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Manuel P. Malumbres

Researcher at Universidad Miguel Hernández de Elche

Publications -  126
Citations -  1447

Manuel P. Malumbres is an academic researcher from Universidad Miguel Hernández de Elche. The author has contributed to research in topics: Encoder & Wavelet. The author has an hindex of 22, co-authored 124 publications receiving 1360 citations. Previous affiliations of Manuel P. Malumbres include University of Valencia & Polytechnic University of Valencia.

Papers
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Proceedings ArticleDOI

An efficient implementation of tree-based multicast routing for distributed shared-memory multiprocessors

TL;DR: In this paper, a tree-based multicast with pruning to avoid deadlocks is proposed, which is targeted to situations where the size of message data is very small, like in invalidation and update messages in distributed shared-memory multiprocessors.
Proceedings ArticleDOI

Hierarchical Parallelization of an H.264/AVC Video Encoder

TL;DR: A hierarchical parallelization of H.264 encoders very well suited to low cost clusters is proposed and is a compromise between speed-up and latency and then a broader spectrum of applications can be covered.
Journal ArticleDOI

Monitoring Pest Insect Traps by Means of Low-Power Image Sensor Technologies

TL;DR: This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application.
Book ChapterDOI

Efficient Adaptive Routing in Networks of Workstations with Irregular Topology

TL;DR: A general methodology for the design of adaptive routing algorithms for networks with irregular topology, based on the application of the theory of deadlock avoidance proposed in [14], which increases routing flexibility by allowing cyclic dependencies between channels.
Journal ArticleDOI

On the Design of a Bioacoustic Sensor for the Early Detection of the Red Palm Weevil

TL;DR: The design of an autonomous bioacoustic sensor that can be installed in every palm tree under study and is able to analyze the captured audio signal during large periods of time is proposed, achieving average detection rates over 90%.