route waypoints
Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems
This paper describes a comprehensive prototype of large-scale fault adaptive
embedded software developed for the proposed Fermilab BTeV high energy physics
experiment. Lightweight self-optimizing agents embedded within Level 1 of the
prototype are responsible for proactive and reactive monitoring and mitigation
based on specified layers of competence. The agents are self-protecting,
detecting cascading failures using a distributed approach. Adaptive,
reconfigurable, and mobile objects for reliablility are designed to be
self-configuring to adapt automatically to dynamically changing environments.
These objects provide a self-healing layer with the ability to discover,
diagnose, and react to discontinuities in real-time processing. A generic
modeling environment was developed to facilitate design and implementation of
hardware resource specifications, application data flow, and failure mitigation
strategies. Level 1 of the planned BTeV trigger system alone will consist of
2500 DSPs, so the number of components and intractable fault scenarios involved
make it impossible to design an `expert system' that applies traditional
centralized mitigative strategies based on rules capturing every possible
system state. Instead, a distributed reactive approach is implemented using the
tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th
Annual IEEE International Conference and Workshop on the Engineering of
Computer Based Systems (ECBS), Washington, DC, April, 200
Fast Linear Algorithm for Active Rules Application in Transition P Systems
Transition P systems are computational models based on basic features of biological membranes and the observation of biochemical processes. In these models, membrane contains objects multisets, which evolve according to given evolution rules. In the field of Transition P systems implementation, it has been detected the necessity to determine whichever time are going to take active evolution rules application in membranes. In addition, to have time estimations of rules application makes possible to take important decisions related to the hardware/software architectures design.
In this paper we propose a new evolution rules application algorithm oriented towards the implementation of Transition P systems. The developed algorithm is sequential and, it has a linear order complexity in the number of evolution rules. Moreover, it obtains the smaller execution times, compared with the preceding algorithms. Therefore the algorithm is very appropriate for the implementation of Transition P systems in sequential devices
Fast Linear Algorithm for Active Rules Application in Transition P Systems
Transition P systems are computational models based on basic features of biological membranes and
the observation of biochemical processes. In these models, membrane contains objects multisets, which evolve
according to given evolution rules. In the field of Transition P systems implementation, it has been detected the
necessity to determine whichever time are going to take active evolution rules application in membranes. In
addition, to have time estimations of rules application makes possible to take important decisions related to the
hardware / software architectures design.
In this paper we propose a new evolution rules application algorithm oriented towards the implementation of
Transition P systems. The developed algorithm is sequential and, it has a linear order complexity in the number of
evolution rules. Moreover, it obtains the smaller execution times, compared with the preceding algorithms.
Therefore the algorithm is very appropriate for the implementation of Transition P systems in sequential devices
Bosonic Qiskit
The practical benefits of hybrid quantum information processing hardware that
contains continuous-variable objects (bosonic modes such as mechanical or
electromagnetic oscillators) in addition to traditional (discrete-variable)
qubits have recently been demonstrated by experiments with bosonic codes that
reach the break-even point for quantum error correction and by efficient
Gaussian boson sampling simulation of the Franck-Condon spectra of triatomic
molecules that is well beyond the capabilities of current qubit-only hardware.
The goal of this Co-design Center for Quantum Advantage (C2QA) project is to
develop an instruction set architecture (ISA) for hybrid qubit/bosonic mode
systems that contains an inventory of the fundamental operations and
measurements that are possible in such hardware. The corresponding abstract
machine model (AMM) would also contain a description of the appropriate error
models associated with the gates, measurements and time evolution of the
hardware. This information has been implemented as an extension of Qiskit.
Qiskit is an opensource software development toolkit (SDK) for simulating the
quantum state of a quantum circuit on a system with Python 3.7+ and for running
the same circuits on prototype hardware within the IBM Quantum Lab. We
introduce the Bosonic Qiskit software to enable the simulation of hybrid
qubit/bosonic systems using the existing Qiskit software development kit. This
implementation can be used for simulating new hybrid systems, verifying
proposed physical systems, and modeling systems larger than can currently be
constructed. We also cover tutorials and example use cases included within the
software to study Jaynes- Cummings models, bosonic Hubbard models, plotting
Wigner functions and animations, and calculating maximum likelihood estimations
using Wigner functions
Design of Hardware CNN Accelerators for Audio and Image Classification
Ever wondered how the world was before the internet was invented? You might soon wonder how the world would survive without self-driving cars and Advanced Driver Assistance Systems (ADAS). The extensive research taking place in this rapidly evolving field is making self-driving cars futuristic and more reliable. The goal of this research is to design and develop hardware Convolutional Neural Network (CNN) accelerators for self-driving cars, that can process audio and visual sensory information. The idea is to imitate a human brain that takes audio and visual data as input while driving. To achieve a single die that can process both audio and visual sensory information, it takes two different kinds of accelerators where one processes visual data from images captured by a camera and the other processes audio information from audio recordings. The Convolutional Neural Network AI algorithm is chosen to classify images and audio data.
Image CNN (ICNN) is used to classify images and Audio CNN (ACNN) to classify any sound of significance while driving. The two networks are designed from scratch and implemented in software and hardware. The software implementation of the two AI networks utilizes the Numpy library of Python, while the hardware implementation is done in Verilog®. CNN is trained to classify between three classes of objects, Cars, Lights, and Lanes, while the other CNN is trained to classify sirens from an emergency vehicle, vehicle horn, and speech
Fast Hardware Implementations of Static P Systems
In this article we present a simulator of non-deterministic static P systems
using Field Programmable Gate Array (FPGA) technology. Its major feature
is a high performance, achieving a constant processing time for each transition. Our
approach is based on representing all possible applications as words of some regular
context-free language. Then, using formal power series it is possible to obtain the
number of possibilities and select one of them following a uniform distribution, in
a fair and non-deterministic way. According to these ideas, we yield an implementation
whose results show an important speed-up, with a strong independence from
the size of the P system.Ministry of Science and Innovation of the Spanish Government under the project TEC2011-27936 (HIPERSYS)European Regional Development Fund (ERDF)Ministry of Education of Spain (FPU grant AP2009-3625)ANR project SynBioTI
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