Vladimir D. Luchinskiy - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Author details

Vladimir D. Luchinskiy

Publications

1

Citations

2

Publications by Year

20232023

Co-Authors:

Vitaly D. BezuglyjAleksandr KhorinAlexey A. KlokovLeon KochievDi Un Pak

Show All Co-Authors (6)

Vladimir D. Luchinskiy

Affiliation

Autonomics.tech, Limassol, Cyprus

Publication Topics

2D Images,
3D LiDAR Point Clouds,
3D Point Cloud,
3D Scene,
3D Segmentation,
Artificial Neural Network,
Autonomous Vehicles,
Deep Neural Network,
Domain Adaptation,
Domain Adaptive Segmentation,
Field Of View,
LiDAR Point Clouds

Biography

Vladimir D. Luchinskiy was born in Ulyanovsk, Russia, in 1998. He received the bachelor’s degree in the software and administration of information systems from Saint-Petersburg State University (SPbU), in 2020.,From 2020 to 2021, he was a Machine Learning Engineer with Autonomics.tech. His research interests include deep learning computer vision problems.(Based on document published on 25 July 2023).

Publications

1

Citations

2

Publications by Year

20232023

Co-Authors:

Vitaly D. Bezuglyj
Aleksandr Khorin
Alexey A. Klokov
Leon Kochiev
Di Un Pak

Show All Co-Authors (6)

Author's Published Works

Search History

Showing 1-25 of 99 results

Conferences (69)

Journals (30)

Sort

Filter Results

Show

Open Access Only

Range
Single Year
C. Turchetti(57)
M. Conti(42)
Claudio Turchetti(42)
S. Orcioni(30)
P. Crippa(26)
Department of Electronics, University of Ancona, Ancona, Italy(17)
Dipartimento di Elettronica e Automatica, University of Ancona, Ancona, Italy(7)
University of Ancona, Ancona, Italy(6)
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy(6)
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Ancona, Italy(4)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(6)
2003 Design, Automation and Test in Europe Conference and Exhibition(5)
9th International Conference on Electronics, Circuits and Systems(3)
IEEE Journal of Solid-State Circuits(3)
IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications(3)
IEEE(99)
Code(2)
Munich, Germany(5)
Dayton, OH, USA(3)
Dubrovnik, Croatia(3)
Honolulu, HI, USA(3)
Phoenix-Scottsdale, AZ, USA(3)
Drain Current(17)
Neural Network(15)
Random Variables(14)
Circuit Simulation(12)
Nonlinear Systems(11)

Select All on Page

Sort By

Results

Embedded Real-Time Vehicle and Pedestrian Detection Using a Compressed Tiny YOLO v3 Architecture

Laura Falaschetti;Lorenzo Manoni;Lorenzo Palma;Paola Pierleoni;Claudio Turchetti

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

HTML

Vehicle and pedestrian detection (VaPD) is one of the most critical tasks in an advanced driver assistance system which help the driver to drive safely and save the pedestrian life. VaPD is a typical object detection problem that requires a trade-off among accuracy, speed, and memory consumption. Most existing methods focus on improving detection accuracy, while ignoring VaPD requires real-time de...Show More

Embedded Real-Time Vehicle and Pedestrian Detection Using a Compressed Tiny YOLO v3 Architecture

Laura Falaschetti;Lorenzo Manoni;Lorenzo Palma;Paola Pierleoni;Claudio Turchetti

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

An Embedded Vision System for Autoimmune Skin Diseases Classification Based on Deep Learning: A Preliminary Study

Laura Falaschetti;Sara Campanella;Sofia Curina;Giorgia Martini;Paola Pierleoni;Alberto Belli;Claudio Turchetti;Lorenzo Palma

2024 IEEE Sensors Applications Symposium (SAS)

Year: 2024 | Conference Paper |

HTML

The skin can exhibit symptoms of internal patho-logic conditions. Cutaneous autoimmunity is one such instance, serving as a sign of a systemic lack of immune tolerance. It is quite challenging to appropriately identify skin conditions because of their seemingly identical clinical presentations. Thus, this work suggests an embedded vision system based on a CNN model that is put into practice on a l...Show More

An Embedded Vision System for Autoimmune Skin Diseases Classification Based on Deep Learning: A Preliminary Study

Laura Falaschetti;Sara Campanella;Sofia Curina;Giorgia Martini;Paola Pierleoni;Alberto Belli;Claudio Turchetti;Lorenzo Palma

2024 IEEE Sensors Applications Symposium (SAS)

Year: 2024 | Conference Paper |

EEG-Based Neurodegenerative Disease Classification using LSTM Neural Networks

Michele Alessandrini;Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simona Luzzi;Claudio Turchetti

2023 IEEE Statistical Signal Processing Workshop (SSP)

Year: 2023 | Conference Paper |

Cited by: Papers (9)

HTML

In recent years, the use of electroencephalography (EEG) for the clinical diagnosis of neurodegenerative diseases, such as Alzheimer’s disease, frontotemporal dementia and dementia with Lewy bodies, has been extensively studied. The classification of these different neurodegenerative diseases can benefit from machine learning techniques which, compared to manual diagnosis methods, have higher reli...Show More

EEG-Based Neurodegenerative Disease Classification using LSTM Neural Networks

Michele Alessandrini;Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simona Luzzi;Claudio Turchetti

2023 IEEE Statistical Signal Processing Workshop (SSP)

Year: 2023 | Conference Paper |

Complexity bounded classification of fish-eye distorted objects with micro-controllers

Danilo Pietro Pau;Alessandro Carra;Marco Garzola;Laura Falaschetti;Claudio Turchetti

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

HTML

Since 2019, tiny machine learning has imposed itself everywhere as an innovative technology trend deployed at the edge and has been pervasive in many IoT applications. One interesting, addressed by this work, is related to the welfare of the laboratory animals that could be preserved by acquiring and classifying some image data to monitor object’s presence in their cages. For example mice and rats...Show More

Complexity bounded classification of fish-eye distorted objects with micro-controllers

Danilo Pietro Pau;Alessandro Carra;Marco Garzola;Laura Falaschetti;Claudio Turchetti

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)

Year: 2022 | Conference Paper |

A Low-Rank CNN Architecture for Real-Time Semantic Segmentation in Visual SLAM Applications

Laura Falaschetti;Lorenzo Manoni;Claudio Turchetti

IEEE Open Journal of Circuits and Systems

Year: 2022 | Volume: 3 | Journal Article |

Cited by: Papers (6)

HTML

Real-time semantic segmentation on embedded devices has recently enjoyed significant gain in popularity, due to the increasing interest in smart vehicles and smart robots. In particular, with the emergence of autonomous driving, low latency and computation-intensive operations lead to new challenges for vehicles and robots, such as excessive computing power and energy consumption. The aim of this ...Show More

A Low-Rank CNN Architecture for Real-Time Semantic Segmentation in Visual SLAM Applications

Laura Falaschetti;Lorenzo Manoni;Claudio Turchetti

IEEE Open Journal of Circuits and Systems

Year: 2022 | Volume: 3 | Journal Article |

Tunable and Miniaturized Microwave Filters Using Carbon Nanotube-Based Variable Capacitors

Martino Aldrigo;Mircea Dragoman;Sergiu Iordanescu;George Boldeiu;Paolo Crippa;Giorgio Biagetti;Claudio Turchetti;Luca Pierantoni;Davide Mencarelli;Stephane Xavier;Laurent Gangloff;Afshin Ziaei

IEEE Transactions on Nanotechnology

Year: 2022 | Volume: 21 | Journal Article |

Cited by: Papers (7)

HTML

In this paper, we introduce a novel concept of tunable and miniaturized filtersthat embed, as voltage-controlled elements, state-of-the-art variable capacitors, based on vertically aligned carbon nanotubes (VACNTs). Starting from a theoretical estimation of the voltage-dependent capacitance between two adjacent CNTs, we extended this physics principle to a large matrix of CNTs, suitably placed on ...Show More

Tunable and Miniaturized Microwave Filters Using Carbon Nanotube-Based Variable Capacitors

Martino Aldrigo;Mircea Dragoman;Sergiu Iordanescu;George Boldeiu;Paolo Crippa;Giorgio Biagetti;Claudio Turchetti;Luca Pierantoni;Davide Mencarelli;Stephane Xavier;Laurent Gangloff;Afshin Ziaei

IEEE Transactions on Nanotechnology

Year: 2022 | Volume: 21 | Journal Article |

A Low-Cost, Low-Power and Real-Time Image Detector for Grape Leaf Esca Disease Based on a Compressed CNN

Laura Falaschetti;Lorenzo Manoni;Romel Calero Fuentes Rivera;Danilo Pau;Gianfranco Romanazzi;Oriana Silvestroni;Valeria Tomaselli;Claudio Turchetti

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Year: 2021 | Volume: 11, Issue: 3 | Journal Article |

Cited by: Papers (22)

HTML

Esca is one of the most common grape leaf diseases that seriously affect grape yield, causing a loss of global production in the range of 20%–40%. Therefore, a timely and effective identification of the disease could help to develop an early treatment approach to control its spread while reducing economic losses. For this purpose the use of computer vision and machine learning techniques for recog...Show More

A Low-Cost, Low-Power and Real-Time Image Detector for Grape Leaf Esca Disease Based on a Compressed CNN

Laura Falaschetti;Lorenzo Manoni;Romel Calero Fuentes Rivera;Danilo Pau;Gianfranco Romanazzi;Oriana Silvestroni;Valeria Tomaselli;Claudio Turchetti

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Year: 2021 | Volume: 11, Issue: 3 | Journal Article |

Principal Tensor Embedding for Unsupervised Tensor Learning

Claudio Turchetti;Laura Falaschetti;Lorenzo Manoni

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (1)

HTML

Tensors and multiway analysis aim to explore the relationships between the variables used to represent the data and find a summarization of the data with models of reduced dimensionality. However, although in this context a great attention was devoted to this problem, dimension reduction of high-order tensors remains a challenge. The aim of this article is to provide a nonlinear dimensionality red...Show More

Principal Tensor Embedding for Unsupervised Tensor Learning

Claudio Turchetti;Laura Falaschetti;Lorenzo Manoni

Year: 2020 | Volume: 8 | Journal Article |

An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals

Lorenzo Manoni;Claudio Turchetti;Laura Falaschetti

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (2)

HTML

Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation. However finding appropriate models from high-dimensional data sampled from biosignals is in general unpracticable due to the problem known as the “curse of dimensionality”. Dimensionality reduction, that is representing data in some lower-dimensio...Show More

An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals

Lorenzo Manoni;Claudio Turchetti;Laura Falaschetti

Year: 2020 | Volume: 8 | Journal Article |

A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2019 27th European Signal Processing Conference (EUSIPCO)

Year: 2019 | Conference Paper |

Cited by: Papers (4)

HTML

The aim of this paper is to present a general machine learning approach to the identification of nonlinear systems, using the observed input-output finite datasets. The approach is derived representing the input and output signals in the feature space by the principal component analysis (PCA), thus transforming the nonlinear time dependent identification problem to the regression of a nonlinear in...Show More

A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2019 27th European Signal Processing Conference (EUSIPCO)

Year: 2019 | Conference Paper |

A GPU Parallel Algorithm for Non Parametric Tensor Learning

Claudio Turchetti;Laura Falaschetti

2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

HTML

One rich source of large data sets is the high dimensionality of the data formats known as tensors. Compared to the vector use, learning with tensors is inherently more complex and requires high-performance computing. The aim of this paper is to investigate tensor-based algorithms for regression and classification, i.e. tensor learning, that are suitable to be implemented in parallel architecture ...Show More

A GPU Parallel Algorithm for Non Parametric Tensor Learning

Claudio Turchetti;Laura Falaschetti

2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Year: 2018 | Conference Paper |

Sigma-Delta Based Modulation Method for Matrix Converters

Simone Orcioni;Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

Year: 2018 | Conference Paper |

Cited by: Papers (1)

HTML

This paper presents a new modulation method for matrix converters control, based on Sigma-Delta modulation. The method employs a Sigma-Delta modulator, equipped with a quantizer with time-variable reference levels. A new filter type is also presented that reduces filter quality factor with losses that are present only at resonance.Show More

Sigma-Delta Based Modulation Method for Matrix Converters

Simone Orcioni;Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

Year: 2018 | Conference Paper |

A machine learning method to determine intrinsic dimension of time series data

Claudio Turchetti;Laura Falaschetti

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Year: 2017 | Conference Paper |

Cited by: Papers (1)

HTML

The estimation of Intrinsic Dimension (ID) of data is particularly crucial in the unsupervised learning of nonlinear time series, as it essentially represents the minimum number of parameters to describe the data. The aim of this paper is to give both a new theoretical contribution and a machine learning algorithm that can be used for the ID estimation of time series. Several experimental results ...Show More

A machine learning method to determine intrinsic dimension of time series data

Claudio Turchetti;Laura Falaschetti

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Year: 2017 | Conference Paper |

Machine learning regression based on particle bernstein polynomials for nonlinear system identification

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

Year: 2017 | Conference Paper |

Cited by: Papers (9)

HTML

Polynomials have shown to be useful basis functions in the identification of nonlinear systems. However estimation of the unknown coefficients requires expensive algorithms, as for instance it occurs by applying an optimal least square approach. Bernstein polynomials have the property that the coefficients are the values of the function to be approximated at points in a fixed grid, thus avoiding a...Show More

Machine learning regression based on particle bernstein polynomials for nonlinear system identification

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

Year: 2017 | Conference Paper |

An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti

IEEE Transactions on Cybernetics

Year: 2017 | Volume: 47, Issue: 12 | Journal Article |

Cited by: Papers (24)

HTML

Speaker identification plays a crucial role in biometric person identification as systems based on human speech are increasingly used for the recognition of people. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to decorrelate the voca...Show More

An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti

IEEE Transactions on Cybernetics

Year: 2017 | Volume: 47, Issue: 12 | Journal Article |

Homomorphic Deconvolution for MUAP Estimation From Surface EMG Signals

Giorgio Biagetti;Paolo Crippa;Simone Orcioni;Claudio Turchetti

IEEE Journal of Biomedical and Health Informatics

Year: 2017 | Volume: 21, Issue: 2 | Journal Article |

Cited by: Papers (28)

HTML

This paper presents a technique for parametric model estimation of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal by using homomorphic deconvolution. The cepstrum-based deconvolution removes the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal itself. In this way, only information on MUAP shape ...Show More

Homomorphic Deconvolution for MUAP Estimation From Surface EMG Signals

Giorgio Biagetti;Paolo Crippa;Simone Orcioni;Claudio Turchetti

IEEE Journal of Biomedical and Health Informatics

Year: 2017 | Volume: 21, Issue: 2 | Journal Article |

Discrete bessel functions for representing the class of finite duration decaying sequences

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2016 24th European Signal Processing Conference (EUSIPCO)

Year: 2016 | Conference Paper |

Cited by: Papers (3)

HTML

Bessel functions have shown to be particularly suitable for representing certain classes of signals, since using these basis functions may results in fewer components than using sinusoids. However, as there are no closed form expressions available for such functions, approximations and numerical methods have been adopted for their computation. In this paper the functions called discrete Bessel fun...Show More

Discrete bessel functions for representing the class of finite duration decaying sequences

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti

2016 24th European Signal Processing Conference (EUSIPCO)

Year: 2016 | Conference Paper |

Wireless surface electromyograph and electrocardiograph system on 802.15.4

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti

IEEE Transactions on Consumer Electronics

Year: 2016 | Volume: 62, Issue: 3 | Journal Article |

Cited by: Papers (32)

HTML

This paper presents a flexible low-cost wireless system specifically designed to acquire fitness metrics both from surface electromyographic (sEMG) and electrocardiographic (ECG) signals. The system, that can be easily extended to capture and process many other biological signals as well as the motion-related body signals, consists of several ultralight wireless sensing nodes that acquire, amplify...Show More

Wireless surface electromyograph and electrocardiograph system on 802.15.4

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti

IEEE Transactions on Consumer Electronics

Year: 2016 | Volume: 62, Issue: 3 | Journal Article |

An algorithm for automatic words extraction from a stream of phones in dictionary-based large vocabulary continuous speech recognition systems

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti

2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Year: 2015 | Conference Paper |

HTML

As improvements on acoustic modeling have rapidly progressed in recent years thanks to the impressive gains in performance obtained using deep neural networks (DNNs), language modeling remains a bottleneck for high performance large vocabulary continuous speech recognition (LVCSR) systems. In this paper an algorithm for automatic words extraction from a stream of phones is suggested to be used in ...Show More

An algorithm for automatic words extraction from a stream of phones in dictionary-based large vocabulary continuous speech recognition systems

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti

2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Year: 2015 | Conference Paper |

CARMA: A robust motion artifact reduction algorithm for heart rate monitoring from PPG signals

Alessandro Bacà;Giorgio Biagetti;Marta Camilletti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Luca Rossini;Dario Tonelli;Claudio Turchetti

2015 23rd European Signal Processing Conference (EUSIPCO)

Year: 2015 | Conference Paper |

Cited by: Papers (17)

HTML

Photoplethysmography (PPG) is a non invasive measurement of the blood flow, that can be used instead of electrocardiography to estimate heart rate (HR). Most existing techniques used for HR monitoring in fitness with PPG focus on slowly running alone, while those suitable for intensive physical exercise need an initialization stage in which wearers are required to stand still for several seconds. ...Show More

CARMA: A robust motion artifact reduction algorithm for heart rate monitoring from PPG signals

Alessandro Bacà;Giorgio Biagetti;Marta Camilletti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Luca Rossini;Dario Tonelli;Claudio Turchetti

2015 23rd European Signal Processing Conference (EUSIPCO)

Year: 2015 | Conference Paper |

Improvement of RS-485 performance over long distances using the ToLHnet protocol

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Nicola Ortolani;Claudio Turchetti

2015 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES)

Year: 2015 | Conference Paper |

Cited by: Papers (1)

HTML

This paper presents a technique to extend the transmission range over a single RS-485 cable, without requiring installation of expensive and cumbersome RS-485 repeaters, to virtually unlimited distances, while simultaneously improving transmission speeds between closer nodes. This was accomplished by leveraging the routing capabilities embedded into each node that implements the recently released ...Show More

Improvement of RS-485 performance over long distances using the ToLHnet protocol

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Nicola Ortolani;Claudio Turchetti

2015 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES)

Year: 2015 | Conference Paper |

Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition

Giorgio Biagetti;Paolo Crippa;Alessandro Curzi;Simone Orcioni;Claudio Turchetti

IEEE Journal of Biomedical and Health Informatics

Year: 2015 | Volume: 19, Issue: 5 | Journal Article |

Cited by: Papers (49)

HTML

Sport, fitness, as well as rehabilitation activities, often require the accomplishment of repetitive movements. The correctness of the exercises is often related to the capability of maintaining the required cadence and muscular force. Failure to maintain the required force, also known as muscle fatigue, is accompanied by a shift in the spectral content of the surface electromyography (EMG) signal...Show More

Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition

Giorgio Biagetti;Paolo Crippa;Alessandro Curzi;Simone Orcioni;Claudio Turchetti

IEEE Journal of Biomedical and Health Informatics

Year: 2015 | Volume: 19, Issue: 5 | Journal Article |

A Multi-class ECG Beat Classifier Based on the Truncated KLT Representation

Giorgio Biagetti;Paolo Crippa;Alessandro Curzi;Simone Orcioni;Claudio Turchetti

2014 European Modelling Symposium

Year: 2014 | Conference Paper |

Cited by: Papers (18)

HTML

Automatic classification of electrocardiogram (ECG) signals is of Paramount importance in the detection of a wide range of heartbeat abnormalities as aid to improve the diagnostic achieved by cardiologists. In this paper an effective multi-class beat classifier, based on statistical identification of a minimum-complexity model, is proposed. The classifier is trained by extracting from the ECG sign...Show More

A Multi-class ECG Beat Classifier Based on the Truncated KLT Representation

Giorgio Biagetti;Paolo Crippa;Alessandro Curzi;Simone Orcioni;Claudio Turchetti

2014 European Modelling Symposium

Year: 2014 | Conference Paper |

Multi-port de-embedding methodology based on exponential mapping

Mauro Ballicchia;Claudio Turchetti;Simone Orcioni

2014 9th European Microwave Integrated Circuit Conference

Year: 2014 | Conference Paper |

HTML

This paper proposes a methodology that, starting from a set of calibration measurements picked up at the external ports, allows the de-embbeding of a multi-port transition and the determination of its representative matrix. With this methodology the coupling between internal versus external ports are supposed symmetrical. This hypothesis together with the use of exponential mapping and Baker-Campb...Show More

Multi-port de-embedding methodology based on exponential mapping

Mauro Ballicchia;Claudio Turchetti;Simone Orcioni

2014 9th European Microwave Integrated Circuit Conference

Year: 2014 | Conference Paper |

Multi-port de-embedding methodology based on exponential mapping

Mauro Ballicchia;Claudio Turchetti;Simone Orcioni

2014 44th European Microwave Conference

Year: 2014 | Conference Paper |

HTML

This paper proposes a methodology that, starting from a set of calibration measurements picked up at the external ports, allows the de-embbeding of a multi-port transition and the determination of its representative matrix. With this methodology the coupling between internal versus external ports are supposed symmetrical. This hypothesis together with the use of exponential mapping and Baker-Campb...Show More

Multi-port de-embedding methodology based on exponential mapping

Mauro Ballicchia;Claudio Turchetti;Simone Orcioni

2014 44th European Microwave Conference

Year: 2014 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.