Giuliano Laccetti

ORCID: 0000-0002-0057-2573
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About
Contact & Profiles
Research Areas
  • Distributed and Parallel Computing Systems
  • Parallel Computing and Optimization Techniques
  • Cloud Computing and Resource Management
  • Scientific Computing and Data Management
  • Advanced Data Storage Technologies
  • Distributed systems and fault tolerance
  • Advanced Image Processing Techniques
  • Reservoir Engineering and Simulation Methods
  • Matrix Theory and Algorithms
  • Numerical methods in inverse problems
  • Image and Signal Denoising Methods
  • Meteorological Phenomena and Simulations
  • Computer Graphics and Visualization Techniques
  • IoT and Edge/Fog Computing
  • Oceanographic and Atmospheric Processes
  • Numerical Methods and Algorithms
  • Underwater Vehicles and Communication Systems
  • Interconnection Networks and Systems
  • Advanced Vision and Imaging
  • Image Enhancement Techniques
  • Thermoelastic and Magnetoelastic Phenomena
  • Advanced Malware Detection Techniques
  • Heat Transfer and Mathematical Modeling
  • Chaos-based Image/Signal Encryption
  • Cryptographic Implementations and Security

University of Naples Federico II
2014-2024

Istituto Nazionale di Fisica Nucleare
2016

Istituto Nazionale di Fisica Nucleare, Sezione di Napoli
2013

Institute for High Performance Computing and Networking
2000-2002

National Research Council
1999

National Research Council
1999

University of Basilicata
1990

While the everything as a sensor is typical data gathering pattern in Internet of Things (IoT) applications contexts such smart cities, factories, and precision agriculture, among others, use same technique coastal marine environment still not explored at full potential. Nevertheless, when it comes to maritime scenarios, application IoT networks distributed sensors actuators are limited, even though development electronics extreme network technologies present for decades also this area. In...

10.1109/access.2020.2996778 article EN cc-by IEEE Access 2020-01-01

Our method is based on the numerical evaluation of integral which occurs in Riemann Inversion formula. The trapezoidal rule approximation to this reduces a Fourier series. We analyze corresponding discretization error and demonstrate how expression can be used development an automatic routine , one user needs specify only required accuracy.

10.1145/326147.326148 article EN ACM Transactions on Mathematical Software 1999-09-01

Summary Low‐power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real‐time applications to run. In this paper, we describe RAPID, a complete framework suite computation offloading help low‐powered overcome these limitations. RAPID supports on Linux Android devices. Moreover, the implements lightweight secure data transmission operations. We present architecture framework, showing integration modules. show by extensive experiments that...

10.1002/cpe.4286 article EN Concurrency and Computation Practice and Experience 2017-09-08

10.1016/j.future.2007.01.002 article EN Future Generation Computer Systems 2007-01-18

Modern graphics processing units (GPUs) have been at the leading edge of increasing parallelism over last 10 years. This fact has encouraged use GPUs in a broader range applications, where developers are required to lever age this technology with new programming models which ease task writing programs run efficiently on GPUs. In paper, we discuss main guidelines assist developer when porting sequential scientific code modern These were carried out by L-BFGS, (Limited memory-) BFGS algorithm...

10.1080/00207160.2014.899589 article EN International Journal of Computer Mathematics 2014-04-23

Abstract The Block Conjugate Gradient algorithm (Block‐CG) was developed to solve sparse linear systems of equations that have multiple right‐hand sides. We adapted it for use in heterogeneous, geographically distributed, parallel architectures. Once the main operations Block‐CG (Tasks) been collected into smaller groups (subjobs), each subjob is matched by middleware MJMS (MPI Jobs Management System) with a suitable resource selected among those which are available. Moreover, within subjob,...

10.1002/cpe.1548 article EN Concurrency and Computation Practice and Experience 2010-03-04

10.1016/j.future.2007.03.009 article EN Future Generation Computer Systems 2007-04-07

In order to solve a problem in parallel we need undertake the fundamental step of splitting computational tasks into parts, i.e. decomposing solving. A whatever decomposition does not necessarily lead algorithm with highest performance. This topic is even more important when complex algorithms must be developed for hybrid or heterogeneous architectures. We present an innovative approach which starts from parts (sub-problems). These will regarded as elements algebraic structure and related...

10.31577/cai_2019_4_817 article EN Computing and Informatics 2019-01-01
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