Special Issue "10th International Conference Modern Circuit and Systems Technologies (MOCAST 2021)"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 20 December 2021.

Special Issue Editors

Prof. Dr. Spiros Nikolaidis
E-Mail Website
Guest Editor
Physics Department, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
Interests: digital circuits and systems
Special Issues and Collections in MDPI journals
Dr. Alon Ascoli
E-Mail Website
Guest Editor
Institut für Grundlagen der Elektrotechnik und Elektronik, Technische Universität Dresden, 01062 Dresden, Deutschland
Interests: nonlinear circuits and systems; networks of oscillators; cellular nonlinear networks; memristors

Special Issue Information

Dear Colleagues,

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from 5 to 7 July, 2021. The MOCAST technical program includes all aspects of circuit and system technologies from modeling, design, and verification to implementation and application in the area of Electronics and Communications. This Special Issue aims at publishing extended versions of top-ranked papers in the conference. The topics of MOCAST include:

  • Analog/RF and mixed signal circuits;
  • Digital circuits and systems design;
  • Nonlinear circuits and systems;
  • Device and circuit modeling;
  • Systems and applications;
  • Communication systems;
  • Network systems;
  • Power management;
  • Imagers, MEMS, medical, and displays;
  • Radiation front ends (nuclear and space application);
  • Education in circuits, systems, and communications.

Prof. Dr. Spiros Nikolaidis
Dr. Alon Ascoli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • electronics
  • communications
  • circuits and systems
  • systems and applications

Published Papers (1 paper)

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Analog Gaussian Function Circuit: Architectures, Operating Principles and Applications
Electronics 2021, 10(20), 2530; https://doi.org/10.3390/electronics10202530 - 17 Oct 2021
Viewed by 206
This review paper explores existing architectures, operating principles, performance metrics and applications of analog Gaussian function circuits. Architectures based on the translinear principle, the bulk-controlled approach, the floating gate approach, the use of multiple differential pairs, compositions of different fundamental blocks and others [...] Read more.
This review paper explores existing architectures, operating principles, performance metrics and applications of analog Gaussian function circuits. Architectures based on the translinear principle, the bulk-controlled approach, the floating gate approach, the use of multiple differential pairs, compositions of different fundamental blocks and others are considered. Applications involving analog implementations of Machine Learning algorithms, neuromorphic circuits, smart sensor systems and fuzzy/neuro-fuzzy systems are discussed, focusing on the role of the Gaussian function circuit. Finally, a general discussion and concluding remarks are provided. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Ultra-Low Power, Low-Voltage, Fully-Tunable, Bulk-Controlled Bump Circuit
Authors: Vassilis Alimisis; Marios Gourdouparis; Christos Dimas; Paul P. Sotiriadis
Affiliation: National Technical University of Athens
Abstract: This work proposes an ultra-low-power (6.0nW), low-voltage (0.6V), bulk-controlled, 10-transistor bump circuit architecture for Gaussian-function implementation. It can be used as a building block for analog implementation of Gaussian Mixture Model and Kernel Methods. The Gaussian curve width, height and center are independently and electronically adjustable. It consists of a modified current correlator and a bulk-controlled differential block with all transistors operating in sub-threshold. Proper operation, accuracy and robustness are confirmed via simulation and theoretical analysis. It was implemented in TSMC 90nm CMOS process and was simulated using the Cadence IC Suite.

Title: Control Strategies to Optimize Graph Coloring via M-CNNs with Locally Active NbOx Memristors
Authors: Alon Ascoli; Martin Weiher; Ronald Tetzlaff; Melanie Herzig; Stefan Slesazeck; Thomas Mikolajick
Affiliation: Technische Universität Dresden
Abstract: In this work, we will address reliable methods for the vertex coloring problem. It has been shown that networks of capacitively coupled memristor oscillators can be used to solve vertex coloring problems. In this paper, we figure out the negative impact of an unbalanced number of couplings for the oscillators on the performance of the network and compensate for this non-uniform coupling structure by an adjustment in the network itself. The negative effect of the device-to-device variability, affecting the NbOx memristor, employed in the array, on its functionality will be investigated and reduced via an adaptation of the memristor operating point. The main improvement in network performance is achieved by a detailed investigation of the local and global solutions of the network. Two strategies inspired by global optimization algorithms will be proposed to allow the network to overcome local solutions and find the global solution of the vertex coloring problem.

Title: ApproxQAM: High-Order QAM Demodulation Circuits with Approximate Arithmetic
Authors: Vasileios Leon; Ioannis Stratakos; Giorgos Armeniakos; George Lentaris; Dimitrios Soudris
Affiliation: National Technical Univ. of Athens
Abstract: Modern mobile communication systems utilize increased bandwidth to provide advanced network performance and connectivity, all while their most computationally intensive functions must be accelerated within the limited power envelope of embedded devices. In this paper, we improve the circuit complexity and throughput of a key digital function in the baseband processing chain, namely the high-order QAM demodulation. In particular, we explore four different demodulation algorithms; we employ both floating- and fixed-point arithmetic, and we insert approximations in the arithmetic units. In terms of the accuracy of our most prominent implementations, i.e., for 64-QAM, our designs deliver BER values ranging from 10−1 to 10−4 for SNR 0 − 14dB. In terms of FPGA resources on Xilinx ZCU106, these 64-QAM designs achieve up to 98% reduction in LUT utilization compared to the accurate floating-point model of the same algorithm, and up to 122% increase in operating frequency. When targeting demodulation with high levels of accuracy, i.e., almost zero BER degradation with respect to that of the original floating-point model, the prevailing solution is the Approximate LLR algorithm configured with fixed-point arithmetic and 8-bit truncation, providing 81% decrease in LUTs and 13% increase in frequency to sustain a throughput of 323 Msamples/second.

Title: Acoustic leak localization method based on signal segmentation and statistical analysis
Authors: Georgios-Panagiotis Kousiopoulos; Nikolaos Karagiorgos; Dimitrios Kampelopoulos; Vasileios Konstantakos; Spyridon Nikolaidis
Affiliation: Aristotle University of Thessaloniki
Abstract: One of the most serious problems occurring in a pipeline network is the appearance of leaks. The process of detecting and localizing leaks in pipeline systems concerns a very extensive field of signal processing methods employed for this matter. In this paper, a leak localization method combining the segmentation of acoustic leak signals, both in the time and in the frequency domains, with a statistical algorithm needed for dealing with the non-deterministic (stochastic) nature of these signals is proposed. This algorithm involves the use of cross-correlation techniques along with the grouping of the time-delay data in a histogram and selecting the bin with the largest number of elements as the one that provides the correct answer. Successful detection of the leak position requires the knowledge of the acoustic wave velocity in the pipe. In the present paper, calculation of the acoustic velocity is performed by the use of a PCB hammer to cover more realistic situations. The proposed leak localization method is tested experimentally in a laboratory setup containing a 67-meter steel pipeline and the results show that the presented method can localize leaks efficiently, since the average localization error is around 3%.

Title: Nonlinear System Identification: Prediction Error Method vs Neural Network
Authors: Jinming Sun; Yanqiu Huang; Wanli Yu; Alberto Garcia-Ortiz
Affiliation: University Bremen
Abstract: System identification has been used in various domains for analyzing system properties and carrying out filtering, prediction and automatic control. Prediction-error methods (PEMs), a broad family of parameter estimating approaches, perform excellently in identifying systems and are applicable to a wide range of arbitrary models. In recent years, the neural network technique (NN) has gained great attention in system identification. Its general framework is favored by black-box systems. As current applications tend to be executed on resource-constrained devices, e.g., wireless sensor networks (WSNs) or Internet of Things (IoT) end-devices, the complexity of an algorithm is crucial as well as the accuracy. Although there have been studies reducing calculations in NN by considering the system’s physical structure, the performance comparison with PEM is not clear. This paper gives a fair comparison between Kalman predictor-based PEM and continuous-time state-space-based NN, regarding the estimation accuracy and speed on several nonlinear systems. The results indicate that NN is more widely applicable and accurate, but more expensive from a computational perspective, whereas PEM has limitations on a system with frequently abrupt-changing signals, but its lightweight feature also makes it irreplaceable for many applications.

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