<abstract xmlns="http://www.w3.org/1999/xhtml">

This paper is focused on presenting the simulated solution for synchronizing the CAN communication, which is signal based, with the Pulse Width Modulation (PWM) for each LED connected to a LED matrix manager device. Using the presented solution, the user is able to read and verify each LEDand represents a very easy and fast testing approach for automotive lighting with LMM (LED Matrix Manager) devices. The information provided by the sniffer improves the error root cause detection time, offers an overview on the requirements compliance, and assures an efficient monitorisation of the LMM behaviour. The LMM sniffer is developed due to the missing of a dedicated standard solution for this purpose.
</abstract>

A Led Matrix Manager Sniffer

<abstract xmlns="http://www.w3.org/1999/xhtml">

In this study, the performance analysis of waste heat recovery systems in a power generation system consisting of 13000 Proton Exchange Membrane Fuel Cells (PEMFC) in a stack has been investigated. Organic Rankine Cycle (ORC) and Kalina Cycle System 11 (KCS11) as bottoming cycles to convert generated waste heat of stack into electricity were compared with each other in a defined hybrid system. The improvement of system with an exact energy and exergy analysis after utilizing the waste heat in the hybrid system has been analyzed. Results show that the energy efficiency of combined system using Organic Rankine Cycle and Kalina Cycle System 11 increase by about 5% and 1.75% respectively. In addition, exergy analysis results indicate that exergy efficiency of combined system using Organic Rankine Cycle and Kalina Cycle System 11 increases by about 4% and 1.5% respectively. The total exergy destruction rate obtained for the hybrid power systems is 235.5 kW when ORC is used and 329.7 kW when KCS11 is used respectively. Results show that in presented systems ORC has higher energy and exergy efficiencies than KCS11 but different used working fluids and equipment of systems must also be considered from an economical point of view.
</abstract>

Technical and Vocational University (TVU)

Department of Mechanical Engineering, Science and Research Branch

A Comparative Energy and Exergy Analysis Between Organic Rankine Cycle and Kalina Cycle System 11 for the Waste Heat Recovery of a PEM Fuel Cell Power Station

<abstract xmlns="http://www.w3.org/1999/xhtml">

Nowadays, the real estate sector is also benefiting from the advance of the technology. Digital images accompany announcements advertising for renting or selling a property. Computer vision and image analysis contribute to the understanding of these images towards better consumer experience. In this paper we show how retrieval and description can be used efficiently to isolate cvasi-duplicate images and identify similar examples to the ones that have drawn viewer’s interest. Three problems related to real-estate imaging are identified and solutions are proposed and evaluated on large databases.
</abstract>

Image Processing and Analysis Laboratory (LAPI)

University Politehnica of București (UPB)

Automatic Real-Estate Image Analysis for Retrieval and Classification

<abstract xmlns="http://www.w3.org/1999/xhtml">

Using nonlinear chaotic systems for secure communication is an application field presented in recent published research. This paper aims at improving the security of signal transmission using chaotic encryption. The proposed approach uses a combined analogue and discrete-time structure, for both chaotic emitter and synchronizing receiver. The prototype system used is similar to the Lorenz nonlinear oscillator. Our study presents the emitter dynamic properties, develops the receiver synchronization using the emitter partitioning method, and designs a linear equalizer at the receiving end of the communication chain in order to improve the demodulated signal.
</abstract>

“Grigore T. Popa” University of Medicine and Pharmacy of Iaşi

“Gheorghe Asachi” Technical University of Iaşi, Faculty of Electronics

Synchronizing Analog - Discrete Nonlinear Systems

<abstract xmlns="http://www.w3.org/1999/xhtml">

Epilepsy is a neurological disorder characterized by recurrent seizures and has a high incidence rate. The aim of this research is to classify EEG signals as either focal and non-focal in order to identify the epileptogenic area of the brain, which can be surgically treated to manage epilepsy. In this paper, was proposed a classification method based on higher order spectra (HOS) parameters and four different classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-Nearest neighbors (KNN), and Mahalanobis distance (MD). The method was evaluated using a public dataset that consists in EEG recordings from epileptic patients. The classifiers performances were evaluated and it was shown that KNN classifier achieves a maximum classification rate of 99.55%, sensitivity of 100%, and specificity of 99.09%. The data classification was performed with maximum values of 0.96 for F1-score, and 0.91 for both Kappa and Matthews Coefficient. The results demonstrate the efficiency of the proposed method to identify the type of EEG signals.
</abstract>

madalina-giorgiana.murariu@student.tuiasi.ro

Discrimination of Focal and Non-Focal Epileptic Eeg Signals Using Different Types of Classifiers

<abstract xmlns="http://www.w3.org/1999/xhtml">

Spiking neural networks (SNNs) are inspired from natural computing, modelling with high accuracy the interactions and processes between the synapses of the neurons focusing on low response time and energy efficiency. This novel paradigm of event-based processing opens new opportunities for discovering applications and developing efficient learning methods that should highlight the advantages of SNNs such as the large memory capacity and the fast adaptation, while preserving the easy-to-use and portability of the conventional computing architectures. In this paper, we do a brief review of the developments of the past decades in the field of SNNs. We start with a brief history of the SNN and summarize the most common models of spiking neurons and methods to implement synaptic plasticity. We also classify the SNNs according to the implemented learning rules and network topology. We present the computational advantages, liabilities, and applications suitable for using SNNs in terms of energy efficiency and response time. In addition, we briefly sweep through the existing platforms and simulation frameworks for SNNs exploration. The paper ends with conclusions that show predictions of future challenges and the emerging research topics associated with SNNs.
</abstract>

A Short Survey of the Development and Applications of Spiking Neural Networks of High Biological Plausibility

AHEAD OF PRINT

Volume 68 (2022): Issue 4 (December 2022)

Volume 68 (2022): Issue 3 (September 2022)

Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section

Volume 67 (2021): Issue 4 (December 2021)

Volume 67 (2021): Issue 3 (September 2021)

Volume 67 (2021): Issue 2 (June 2021)

Volume 67 (2021): Issue 1 (March 2021)

 The journal Buletinul Institutului Politehnic din Iaşi, Electrotehnicã. Energeticã. Electronicã (Bulletin of thE Polytechnic Institute of Iasi, Electrical Engineering, Power Engineering, Electronics section) is publishing papers that present the research work in the broad area of Electrical Engineering, Power Engineering, Electronics, Automatic Control and Computer Science. The proposed topics of the published papers cover not only classical fields of Electrical and Computer Engineering Science, but also subjects in areas that show a rapid process of maturation. The journal is an effective mean of disseminating the recently obtained results, of promoting interdisciplinary research and also an opportunity for young researchers to present their relevant scientific work.  Authors are invited to submit papers to one of the following subject areas: Electrical Engineering Power Engineering Electronics Automatic Control Computer Science  The journal Buletinul Institutului Politehnic din Iaşi, Electrotehnicã. Energeticã. Electronicã is publishing mainly original research but, good quality focused review articles that scrutinize state of art, that highlight newly emerging trends or propose future directions for developing areas, are also welcomed after consulting the editor.  All articles are subject to a blind review assessing the content, scientific relevance, novelty and originality of research, quality of presentation, text structure and style, referencing, and the overall contribution of the article. Based on the review, the editor reserves the right to reject the paper or request the author to adjust it according to the reviewers suggestions.  The journal Buletinul Institutului Politehnic din Iaşi, Electrotehnicã. Energeticã. Electronicã is jointly coordinated by the Faculty of Electrical Engineering, Faculty of Electronics, Telecommunications and Information Technology and Faculty of Automatic Control and Computer Engineering of “Gheorghe Asachi” Technical University of Iași.  Plagiarism and Archiving Policy  Archiving  Sciendo archives the contents of this journal in System's Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section

Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section

A Led Matrix Manager Sniffer

A Comparative Energy and Exergy Analysis Between Organic Rankine Cycle and Kalina Cycle System 11 for the Waste Heat Recovery of a PEM Fuel Cell Power Station

Automatic Real-Estate Image Analysis for Retrieval and Classification

Synchronizing Analog - Discrete Nonlinear Systems

Discrimination of Focal and Non-Focal Epileptic Eeg Signals Using Different Types of Classifiers

A Short Survey of the Development and Applications of Spiking Neural Networks of High Biological Plausibility

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