Spiros  Nikolaidis

Dr. Spiros Nikolaidis

orcid

Research Focus & Metrics

Academic Career Length

36Years (1991-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
26 October 2020 in IEEE Sensors Journal
Verified
Journal Article
Closed access
Achilles D. BoursianisAchilles D. Boursianis
Achilles D. Boursianis
Maria PapadopoulouMaria Papadopoulou
Maria Papadopoulou
AGAntonis Gotsis
Antonis Gotsis
Shaohua WanShaohua Wan
Shaohua Wan
Panagiotis SarigiannidisPanagiotis Sarigiannidis
Panagiotis Sarigiannidis
Sotirios K.  GoudosSotirios K. Goudos
Sotirios K. Goudos
Agriculture 4.0, as the future of farming technology, includes several key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. In this paper, we present in detail the subsystems and the architecture of an intelligent irrigation system for precision agriculture, the AREThOU5A IoT platform. We describe the operation of the IoT node that is utilized in the platform. Moreover, we apply the radiofrequency energy harvesting technique to the presented IoT platform, as an alternative technique to deliver power to the IoT node of the platform. To this end, we fabricate and validate a rectenna module for radiofrequency energy harvesting. Experimental results of the fabricated rectenna exhibit a satisfactory performance as a harvester of ambient sources in an outdoor environment.

1 January 1998 in IEEE Journal of Solid-State Circuits
Verified
Journal Article
Closed access
LBL. Bisdounis
L. Bisdounis
Spiros  NikolaidisSpiros Nikolaidis
Spiros Nikolaidis
OKO. Koufopavlou
O. Koufopavlou

9 January 2018 in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Verified
Journal Article
Closed access
IMIoannis Messaris
Ioannis Messaris
Alexander SerbAlexander Serb
Alexander Serb
SSSpyros Stathopoulos
Spyros Stathopoulos
AKAli Khiat
Ali Khiat
Spiros  NikolaidisSpiros Nikolaidis
Spiros Nikolaidis
Themis ProdromakisThemis Prodromakis
Themis Prodromakis
The translation of emerging application concepts that exploit Resistive Random Access Memory (ReRAM) into large-scale practical systems requires realistic yet computationally efficient device models. Here, we present a ReRAM model where device current-voltage characteristics and resistive switching rate are expressed as a function of a) bias voltage and b) initial resistive state. The model versatility is validated on detailed characterization data, for both filamentary valence change memory and non-filamentary ReRAM technologies, where device resistance is swept across its operating range using multiple input voltage levels. Furthermore, the proposed model embodies a window function which features a simple mathematical form analytically describing resistive state response under constant bias voltage as extracted from physical device response data. Its Verilog-A implementation captures the ReRAM memory effect without requiring integration of the model state variable, making it suitable for fast and/or large-scale simulations and overall inter-operable with current design tools.

1 March 1998 in IEEE Transactions on Circuits and Systems I: Regular Papers
Verified
Journal Article
Closed access
LBL. Bisdounis
L. Bisdounis
Spiros  NikolaidisSpiros Nikolaidis
Spiros Nikolaidis
OLO. Loufopavlou
O. Loufopavlou

13 February 2023 in IEEE Access
Verified
Journal Article
Open access
Lazaros MoysisLazaros Moysis
Lazaros Moysis
LILazaros Alexios Iliadis
Lazaros Alexios Iliadis
SSSotirios Sotiroudis
Sotirios Sotiroudis
Achilles D. BoursianisAchilles D. Boursianis
Achilles D. Boursianis
Maria PapadopoulouMaria Papadopoulou
Maria Papadopoulou
Sotirios K.  GoudosSotirios K. Goudos
Sotirios K. Goudos
The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments in Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

5 March 2008 in IEEE Transactions on Instrumentation and Measurement
Verified
Journal Article
Closed access
Vasileios KonstantakosVasileios Konstantakos
Vasileios Konstantakos
ACAlexander Chatzigeorgiou
Alexander Chatzigeorgiou
Spiros  NikolaidisSpiros Nikolaidis
Spiros Nikolaidis
TLTheodore Laopoulos
Theodore Laopoulos
This paper presents an energy consumption modeling technique for embedded systems based on a microcontroller. The software tasks that run on the embedded system are profiled, and their characteristics are analyzed. The type of executed assembly instructions, as well as the number of accesses to the memory and the analog-to-digital converter, is the required information for the derivation of the proposed model. An appropriate instrumentation setup has been developed for measuring and modeling the energy consumption in the corresponding digital circuits.