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John N. Sahalos

Researcher at Aristotle University of Thessaloniki

Publications -  308
Citations -  3952

John N. Sahalos is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Antenna (radio) & Microstrip antenna. The author has an hindex of 30, co-authored 307 publications receiving 3538 citations. Previous affiliations of John N. Sahalos include Technical University of Madrid & ETSI.

Papers
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Proceedings ArticleDOI

Numerical approaches for EMI reduction of ICs and PCBs inside metallic enclosures

TL;DR: In this paper, a numerical approach to the reduction of electromagnetic interference (EMI) from the emissions of ICs and PCBs inside rectangular metallic enclosures is presented. And the applications of the above approach in PCB design are discussed.
Proceedings ArticleDOI

A wideband UHF RFID reader antenna array with bow-tie elements

TL;DR: In this paper, the authors presented the design procedure of a wideband UHF RFID reader antenna with polarization diversity suitable for searching tagged items, which consists of a microstrip array with alternating orthogonal bow-tie elements, which are fed in series by a pair of microstrip lines.
Journal ArticleDOI

Simultaneous optimization of an index and determination of the polarization of arrays of nonparallel wire antennas by the eigenvalue method

TL;DR: In this paper, the maximum obtainable index of an antenna which has not a predefined polarization is the largest of the only two nonzero eigenvalues of the regular pencil of its matrices.
Proceedings Article

A combined deterministic and stochastic approach for the design of non-uniform arrays

TL;DR: In this paper, a non-uniformly spaced array design with aperture and element size constraints is presented. But the resulting layouts fulfill demanding beamforming problems with a finite number of elements.
Proceedings ArticleDOI

Edge Computing for Offload-Aware Energy Conservation Using M2M Recommendation Mechanisms

TL;DR: This work proposes a novel offloading methodology that hosts a “resource-aware” recommendation scheme, which allows the efficient monitoring of energy draining applications that run in an IoT ecosystem.