sad - etti 2019...we lower these operations inside llvm’s instruction selection pass. we report...

Annual Symposium of ETTI-B Doctoral School, 2nd Edition

SAD - ETTI 2019 Ediţia a II-a

Simpozionul Anual al Doctoranzilor

în Electronică,Telecomunicaţii şi Tehnologia Informaţiei

Annual Symposium of Doctoral School of Electronics, Telecommunications and

Information Technology

Bucureşti, 4 iulie 2019


CUVÂNT ÎNAINTE

Simpozionul anual al doctoranzilor Şcolii Doctorale ETTI-B, SAD-ETTI,

este un forum de promovare a contribuţiilor stiinţifice obţinute an de an, de

studenţi în programul de doctorat.

Lucrările incluse în program cuprind:

contribuţii originale publicate deja sau

rezultate noi, în curs de publicare.

Prezentarea la simpozion are două componente:

o expunere orală de maximum 3 minute, a principalelor rezultate;

un poster cu rezultate detaliate; la fiecare panou cu poster vor fi discuţii

cu membrii Comitetului stiinţific si cu cei interesaţi.

Acest volum conţine informaţii sumare despre lucrările selectate pentru a II-

a ediţie a simpozionului şi anume:

titlul lucrării,

autorii: doctorand, conducător de doctorat şi (eventual) coautori,

un rezumat de aproximativ150 cuvinte.

Comitetul științific al Simpozionului va acorda diplome pentru:

rezultate semnificative, deja publicate cu doctorandul prim autor,

rezultate consistente obținute în ultimul an și nepublicate încă.

Toate prezentările vor fi incluse în lista de lucrări a doctoranzilor la

categoria Comunicări ştiinţifice nepublicate.

Vă aşteptăm cu mult interes la a II-a ediţie a SAD-ETTI.

4 iulie, 2019 Directorul Şcolii doctorale ETTI-B

Gheorghe Brezeanu


FOREWORD

The Annual PhD Symposium SAD-ETTI is a forum for promoting the

most recent scientific contributions of the doctoral students. The papers in the

program include:

contributions already published or,

original results in press.

The participation in the symposium will have two stages:

an oral exposition of the topic and main results (3 minutes at

maximum)

a poster with detailed results; each poster stand will host discussions

with members of the Scientific Committee and other attendees

interested by the subject.

This volume contains basic information about the papers selected for the second edition of the symposium:

paper title,

authors: PhD student, supervisor and (eventually) co-authors,

an 150 words abstract.

The Scientific Committee will award diplomas for: significant results already published with the PhD student as first

author,

substantial results not published yet.

All presentations will be included in the PhD students’ list of papers in the category Unpublished Scientific Communications.

Looking forward to your participation at the first edition of SAD-ETTI, 4 July 2019 Director of Doctoral School ETTI-B

Gheorghe Brezeanu


Program

Thursday, 4th of July

09:00– 09: 30 Registration (First floor lobby, A building - ETTI)

09:30– 09: 45 Opening ceremony

09:45 – 10:30 Oral session I

10:30 – 10:45 Break

10:45 – 11:45 Oral session II

11:45 – 14:00 Poster session


Evaluation committee

Prof. Ioan Bacivarov

Prof. Mircea Bodea

Prof. Eugen Borcoci

Prof. Gheorghe Brezeanu

Prof. Corneliu Burileanu

Prof. Daniela Colțuc

Prof. Victor Croitoru

Prof. Lidia Dobrescu

Prof. Adriana Florescu

Prof. Simona Halunga

Prof. Bogdan Ionescu

Prof. Ioan Marghescu

Prof. Victor Neagoe

Prof. Constantin Paleologu

Prof. Sever Pașca

Prof. Cristian Ravariu

Prof. Rodica Strungaru

Prof. Paul Svasta

Prof. Dumitru Stanomir

Prof. Paul Șchiopu

Prof. Gheorghe Ștefan

Prof. Răzvan Tamaș

Prof. Adriana Vlad


Summary

1 - Alexandru EFTIMIE, Provisioning using Opendaylight OVSDB into Openstack: experiments

2 - Alexandru E. ŞUŞU, Compiling for the Wide, Energy-Efficient Connex-S Vector Accelerator

3 - Sabin BELU, Faster Canonical Huffman Decoder

4 - Andreea MIHAILESCU, Machine Learning for Intelligent Wireless Gas Sensors Networks, Wearables and Autonomous Unmanned Systems

5 - Andreea MIHAILESCU, Miniaturized Low-Cost Gas Sensors Based on Organic Materials for Room-Temperature Detection of Explosive and Hazardous Gases

6 - Cristian MONEA, An optimized NQR spectrometer for detection of prohibited substance

7 - Andrei Cosmin JITARU, Romanian Word Spotting in Wild Scenarios

8 - Mihai Gabriel CONSTANTIN, Movie genome: alleviating new item cold start in movie recommendation

9 - Cristina POPA, On the Watermarking of Image Compressed Samples

10 - Adrian FOCȘA, Synthetic aperture radar focusing using backprojection compressive sensing

11 - Victor-Bogdan BĂRBULESCU, A Bag of Words approach for remote sensing image classification

12 - Cristian IORGA, A Deep CNN approach with Transfer Learning for image recognition

13 - Paul DIACONESCU, A Highly Configurable Deep Learning Architecture for Hyperspectral Image Classification

14 - Adrian DINCULESCU, Automatic identification of anthropological face landmarks for emotion detection


15 - Laurentiu Cristian BURCOS, Characteristics of SCHIZOPHRENIA based on the interpretation of handwriting using statistical methods and a SVM classifier

16 - Thitimanan DAMRONGSAKMETHEE, Principal Component Analysis and ReliefF cascaded with Decision Tree for Credit Scoring

17 - Mihai BADEA, Can We Teach Computers to Understand Art? Domain Adaptation for Enhancing Deep Networks Capacity to De-Abstract Art

18 - Marius IONESCU, Algorithms of Absorbance and Colorimeter for Measuring Blood Glucose

19 - Manole-Stefan NICULESCU, LabConcept - A new mobile healthcare platform for standardizing patient results in telemedicine

20 - Anca DRAGAN, Andrei ENACHE, An Improved I/O Pin for Serial Communication Interfaces

21 - Ines HUREZ, Galvanically Isolated IGBT Gate Driver with Advanced Protections and A Fault Detection and Reporting Method

22 - Catalin MEIROSU, Influence of the exhaust high frequency noise over the blind spot warning performance

23 - Elena-Diana ȘANDRU, Modeling the Dependencies between Circuit and Technology Parameters for Sensitivity Analysis using Machine Learning Techniques

24 - Aurelian COSTEA, Nanogenerator that relies on piezo properties within PVDF

25 - Constantin ROPOTEANU, Effects of Excitation Waveform on Developing Planar Core Transformers

26 - Rodica-Cristina NEGROIU, Methods for Determining the Leakage Current of Supercapacitors

27 - Niculina BĂDĂLAN, Thermal Model for LED Luminaire

28 – Daniel Ciprian VASILE, Innovative Conductive Mesh Structure for the Protection of Security Electronic Circuits

29 - Alexandru Ioan RUSU, Improved model of a standard LLC resonant converter architecture

30 - Ciprian Mihai COMAN, Electric grid monitoring and control architecture for industry 4.0 system


31 - Bogdan ANTON, Methods of maximizing power efficiency for hybrid vehicles

32 - Razvan-Florentin TRIFAN, Hybrid MU-MIMO Precoding Based on K-Means User Clustering

33 - Madalina-Georgiana BERCEANU, AF relaying in a Massive MU-MIMO OFDM system

34 - Cristina Adelaida HEIMAN, A Novel Design and Simulation of a Ku Broadband Double Ridged Guide Horn Antenna for Satellite Communications

35 - Adrian ANDRONE, Influence of the Substrate and Cell Dimensions on the Radar Cross Section of Closed Loop Type Frequency Selective Surface Unit Cells

36 - Cristina ONEA, Control of slave chaotic dynamics by master current modulation in a chaotic coupled laser system


Provisioning using Opendaylight OVSDB into Openstack: experiments PhD Student: Alexandru EFTIMIE, [email protected]

Supervisor: Eugen BORCOCI Unpublished

Abstract—– Network function virtualization (NFV) and Software Defined networking (SDN) are two recent, complementary and strong technologies that support flexible development or virtual machines in various environments: multitenant/multidomain. NFV and SDN bring powerful capabilities by decoupling software from hardware; a lot of functions that traditionally were being performed by dedicated boxes are now moved to software – NFV. There are still open issues in both SDN and NFV, and many more in the integration between these two technologies. This paper presents an experiment of deploying networks and VMs using Openstack and OVSDB from Opendaylight project: this environment will be used to deploy network and instances on various compute node and to show how tunnels are automatically configured and how OpenFlow is used for forwarding the traffic.

Compiling for the Wide, Energy-Efficient Connex-S Vector Accelerator PhD Student: Alexandru E. ŞUŞU Advisor: Gheorghe M. ŞTEFAN

Published at WPMVP 2019

Abstract—– Wide vector (or SIMD) architectures are gaining popularity because their simple design can scale easily, they have better compilation support these days, and they are energy efficient due to their small control unit. Compiling from sequential C programs using LLVM for the wide Connex-S vector accelerator, a competitive customizable architecture for embedded applications with 32 to 4096 16-bit integer lanes, is challenging. Our back end for Connex-S needs to lower code to emulate efficiently arithmetic operations for non-native types such as 32-bit integer and 16-bit floating point. To simplify the work of the compiler writer we conceive a method to code generate how we lower these operations inside LLVM’s instruction selection pass. We report speedup factors of up to 12.24 when running on a Connex-S processor with 128 lanes, implemented in a Xilinx Zynq-7020 FPGA w.r.t. the loosely integrated dual-core ARM Cortex A9 clocked at a frequency 6.67 times higher, when accelerating linear algebra and computer vision kernels. We also achieve a maximum energy efficiency improvement of 4.11 for native 16-bit integer operations – note, however, that a Connex-S IC implemented in 28 nm technology can achieve an order of magnitude more energy efficiency than our current FPGA implementation. The Connex-S processor can become even more low-power by implementing a simple explicit lane gating power management technique, which is useful to emulate floating point arithmetic operations.


Faster Canonical Huffman Decoder PhD Student: Sabin BELU, [email protected]

Supervisor: Daniela COLTUC Unublished

Abstract - The paper proposes a new canonical Huffman decoder called Advanced Canonical Huffman Decoder, or simply ACHD. Unlike any of the classical implementations that we have surveyed, our ACHD implements a proprietary algorithm that allows very fast decoding of multiple symbols in only one decoding cycle. The big advantage of such approach is extremely high decoding speed obtained with a small price paid for some degraded compression rates. Our Advanced Canonical Huffman Decoder is tested on three types of data – English text, log files, binary files, combined group of files from Silezia corpus and highly compressible 24bit bitmap files - and it is compared with two other public implementations of Huffman codecs. In all the above case, our ACHD is up to 8.83 times faster than its counterparts while the degraded compression ratios are limited to 5% compared to Huff0 and 2-3% compared to zlib's Z_HUFFMAN_ONLY compression. The Huffman Encoder outperforms Facebook's Yan Collet ZHuff decoding by a factor of 2.57 on binaries to 5.67 on highly compressible files and ZLib's Z_HUFFMAN_ONLY decoding implementations by a factor of 3 to 8.83x.

Machine Learning for Intelligent Wireless Gas Sensors Networks, Wearables and Autonomous Unmanned Systems

PhD Student: Andreea MIHAILESCU Supervisors: Cristian RAVARIU, Gabriel SOCOL

Published at INDDAT2019

Abstract – With an estimated of over 26 billion connected devices by 2020, the Internet of Things (IoT) is increasingly becoming more than a growing technological trend, namely a vital part of many solutions to problems of global concern. While connectivity problems are less likely to pose major issues, the real challenge for IoT is to offer devices an intelligent functionality and to make them reliable at all times and in any conditions. This presentation is meant to offer an overview of self-designed and implemented machine learning solutions for intelligent wireless gas sensors networks, wearables and autonomous unmanned water surface biovehicles for offshore plastic waste degradation. The intelligent wireless gas sensors network was developed in our laboratory and is currently undergoing heavy testing. Its main purpose is to serve detecting common greenhouse gases related to industrial activities, to issue level and toxicity alerts and to subsequently predict the nature of the fault and equipment malfunction. Two intelligent wearables were designed for smart farming and for personalized remote healthcare respectively.


Miniaturized Low-Cost Gas Sensors Based on Organic Materials for Room-Temperature Detection of Explosive and Hazardous Gases

PhD Student: Andreea MIHAILESCU Supervisors: Cristian RAVARIU, Gabriel SOCOL

Unpublished

Abstract – Flammable, respectively hazardous gases like methane (CH4) and ammonia (NH3) encounter multiple applications in many industries. Given their classification statements within the Globally Harmonized System of Classification and Labelling of Chemicals (GHS), special safety precautions and environmental considerations are to be taken into account hence the detection of incipient leaks is a requisite. While metal oxides based gas sensors are known to be cheap and extremely effective in detecting CH4 and

NH3 even in low concentrations, their high operating temperatures (200-600⁰C) pose

additional risks, especially in terms of meeting autocombustion conditions. This work proposes low-cost conductometric gas sensors based on organic materials sensitive to CH4 and to NH3 respectively, able to perform at room temperature and in atmospheric pressure conditions. Therefore, a Zn-porphyrin-chalcogenide nanocomposite material was synthesized and used as a detection layer for CH4 sensors while the polyaniline (PANi) conducting polymer was deployed for sensing NH3. Tests have been performed both in dry synthetic air as well as in controlled humidity conditions. These sensors exhibit a prompt and fairly strong response even for concentrations as low as 100ppm of CH4 and of 100ppb of NH3.

An optimized NQR spectrometer for detection of prohibited substances

PhD Student: Cristian MONEA, [email protected] Supervisor: Nicu BIZON

Abstract—Nuclear quadrupole resonance is a radio frequency spectroscopic technique which offers highly specific detection and is used in applications such as chemical analysis and security screening. This paper describes hardware and software solutions for improving the detection of prohibited substances using a nuclear quadrupole resonance scanner. The system’s hardware components are presented and several noise reduction solutions are highlighted, such as fiber optic communication, battery-powered low noise amplifiers, as well as optimized excitation sequences. A multi-criteria signal processing algorithm is implemented to ensure detection in specific scenarios, which, unlike existing solutions, is independent of a response signal model. The system’s performance is compared to other systems and is shown to achieve a minimum detection limit of 1g sodium nitrite and a detection probability of 94%, evaluated on 8000 real data sets, which outperforms existing solutions under specific conditions. Several solutions are proposed to further improve the detection system.


Romanian Word Spotting in Wild Scenarios

PhD Student: Andrei – Cosmin JITARU, [email protected] Supervisor: Bogdan - Emanuel IONESCU

Unublished

Abstract – The goal of this approach is to develop a multi-language word spotting algorithm from video streaming where the lips movement of the speaker are clearly visible and the audio is absent. In order to spot Romanian words, a new data collection for lip movement understanding was developed. Analyzing over 1000 minutes of TV show recordings from YouTube platform, a vocabulary of more than 10 Romanian words resulted. The pre-processed dataset, includes face tracks and the speech transcription with their time boundaries. There is no dataset in other language than well-known English datasets. Between a wild scene and a controlled scene, the LipReading algorithm has to be able to analyze a time-dependent information for final transcript prediction. Thus, a classification model was trained on our data collection. Considering the nowadays usage of borrowed words between several languages, a new research concept on multi-language word spotting prediction was analyzed.

Movie genome: alleviating new item cold start in movie recommendation PhD Student: Mihai Gabriel CONSTANTIN, [email protected]

Supervisor: Bogdan IONESCU Published in User Modeling and User-Adapted Interaction

Abstract—Most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre, cast or user interactions). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a scenario, since the newly added videos lack interactions—a problem technically known as new item cold start (CS). In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by integrating state-of-the-art audio and visual descriptors, which can be automatically extracted from video content and constitute what we call the movie genome. Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition. Results show the benefits of this approach compared to existing approaches.

mailto:[email protected]


On the Watermarking of Image Compressed Samples PhD Student: Cristina POPA, [email protected]

Supervisor: Daniela COLTUC Accepted at ISSCS 2019

Abstract— The theory of compressive sensing (CS) proposes an efficient method of acquiring signals, allowing both sampling and compression to be performed simultaneously. The original signal can then be recovered from the CS measurements using a reconstruction algorithm. Different methods of embedding and extracting digital watermarks can be integrated with this theory. This paper evaluates how the CS parameters influence the watermark quality for a selected watermarking algorithm. It compares three different sensing matrices (Gaussian, Bernoulli and circulant), two orthogonal transforms (DWT and DCT) and two CS reconstruction algorithms i.e., Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP). The results show that a compromise needs to be made in order to obtain the best watermark capacity while preserving a good quality of the CS reconstruction. The use of DCT with a circulant measurement matrix offers the best trade-off between watermark capacity and recovery rates, while the reconstruction quality was best for OMP.

Synthetic aperture radar focusing using backprojection compressive sensing

for monostatic and bistatic configurations PhD Student: Adrian FOCȘA, [email protected]

Supervisor: Mihai DATCU Submitted in Transactions on Aerospace and Electronic Systems

Abstract - In this paper a new methodology called bidimensional mixed compressive sensing back-projection (CS-BP-2D) for synthetic aperture radar image focusing is presented. Backprojection linear operator is derived. Spatial compressibility of the radar image is exploited by constructing the sparsity basis using the back-projection focusing framework and solving the reconstruction problem by means of the orthogonal matching pursuit algorithm. The results are validated using Sentinel-1 SAR images obtained in monostatic and spaceborne transmitter/stationary receiver bistatic configurations. We show that we can focus both monostatic and bistatic SAR images, using less measurements than the classical approach, while preserving the amplitude, phase and position of the targets.


A Bag of Words approach for remote sensing image classification

PhD Student: Victor-Bogdan BĂRBULESCU, [email protected] Supervisor: Mihai DATCU

Unpublished Abstract— Many automatic systems that extract meaningful information from remote sensing image archives have emerged to analyze and classify large image datasets. Those systems have the advantage of being much faster than a specialist who analyzes one image at a time. Most of the automatic systems reproduce the content of remote sensing images using several feature detectors and descriptors, such as Gabor and color histogram. Feature extraction is a time-consuming process and requires high computational power. To overcome this issue, we present a feature extraction method within the bag-of-words (BoW) framework which will be used in learning the semantics of remote sensing images. It is a simple yet powerful method that uses directly the pixel values from a local window as low-level features. This method has a low computational time and can be applied to large data volume. The BoW framework was tested on multispectral images and UC Merced Land Use Dataset.

A Deep CNN approach with Transfer Learning for image recognition PhD Student: Cristian IORGA, [email protected]

Supervisor: Victor-Emil NEAGOE, [email protected] Accepted at the Electronics, Computers and Artificial Intelligence (ECAI) 2019

Conference

Abstract—This paper presents a model of Deep Convolutional Neural Networks (CNN) based on transfer learning for image recognition. This means to use a Deep CNN system pretrained on the large ImageNet dataset of 14 million images and 1000 classes in order to learn feature selection. The results of the pretraining phase are transferred to the problem of classification for the images belonging to the UC Merced Land Use dataset with 21 classes. As benchmark, we have considered a Deep CNN trained with a fraction of the same UC Merced Land Use dataset containing the test images for classification. The experimental results have pointed out the obvious advantage of the Deep CNN with transfer learning (accuracy of 0.87 using pretraining over 0.46 for fully training on the same dataset).

A Highly Configurable Deep Learning Architecture for Hyperspectral Image Classification


PhD Student: Paul DIACONESCU, [email protected] Supervisor: Victor NEAGOE

Published at IEEE 13th International Symposium on Applied Computational Intelligence and Informatics (SACI 2019)

Abstract— We propose a highly configurable architecture for hyperspectral pixel classification. The training optimization algorithm, the loss function and different hyper parameters can be changed. We have studied the tradeoffs and relations between a large number of components. To extend even more the flexibility and scalability of the model, two development environments have been used: Matlab and PyTorch. This approach is valuable for classifications of complex hyperspectral images and illustrates each of the framework capabilities. The classification accuracy score of proposed deep learning model of 89.8% benchmarked against a Support-Vector Machine (SVM) implementation, confirms the quality of the architecture. A training speed performance evaluation had also been made and the results are confirming the GPU much higher speed over the CPU for this architecture, independent of the framework used. Automatic identification of anthropological face landmarks for emotion detection

PhD Student: Adrian DINCULESCU, [email protected] Supervisor: Corneliu FLOREA

Published as A. Dinculescu, A. Baltoiu, C. Strungaru, L. Petrescu, C. Vizitiu, A. Mandu, N. Talpeş and V. Valeanu, “Automatic identification of anthropological face landmarks for emotion detection,” 9 th International Conference on Recent Advances in Space

Technologies (RAST 2019)

Abstract—The paper proposes a new facial landmark system constructed on anthropological principles that is intended to substitute the costly use of Action Units in characterizing facial dynamics, for the purpose of automatic emotion detection. Based on the concept of the anthropometric measurements, it was defined a facial mask with the minimum number of points considered unambiguous and reliable for any face. It is demonstrated the suitability of the method by implementing a lightweight automatic Anthropological Face Landmarks identification algorithm and compare the task of emotional valence classification using the proposed system to explicitly using AU labels. Due to this comparision, it is shown that even in a Proof-of-Concept setting, the system described in the article follows closely the performance of detecting emotions with Action Units.

Characteristics of SCHIZOPHRENIA based on the interpretation


of handwriting using statistical methods and a SVM classifier PhD Student: Laurentiu Cristian BURCOS

Supervizor: Radu DOGARU Abstract— The current experiment aims to establish a possible connection between a mental illness, such as schizophrenia, and subliminal, measurable characteristics of letters from HANDWRITING, using comparative statistical methods as well as search and classification methods belonging to AI. Handwriting interpretation is one of the current directions of AI, but addresses especially normal handwriting, not PATHOLOGICAL features. The study is conducted on 20 patients from which samples of ten “a” letters were gathered, totaling 200 samples. A SVM classifier and statistic methods with significant results have been used. It should also be noted that there are visible details in advanced disease forms, such as the absence of letters or the appearance of productive letters in RHOTACISM as a form of DELIRIUM. Graphology also addresses the pathological writing, in addition to personality traits, but to a much smaller extent. Principal Component Analysis and ReliefF cascaded with Decision Tree for Credit

Scoring PhD Student: Thitimanan DAMRONGSAKMETHEE, [email protected]

Supervisor: Victor NEAGOE Published at 8th Computer Science On-line Conference 2019 (CSOC2019)

Abstract— The objective of this paper is to propose a credit scoring approval model using a feature selection technique performed by Principal Component Analysis (PCA) and ReliefF algorithm followed by a decision tree classifier. As a reference classifier, we have chosen Support Vector Machine (SVM). The performance of our proposed model has been tested using the German credit dataset. The experimental results of the proposed signal processing cascade for the credit scoring lead to the best accuracy of 91.67%, while classifiers without feature selection show the best accuracy of only 75.35%. On the other side, using the same combination of feature selection (PCA and ReliefF) but cascaded with SVM classifier, one has obtained an accuracy of only 85.15%. The experimental results confirm the accuracy of the proposed model, and at the same time they show the importance of feature selection and its optimization for credit scoring decision systems.

Can We Teach Computers to Understand Art?


Domain Adaptation for Enhancing Deep Networks Capacity to De-Abstract Art PhD Student: Mihai BADEA, [email protected]

Supervisor: Constantin VERTAN Published in Image and Vision Computing, 2018

Abstract—Humans comprehend a natural scene at a single glance; painters and other visual artists, through their abstract representations, stressed this capacity to the limit. The performance of computer vision solutions matched that of humans in many problems of visual recognition. In this paper, we address the problem of recognizing the genre (subject) in digitized paintings using Convolutional Neural Networks (CNN) as part of the more general dealing with abstract and/or artistic representation of scenes. Initially we establish the state of the art performance by training a CNN from scratch. In the next level of evaluation, we identify aspects that hinder the CNNs’ recognition, such as artistic abstraction. Further, we test various domain adaptation methods that could enhance the subject recognition capabilities of the CNNs. The evaluation is performed on a database of 80,000 annotated digitized paintings, which is tentatively extended with artistic photographs, either original or stylized, in order to emulate artistic representations. Surprisingly, the most efficient domain adaptation is not the neural style transfer. Finally, the paper provides an experiment-based assessment of the abstraction level that CNNs are able to achieve.

Algorithms of Absorbance and Colorimeter for Measuring Blood Glucose PhD Student: Marius IONESCU, [email protected]

Supervisor: Rodica STRUNGARU Published at ATEE 2019

Abstract— The main subject of paper is about methods and algorithms for detection and measuring blood glucose through non-invasive methods. The article aims to illustrate a new prototype based on spectroscopic methods, that can monitorize blood glucose. The problems addressed center on the absorbance of light in the matter and non-invasive blood glucose detection using near infrared technology by colorimetric interpretation of the values transmitted by a sensor with six channel transmission. The paper proposes an interface that analyzes and calculates the colorimetric values transmitted by a development board through two NIR sensors: one of light absorption and one of colorimetry. The blood glucose measurement algorithm is based on the absorption of the amount of monochromatic light passing through the tissues to the blood containing capillaries.

LabConcept - A new mobile healthcare platform for standardizing patient results in telemedicine

PhD Student: Manole-Stefan NICULESCU, [email protected]


Supervisor: Rodica STRUNGARU Not published

Abstract—The paper presents a new platform that is designed to embed all the patient’s healthcare information into a mobile software application. The novelty consists in standardizing the laboratory and the point-of-care results into a QR code that is printed on every result bulletin issued and given to the patient after its blood assays are performed. The software platform can load all the assayed results using a camera or a barcode reader attached to the host in order to be sent to the doctor’s mobile application to be interpreted. LabConcept software platform can analyze the evolution of an assay on a predefined period, record comments regarding the patient’s treatment and store the entire patient’s history. In this manner, sending all this information via LabConcept will supply an easy and safe end-to-end real-time patient-doctor communication channel and improve the patient’s monitoring and treatment procedures.

An Improved I/O Pin for Serial Communication Interfaces

PhD Student: Anca DRAGAN, [email protected] PhD Student: Andrei ENACHE, [email protected]

Supervisor: Gheorghe BREZEANU Published at: Romanian Journal of Information Science and Technology

(ROMJIST, Vol 22, No 2, 2019)

Abstract—Modern mid-speed serial communication interfaces are required to operate in a wide range of power supply voltages, while achieving transmission rates in the order of Mbps or tens of Mbps. An important factor in determining the maximum bit rate is the I/O of the interface. An I/O pin with an improved digital push-pull output buffer is designed and implemented in a 0.18µm CMOS EEPROM process. The pin acts as an interface between an internal low voltage and an external, higher level voltage. The circuit can operate at power supply voltages from 1.6V to 5.6V and can be used in an I2C UFm interface at 5 Mbps and an SPI interface at 10 Mbps. These performances were achieved through topology changes to a classic digital output buffer.

Galvanically Isolated IGBT Gate Driver with Advanced Protections and A Fault Detection and Reporting Method

PhD Student: Ines HUREZ, [email protected] Supervisor: prof. dr. ing. Gheorghe BREZEANU


Published in ROMJIST vol. 22, no. 2

Abstract—The aim of this paper is to present a fault detection and reporting technique for galvanically isolated Insulated Gate Bipolar Transistor (IGBT) gate drivers with advanced protection functions, such as soft shutdown and Active Miller Clamp. The technique provides robust transmission of Under Voltage Lock Out (UVLO) and Desaturation (DESAT) events, while the protection methods prevent improper functionality that can damage the IGBT. The proposed circuit was verified by means of simulations, and implemented in a standard 0.25μm CMOS BCD technology, as part of a galvanically isolated IGBT gate driver. Experimental results highlight proper reporting of UVLO/DESAT faults and validate the functionality of the protection functions.

Influence of the exhaust high frequency noise over the blind spot warning performance

PhD Student: Catalin MEIROSU Supervisors: Gheorghe BREZEANU, Mircea OPREAN

Not Published Abstract—The objective of this paper is to show the limitations of the ultrasound sensors used for the Blind Spot Warning detection. The paper oversees all possible driving scenarios and underlines the isolate cases where an ultra-sound-based system would be ineffective for this particular ADAS application. The limitations of the Ultrasound based Blind Spot Warning system was not yes studied. Also, the Exhaust ultrasound influence over the Blind Spot Warning system was never studied as there are not many car makers that use this system. The slide sums-up the 2-years work on a prototype vehicle and the new, currently sold vehicle. Even though the ultrasound system is a cheap method in ensuring the Blind Spot warning performance, this type of sensors have some limitations due to the characteristics. The paper studies these limitations and comes with the countermeasure to minimize the bad influence of the exhaust noises and other exterior factors on the Blind spot warning detection.

Modeling the Dependencies between Circuit and Technology Parameters for Sensitivity Analysis using Machine Learning Techniques

PhD Student: Elena-Diana ȘANDRU, [email protected] Supervisor: Corneliu BURILEANU


Accepted at SMACD2019

Abstract—The sensitivity of integrated circuit parameters regarding manufacturing process variation represents a very important ongoing topic in the semiconductor industry. Establishing the functional relationship between them at an early stage, i.e. simulation, would create an advantage in terms of circuit improvement and eventually high production yield. This paper presents a methodology for finding the influence of technology parameters (i.e. Process Control Monitor parameters) on device performance. The methodology is based on Machine Learning algorithms and Bayesian Optimization framework with the purpose of modelling the functional dependencies between technology and circuit parameters. The experimental results prove that the device performance is highly sensitive to technology parameters variation and this dependency can be modelled.

Nanogenerator that relies on piezo properties within PVDF PhD Student: Aurelian COSTEA, [email protected]

Coauthors: Paul Şchiopu and Marian Vlădescu Supervisor: Paul SCHIOPU

Published

Abstract—One of the most important semicrystalline polymers capable to generate piezoelectricity whilst a pressure or mechanical stress it is applied on it is polyvinylidene

fluoride or simply PVDF. All four crystalline phases α, β, ɣ and δ are dependent of the

chain conformation structure, fig.1. The α phase is an non polar phase, β and ɣ being

polar phases. In PVDF case, piezoelectricity effect occur due to the β and ɣ phase

formation. PVDF (polyvinylidene fluoride) is one of the most interesting material used in nanogenerator preparation due to its flexibility, bio-compatiable, nontoxic in nature, also due to its good ferroelectric, piezoelectric and pyroelectric properties. Output voltage can be generated from this nanogenerator by applying mechanical force or vibration on it. Due to its biocompatiable nature, it can be placed inside our human body and from inside human body motion it generates voltage, nanogenerator is expected to be applied for various applications where the periodic kinetic energy exists, such as to the muscle movement by the beat of a heart or inhalation of lung in a small scale, also in different application like LED light glow, capacitor charging, wireless transmission, etc.

Effects of Excitation Waveform on Developing Planar Core Transformers PhD Student: Constantin ROPOTEANU, [email protected]

Supervisor: Paul SVASTA


To be published at SIITME 2019

Abstract— The development of magnetic materials aimed to work in high frequency domain, became a challenge for the power supply designers who expect to obtain the input/output power ratio near unity. An efficient power conversion process involves the identification and reduction of all the loss sources in the components, either the magnetic or semiconductors. An isolated conversion solution becomes prior to analysis the power transformer. Recently, good enough results were obtained in high frequency approaches regarding the use of planar magnetic components. Losses that involve magnetic cores refer to eddy currents and hysteresis. Available results regarding the core losses in magnetic materials are subject to sine wave excitation. The study of core losses is obtained for two planar transformers, according to the excitation waveform effects. The practical approach results become more important taking into consideration that the working frequency is near 1 MHz.

Methods for Determining the Leakage Current of Supercapacitors

PhD Student: Rodica-Cristina NEGROIU, [email protected] Supervisor: Paul SVASTA

Published at ISSE2018

Abstract—Supercapacitors or Electrochemical Double Layer Capacitors (EDLC’s) have become increasingly used in nowadays applications, for example in the automotive industry. This passive electronic component category, due to their advantages, has been compared to conventional batteries which present a particular interest such as the high number of charge / discharge cycles, low charging time, sustaining high charge / discharge currents and so on. Alongside these advantages they have a series of disadvantages that cause, for users, to become reluctant in using them in different applications. Thus, the relative high values of equivalent series resistance (ESR) and the leakage current, as results of the supercapacitor self-discharge, compared to the batteries, represent disadvantages of the EDLCs. The present paper will focus on the leakage current investigation at different temperatures in a large range of temperature values (from -20ºC to + 50ºC). It is important to monitor the variation of this parameter with temperature in order to evaluate the supercapacitors loss and identify how the supercapacitor influences the behavior of the circuit in a certain application, especially taking into consideration that supercapacitor data sheets do not contain such information.

Thermal Model for LED Luminaire PhD Student: Niculina BĂDĂLAN (DRĂGHICI), [email protected]

Supervisor: Paul SVASTA To be published at SIITME 2019


Abstract— Various electrical and thermal models for LED light fixture can be found in literature but in this paper a virtual model and an equivalent thermal scheme were implemented and thermal simulation were built based on the assumption that there will be two ambient temperatures: one at the heat sink level and a different one above the primary lens of the LED. This difference is due to the fact that the LED is part of a lighting assembly that features a lens/light diffuser that prevents the exchange of fluids between the primary lens of the LED and the exterior of the assembly. It has been found that in order to obtain more realistic models it is advisable to take into account the difference between the values of the fluid parameters above the primary lens and above the heat sink respectively.

Innovative Conductive Mesh Structure for the Protection of Security Electronic Circuits

PhD Student: Daniel-Ciprian VASILE, [email protected] Supervisor: Prof. Paul Mugur SVASTA, Ph. D

Published at ESTC 2018

Abstract— Modern communications are characterized by high speed and large amount of data due to the rapid advance in technology. Home appliances, automotive electronics and portable gadgets are connected to the Internet in order to facilitate communications between them. This is also the result of the human’s need to keep in touch with the surrounding world. These interactions are useful as long as the information between parties is kept securely. Cryptography protects communications and stored data but the processing units must also be protected against specialized attacks, like physical intrusions. The attackers may obtain secret data and information about the cryptographic processes. This knowledge can be further used in cryptanalytic and side channel attacks. This paper proposes an innovative conductive structure used for protection of electronic circuits against intrusion attacks. This conductive structure is made of a very fine mesh and is connected to an active tamper detection circuit that analyzes the signals inside the mesh and takes the decision in case of intrusion.

Improved model of a standard LLC resonant converter architecture PhD Student: Alexandru Ioan RUSU, [email protected]

Supervisor: Adriana FLORESCU Published at ATEE 2019


Abstract— This paper presents a developed model of the standard LLC resonant converter, whose advantages consist in lower switching losses, a prolonged functioning time at a higher resonant frequency, a wider input voltage range, an improved efficiency and a greater compatibility with other power electronics devices, compared with the respective standard converter. The novel circuit was initially simulated in Multisim 14.1, after which it was implemented in the design environment EAGLE 6.3.0 and Altium to verify that the new layout is without any structural errors. The PC oscilloscope PicoScope 2206B was the measuring instrument used to test the completed hardware LLC architecture. The obtained results demonstrate that the new proposed architectural model in this paper has a 7% increase in efficiency and a more than 10 times larger output voltage value than the standard LLC resonant converter model.

Electric grid monitoring and control architecture for industry 4.0 systems PhD Student: Ciprian Mihai COMAN, [email protected]

Supervisor: Adriana FLORESCU Published at ISFEE 2018

Abstract— The existing papers, technologies and products make possible the implementation of large-scale integrated systems that allow monitoring and control of an electric grid. Such a system presents several major advantages, like the ability to closer match the production and consumption of electricity in real time, realistically adjust the dimensions of infrastructure based on measured power requirements, pin-point problems in the grid etc. Furthermore, this system can be adapted and integrated into industry 4.0 systems, to provide control and visibility of the enterprise operations at low level. Nowadays, most of the tools and equipment use electric energy as the main power source or for control, while renewable energy production capabilities are installed in factories. This article presents the requirements for smart metering in an industry 4.0 system along with a proposed architecture for the private grid monitoring and control system. In our experiment, we adapt and integrate the latest transmission encryption methodologies over a wired network to ensure data security in an industrial environment. We bring contributions to the presentation layer in order to display the data in a simple to understand and meaningful way.

Methods of maximizing power efficiency for hybrid vehicles

PhD Student: Bogdan ANTON, [email protected] Supervisor: Adriana FLORESCU

Published in Revue Roumaine des Sciences Techniques - Serie Electrotechnique et Energetique, 2018


Abstract— This paper proposes a new system that gathers numerous methods and devices which help improving the energy management for both conventional vehicles (with a greater benefit for those equipped with a start-stop system), as well as for hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs), by using energy harvesting devices (including regenerative braking) and auxiliary energy storage devices (AESDs) that consist in a bank of supercapacitors combined with a pack of Li-ion cells, designed to improve the efficiency, convenience and reliability of modern cars. Apart from the thermal energy available mainly from the exhaust system, electromagnetic and solar energy sources can be exploited, in combination with a maximum power point tracking (MPPT) converter. Since the thermal efficiency of the internal combustion engines (ICEs) is pretty low (below 40 %) for both petrol and Diesel powered vehicles, a part of the wasted energy can be recovered, stored for a certain amount of time and then used for different purposes.

Hybrid MU-MIMO Precoding Based on K-Means User Clustering PhD Student: Razvan-Florentin TRIFAN, [email protected]

Supervisors: Andrei-Alexandru ENESCU, Constatin PALEOLOGU Partially published at VTC Spring 2019

Abstract— Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) systems have been extensively investigated over the last years from both theoretical and practical perspectives. The low complexity Linear Precoding (LP) schemes for MU-MIMO are already deployed in Long Term Evolution (LTE) networks, however, they do not work well for users with strongly correlated channels. Non-Linear Precoding (NLP) and hybrid precoding schemes were proposed as alternatives in the standardization phase for 3rd Generation Partnership Project (3GPP) 5G New Radio (NR). NLP schemes have better performance, but their complexity is prohibitively high. Hybrid schemes, which combine LP schemes to serve users with separable channels, and NLP schemes used for users with strongly correlated channels, can help reducing the computational burden while limiting the performance degradation. In this paper a new user selection algorithm based on the statistical K-means clustering is proposed to find the optimum set of users that can be co-scheduled through LP schemes. Its performance is then tested for MU-MIMO systems employing hybrid precoding schemes for realistic user distributions.

AF relaying in a Massive MU-MIMO OFDM system

PhD Student: Madalina-Georgiana BERCEANU, [email protected] Supervisor: Simona HALUNGA

Published at SIITME2018


Abstract— Due to the market demands, current communication systems evolve rapidly from existing 4G standard to a future 5G, that implies higher capacity and flexibility. In 4G systems the orthogonal frequency-division multiplexing (OFDM) modulation and multiple-input multiple-output (MIMO) technique are used to enable higher data rates for mobile users, while in 5G it is expected to further increase the performance by using a significantly higher number of antennas (massive MIMO) and cooperative relays for solving the problem of coverage and capacity at the cell border. In this paper massive multiuser MIMO (MU-MIMO) systems, when 10 to 100 antennas are used, without relay nodes, to evaluate the bit error rate (BER) as a function of the Signal to Noise Ratio (SNR) or number of active users. Then, the system is further developed, by introducing amplify-and-forward (AF) relays as intermediaries between the transmitter and the receiver to see if the performances are improved.

A Novel Design and Simulation of a Ku Broadband Double Ridged Guide Horn

Antenna for Satellite Communications PhD Student: Cristina Adelaida HEIMAN, [email protected]

Supervisor: Razvan TAMAS Published at PIERS 2019

Abstract— This paper presents the design and the simulation of a modified Ku broadband double ridged guide horn antenna (DRGH) feed by a coaxial input. Telecommunication satellite horn antennas are need due to the additional necessary bandwidth. Advantages of using this type of antenna are: a wide bandwidth to operate over, adequate directivity, high power and its simple construction. In order to have a clear view regarding the effects of different parameters, Ansoft HFSS was used for the simulations of the DRGH in the range of operating frequencies. The antenna performance is evaluated in terms of gain, directivity, polarization, radiation patterns and voltage standing wave ratio (VSWR). The results show that the parameters near feeding point (the initial distance between ridges, the distance between the center of the probe and the cavity, the radius of inserted probe) play a significant role in controlling VSWR, gain, and in shaping the radiation pattern for high frequencies. By comparing it with the conventional version, the proposed antenna comes with a considerably better performance, smaller physical dimensions and less weight.

Influence of the Substrate and Cell Dimensions on the Radar Cross Section of

Closed Loop Type Frequency Selective Surface Unit Cells PhD Student: Adrian ANDRONE Supervisor: Razvan D. TAMAS

Accepted for publishing at ISSCS 2019


Abstract—In this paper, we propose a new method to improve the RCS (Radar Cross Section) of a Frequency Selective Surface (FSS) by optimizing the cell spacing. The study includes a comparative analysis in terms of substrate i.e. FR4 and alumina, with 1mm and 2mm thicknesses. Simulations for FSS unit cells with square and hexagonal shaped loops, resonating on the 2GHz, 2.5GHz and 3GHz were performed. The results indicate that better RCS values were obtained for alumina substrate than using FR4 substrate.

Control of slave chaotic dynamics by master current modulation in a chaotic coupled laser system

PhD Student Cristina ONEA, Co-authors: I. R. ANDREI, P. E. STERIAN, I. IONITA, M. L. PASCU

Supervisor: Paul E. STERIAN Published at OAM-RC

Abstract—A complex chaotic behaviour can be found in nonlinear dynamics of a semiconductor laser (SL) operated under external optical feedback conditions. In this work, two external-cavity semiconductor laser (ECSL) systems operated in low-frequency fluctuations (LFF) chaotic regime [1] were optically coupled into a master - slave synchronization scheme. Injection current of master system is modulated at frequencies close to, but different from, the natural LFF oscillation frequency of master and slave, as well. Driving master laser induces in both laser emissions LFFs with two dominant frequencies. Synchronization state between chaotic coupled laser system and external modulator is studied using statistical analysis of power dropouts in laser emission of the two chaotic lasers [2].Results show that modulation at frequencies in the domain bonded by natural LFF oscillation frequencies induces, as control effect on slave chaotic dynamics, the clustering of dropouts periods on two frequencies: the driven and natural ones. If modulation frequency is not in the frequency domain, this has no control effect on slave chaotic dynamics; it only has the role to group temporal periods, but at other values than modulation. Results allow to better understand the mechanisms that contribute to synchronization regimes stability and chaotic dynamics control of the coupled systems which contain – each - a semiconductor laser.

Ph.D. Students Index ANDRONE Adrian,


ANTON Bogdan, BADALAN Niculina, BADEA Mihai, BARBULESCU Bogdan, BELU Sabin, BERCEANU Madalina, BURCOS Cristian, COMAN Ciprian Mihai, CONSTANTIN Gabriel, COSTEA Aurelian, DIACONESCU Paul, DINCULESCU Adrian, DRAGAN Anca ENACHE Andrei, EFTIMIE Alexandru, FOCSA_Adrian, HEIMAN Cristina Adelaida, HUREZ Ines, IONESCU Marius, IORGA Cristian, JITARU Cosmin, MEIROSU Catalin, MIHAILESCU Andreea, MONEA Cristian, NEGROIU Rodica, NICULESCU Manole, ONEA Cristina, POPA Cristina, ROPOTEANU Constantin, RUSU Alexandru, SANDRU Diana,

SUSU Alexandr, THITIMANAN Damrongsakmethee, TRIFAN Razvan, VASILE Daniel Ciprian,


Supervisors Index BIZON Nicu, BORCOCI Eugen, BREZEANU Gheorghe, BURILEANU Corneliu, COLŢUC Daniela, DATCU Mihai DOGARU Radu FLOREA Corneliu FLORESCU Adriana, HALUNGA Simona, IONESCU Bogdan, NEAGOE Victor, PALEOLOGU Constantin, RAVARIU Cristian, SCHIOPU Paul, ŞTEFAN Gheorghe, STERIAN Paul, STRUNGARU Rodica, SVASTA Paul, TAMAŞ Razvan, VERTAN Constantin

sad - etti 2019...we lower these operations inside llvm’s instruction selection pass. we report...

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