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CS-ICT Project Report Part VI

Project New ICT Compressive sensing based trends applied to: multimedia, biomedicine and communications (CS-ICT)

Project Leader Prof. Dr Srdjan Stanković

Lead institution Faculty of Electrical Engineering, University of Montenegro

Funded by the Ministry of Science of Montenegro

under the

World Bank loan

Grant Agreement number: 01-1002

Periodic report 1st □ 2nd □ 3rd □ 4th □ 5th □ 6th □

Period covered From To

1.12.2016. 31.5.2017.

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University of Montenegro Ministry of Science of Montenegro

World Bank

Publishable Executive summary

[This section should be of suitable quality to enable direct publication by the Ministry of Science in Montenegro. Please ensure that it is set out and formatted so that it can be printed as a stand-alone paper document not exceeding 1 page. In addition, provide this section in Montenegrin language as a separate document.] Please include a summary description of the project objectives, a description of the work performed since the beginning of the project, a description of the main results achieved so far, the expected final results and their potential impact and use (including the socio-economic impact and the wider societal implications of the project so far). You should update this publishable summary at the end of each reporting period.

During the last six months of the project implementation, the focus was on the research and theoretical developments in the compressive sensing field. In the period covered with this report, the CS-ICT project team published 18 scientific papers and among them 7 papers in the top ranked international journals from the SCI list, and 11 papers at the international conferences. The dissemination activities are done mainly through the participation to the regional and international conferences. The collaboration is made with the University of Heidelberg. Through the Workshop that is organized in this period, the project results and CS-ICT research group is presented to the eminent researchers from the top ranked world universities. The collaboration is made with the researchers from the University of Minnesota, Moscow State University, Imperial College and the Ghent University. Dissemination activities were covered by 5 presentations during the scientific conferences. During the presentations, project ideas, objectives, deliverables and results were presented to the wider scientific community. Research, Innovation, Development: In the last six month of the project implementation, the following activities have been done (some of them have been initiated earlier and are finished during the last six months):

1. A method for automatic data driven CS area selection is proposed. It is shown that by using the

proposed method, we have been able to reduce the optimization requirements, resulting in highly concentrated time-frequency distributions. I. Volarić, V. Sučić, S. Stanković, “A Data Driven Compressive Sensing Approach for Time-Frequency Signal Enhancement,” Signal Processing, accepted for publication, 2017.

2. An adaptive three-mode system based on Go-Back-N (GBN) protocol is analyzed within one of the published papers. An ideal mode selection procedure based on a-priori known packet error probability is defined. R. Vojinović, M. Daković, "Optimization of adaptive three-mode GBN scheme control parameters," Radioengineering, Vol. 26, No. 2, September 2017.

3. Compressive Sensing, as an emerging technique in signal processing together with its’ common

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applications is reviewed in one of the papers. The basic ideas and motivation behind this approach are provided in the theoretical part. The commonly used algorithms for missing data reconstruction are presented. Some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well. A. Draganic, I. Orovic, S. Stankovic, "On some common compressive sensing recovery algorithms and applications - Review paper," Facta Universitatis, Series: Electronics and Energetics, accepted for publication, 2017.

4. Classification of interfering signals that belong to different wireless standards is done in the journal paper published in this period: A. Draganic, I. Orovic, S. Stankovic, X. Li, Z. Wang, "An approach to classification and under-sampling of the interfering wireless signals," Microprocessors and Microsystems, Volume 51, June 2017, Pages 106–113, doi: 10.1016/j.micpro.2017.04.010.

5. The watermark detection procedure for images corrupted by impulsive noise is proposed. The procedure is based on the compressive sensing (CS) method for the reconstruction of corrupted pixels. B. Lutovac, M. Daković, S. Stanković, I. Orović, "Watermark Detection in Impulsive Noise Environment Based on the Compressive Sensing Reconstruction,” Radioengineering, vol. 26, no. 1, pp. 309-315, DOI: 10.13164/re.2017.0309, ISSN: 1805-9600, 2017.

6. A method for denoising and reconstruction of sparse images based on a gradient-descent algorithm. It is assumed that the original (non-noisy) image is sparse in the two-dimensional Discrete Cosine Transform (2D-DCT) domain. I. Stanković, I. Orović, M. Daković, S. Stanković, "Denoising of sparse images in impulsive disturbance environment,” Multimedia Tools and Applications, pp 1–21, First Online: 22 February 2017.

7. The chapter related to the sparse signal processing is published: LJ. Stanković, M. Daković, S. Stanković, and I. Orović, “Sparse Signal Reconstruction - Introduction ,” Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley, 2017.

8. A new generalized concept of cognitive inspired learning motivated by the basic principles used in the compressive sensing theory is proposed. The aim was to introduce a new perspective on the learning process which uses sparsity as a main premise. S. Stankovic, I. Orovic, "Cognitive Inspired Learning based on the Compressive Sensing Postulates", 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

9. The development of a system that would ease the diagnosis of heart diseases would also fasten the work of the cardiologic department in hospitals and facilitate the monitoring of patients with portable devices. A tool for ECG signal analysis which is designed in MATLAB is proposed in the paper: Z. Vulaj, A. Draganic, M. Brajovic, I. Orovic, "A tool for ECG signal analysis using standard and optimized Hermite transform," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

10. An under-sampled signal describing the level of CO2 in the table grape cold chain logistic is observed in one of the papers. The aim was to recover the missing information by applying the Compressive Sensing approach. The reduced number of measurements will lead to decreased number of required sensors, reduced storage demands and will speed up the communication. A. Draganic, I. Orovic, S. Stankovic, X. Zhang, X. Wang, "Compressive Sensing Approach in the Table Grape Cold Chain Logistics," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

11. A procedure for the identification of the image source and content by using the Public Key Cryptography Signature (PKCS) is proposed. The procedure is based on the PKCS watermarking of the images captured with numerous automatic observing cameras in the Trap

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View cloud system. A. Draganic, M. Maric, I. Orovic, S. Stankovic, "Identification of Image Source Using Serial-Number-Based Watermarking under Compressive Sensing Conditions", 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017.

12. A method for improving the reconstruction of noisy images using overlapping blocks is proposed. It is an improvement of the methods for reconstruction algorithms that are based on the detection of the corrupted pixels in spatial domain. I. Stankovic, M. Dakovic, I. Orovic, “Overlapping Blocks in Reconstruction of Sparse Images”, 40th International Convention on Information and Communication Technology, Electronics and Microelectronics , MIPRO 2017.

13. A compressed sensing approach for the reconstruction of 2D sparse signals with missing samples is presented. The statistical behavior of transform coefficients in the case of randomly undersampled signals is exploited as the basis of a simple algorithm for the signal support detection. M. Brajović, I. Orović, M. Daković, S. Stanković, “The Reconstruction of 2D Sparse Signals By Exploiting Transform Coefficients Variances”; IEEE EUROCON 2017, 6 - 8 July 2017, Ohrid, Macedonia.

14. An architecture for hardware realization of a system for sparse signal reconstruction is proposed. The threshold based reconstruction method is considered. The algorithm is modified in this paper with an aim to reduce the system complexity, and to provide easier hardware realization. I. Orovic, A. Draganic, N. Lekic, S. Stankovic, “A System for Compressive Sensing Signal Reconstruction” IEEE EUROCON 2017, 6 - 8 July 2017, Ohrid, Macedonia

15. A new algorithm implemented as combination of gradient based and single iteration reconstruction algorithms for compressively sensed sparse signals is proposed. S. Stankovic, S. Vujovic, I. Orovic, M. Dakovic Lj. Stankovic, “Combination of Gradient Based and Single Iteration Reconstruction Algorithms for Sparse Signals” IEEE EUROCON 2017, 6 - 8 July 2017, Ohrid, Macedonia.

16. Rounding error in the discrete Fourier transform calculated with fast fixed-point algorithms was topic in the paper: M. Dakovic, Lj. Stankovic, B. Lutovac, I. Stankovic, “On the Fixed-point Rounding in the DFT” IEEE EUROCON 2017, 6 - 8 July 2017, Ohrid, Macedonia

17. The application of the median form ambiguity function in direct sequence spread spectrum modulated signals denoising is proposed in the paper: A. Draganić, I. Orovic, S. Stankovic, “Spread-spectrum-modulated signal denoising based on median ambiguity function” , ELMAR 2017 conference, Zadar, Croatia.

18. Computer based recognition and detection of abnormalities in ECG signals is proposed. For this purpose, the Support Vector Machines (SVM) are combined with the advantages of Hermite transform representation. Zoja Vulaj, Miloš Brajović, Anđela Draganić, Irena Orović, “Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine”, ELMAR 2017 conference, Zadar, Croatia

The potential impact and use of main results achieved so far:

One significant part of the achievements in the last project implementation period are related to the development of new approaches for analysis and monitoring of biomedical signals, particularly the ECG signals, which can bring important contributions to the field of medical diagnosis. Particularly, the developed approaches are combined in a virtual instrument / software environment to be more accessible to users.

The results achieved in the sector of communications and signals could be efficiently used

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in communication channel for dealing with noise disturbances which are almost always challenging and inevitable. A special emphasize is made on the design of a system for classification of different wireless signals which can solve the problem of source identification in wireless communication applications.

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University of Montenegro Ministry of Science of Montenegro

World Bank

Overview of project objectives for the period

Please provide a short overview of the project objectives for the reporting period in question, as stated in Annex 1 of the Grant Agreement. (These objectives are required so that this report is a stand-alone document). Suggested length 1 page maximum.

The main objectives planned for this reporting period were:

- Development of new reconstruction algorithms and improving the existing ones, with special attention to the application of the developed solutions in biomedical and communication signal analysis and application of the Hermite transform for processing of such signals.

-The goal for this period was testing the possibility to apply the developed solutions in practical applications (e.g. applications in watermarking, biomedical signals, etc.)

-Several study visits were planned for this period, which are performed successfully.

-Participation to the international conferences was planned – during the previous semi-annual period, the project team participated to the several conferences.

-Dissemination of the project ideas and results – dissemination done through participation to the conferences and study visits.

-Preparation of the journal and conference papers (7 journal papers were prepared in the last six months, including the novel results and applications developed within the CS-ICT project. Also, 11 conference papers have been prepared and 5 of them are presented at the international conferences).

- Contacts with academic partners – INP Grenoble, University of Minnesota, Moscow State University, Imperial College and the Ghent University.

Meetings and events:

- PARTICIPATION TO THE MECO 2017 CONFERENCE 11th - 15th June 2017, Bar, Montenegro CS-ICT project team members participated to the 6th Mediterranean Conference on Embedded Computing-MECO 2017. MECO 2017 is a continuation of very successful MECO events, at which CS-ICT project team members participate every year. Many contacts and collaborations were initiated with the researchers from the region and from the world during the participation at the MECO conferences. This year, participation at the Conference was the opportunity for dissemination of the CS-ICT project idea and results, and promotion of the project within researchers from foreign universities. The research results produced during the past several months of the CS-ICT project implementation, are presented. Also, several research papers were presented.

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-WORKSHOP ON COMPRESSIVE SENSING METHODS AND APPLICATIONS

25th - 28th May 2017, Budva, Montenegro The Workshop on Compressive Sensing methods and applications was organized as a four-day event bringing together the eminent researchers from top ranked world universities (University of Minnesota, Moscow State University, Imperial College London, University of Groningen, Ghent University, etc.). The main purpose of the workshop was presentation of the results achieved during the implementation of the project with the aim to open possibilities for further collaborations based on new ideas within the new projects and funds. The researchers from the University of Montenegro presented their research results, achieved during the CS ICT project implementation - results in digital image and video reconstruction and recovering, results achieved in the area of communications and radar signals, then the results achieved in biomedical signals analysis, the multimedia web platform for CS learning - ECHO platform (that is developed together with the partners from University of Ljubljana and the company Alpmedia from Slovenia), and software for compressive sensing reconstruction. Visiting participants presented the state of the art research in the field related to the project topic with the aim to identify further possibilities for advancement in the area of Compressive sensing, and to provide the knowledge transfer between the participating institutions, aiming at the extension of research activities through the new joint collaborations in the forthcoming period. The presentations of the participants can be found on the links below: Prof. Dr Srdjan Stanković, Prof. Dr Irena Orović - New ICT CS based trends applied to: Multimedia, Biomedicine and Communications Prof. Dr Georgios Giannakis - Adaptive Sketching and Validation for Learning from Large-Scale Data Prof. Dr Aleksandra Pižurica - Sparse Coding and Multimodal Dictionary Learning in Computer Visions Prof. Dr Andrey Krylov - Sparse approach to image ringing detection and suppression Prof. Dr Rainer Herpers Prof. Dr Ljubiša Stanković, Prof. Dr. Miloš Daković - Compressive Sensing Based Signal Reconstruction

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Prof. Dr Danilo Mandić - Tensor Decompositions and Applications Multi-way Analysis of Big Data MSc Miloš Brajović - Analysis of Missing Samples Influence on Common Sparsity Domains MSc Rok Žurbi

PARTICIPATION TO THE MIPRO 2017 CONFERENCE 2nd - 26th May, 2017, Opatija, Croatia The CS-ICT project team members (Prof. dr Ljubiša Stanković, MSc Isidora Stanković and Milan Marić), participated to the 40th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2017. It was a 5-day-event, consisted of several conferences dedicated to high technologies, workshops, tutorials, forums and thematic days addressed to specific technological themes. Conference gathers the researchers and scientists from the various fields, such as: multimedia communications, image and video processing, speech and audio processing, telecommunications, wireless communications, etc. Several papers are presented at the conference:

[1] A. Draganić, M. Marić, I. Orović, S. Stanković, “Identification of Image Source Using Serial

number-Based Watermarking under Compressive Sensing Conditions”

[2] M. Daković, I. Stanković, M. Brajović, Lj. Stanković, “Sparse Signal Reconstruction Based on

Random Search Procedure”

[3] I. Stanković, M. Daković, I. Orović, “Overlapping Blocks in Reconstruction of Sparse Images”

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BEST PAPER AWARD FOR THE "SIGNAL PROCESSING ELSEVIER" JOURNAL The paper “Missing samples analysis in signals for applications to L-estimation and compressive sensing”, (Signal Processing Elsevier, vol. 94, pp. 401-408, January 2014) authored by Prof. Dr Ljubisa Stankovic, Prof. Dr Srdjan Stankovic (University of Montenegro, Faculty of Electrical Engineering) and Prof. Dr Moeness Amin (Villanova University, USA) has been selected as the winner of the Best Paper Award for the SIGNAL PROCESSING ELSEVIER Journal in 2017. Every year European Association for signal processing selects the best papers published in the "Signal Processing Elsevier", leading international journal in this field. Wider selection includes 32 most cited papers in the preceding year. Thereafter, the Committee composed of leading scientists from around the world, selects 6 best papers and among them, in the second round, the best published paper is chosen. This exceptional award will be officially presented at the annual conference of the European Association for signal processing - EUSIPCO 2017, which will be held in Greece with the participation of a large number of prominent scientists from around the world. STUDY VISIT TO THE NATIONAL POLYTECHNIC INSTITUTE OF GRENOBLE (INP GRENOBLE) 13th - 24th January, 2017, Grenoble, France The study visit to the National Polytechnic Institute of Grenoble (INP Grenoble) was performed by the CS-ICT project participants (Prof. Dr Ljubiša Stanković, Prof. Dr Miloš Daković, M.Sc. Anđela Draganić and M.Sc. Miloš Brajović). The purpose of this study visit was continuation of the initiated research and beginning the new research activities, with discussion of possible practical applications of the developed solutions. This was the opportunity to introduce the INP Grenoble staff members with recent developments on the project realization by Montenegrin side.

STUDY VISIT TO THE UNIVERSITY OF HEIDELBERG - PROF. DR. SRDJAN STANKOVIĆ 10th November - 5th December 2016, Heidelberg, Germany Prof. Dr Srdjan Stanković was in one-month study visit to the University of Heidelberg - Heidelberg Collaboratory for Image Processing (HCI). HCI is an "Industry on Campus" project established in the context of the German excellence initiative jointly by the University of Heidelberg. The host institution is one of the leading in the areas covering the automatized segmentation, classification, detection using machine learning, computer-added diagnosis and other actual approaches in signal processing for various applications including biomedicine.

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During this study visit, Prof. Stanković introduced staff from the HCI laboratory with the CS-ICT project, its ideas and achievements, as well as with the project objectives for the further period. Prof. Stanković worked with the Prof. Fred Hamprecht in the field of biomedical signal processing, particularly the ECG and EEG signals, which has been intensively used within the host institution (HCI Heidelberg). The aim of joint research was to combine the algorithms and solutions developed by the CS-ICT project team members, with the approaches developed in the host institution (approaches for features description, tracking, segmentation, machine learning, classification). The joint interest of both institutions is to focus the research activities toward the development of powerful application for biomedical signal analysis. This visit represented the opportunity to share the experience between the research groups, aiming to open possibilities for further advances in the related technology.

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University of Montenegro Ministry of Science of Montenegro

World Bank

Overall work progress and achievements during the period

Please provide a concise overview of the overall progress of the project towards objectives and indicate the main achievements and any main issues that have arisen in this last period. Suggested length 1 page maximum. In addition, please provide an updated Gantt chart as a separate document. In the last six months (since the previous reporting period), we finalized most of the activities specified in the project application, improving the initial results and widening the segment of applications for the developed solutions. It is important to emphasize that all specified deliverables has been provided and thee can be eventually further extended through the future activities. The detailed list of the achievements during the period December 2016 – June 2017 follows:

- The budget reallocation, for the continuation of the contracts for people actively involved in the project implementation, has been done.

- Participation to the conferences and dissemination of project results with high impact on international RDI community:

o Participation to the 40th International Convention on Information and Communication

Technology, Electronics and Microelectronics, MIPRO 2017 o Workshop on compressive sensing methods and applications o 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro

IN TOTAL: 5 presentations at the conference in the last six months (and published papers) - Overall scientific achievements: TOTAL - 18 scientific papers published

o 7 Papers published in Leading International Journals and Book Chapters o 11 Papers published at International Conferences (5 of them already presented at

the conferences)

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University of Montenegro Ministry of Science of Montenegro

World Bank

Individual Work packages progress

For each active work package please provide the information below. Additional notes have been supplied below for Management and Dissemination work packages. Suggested length ½ page maximum per Work Package.

A summary of progress towards objectives and details for each task; Highlight clearly significant results; If applicable, explain the reasons for deviations from the original work plan and their impact on

other tasks as well as on available resources and planning; If applicable, explain the reasons for failing to achieve critical objectives and/or not being on

schedule and explain the impact on other tasks as well as on available resources and planning (the explanations should be consistent with the declaration by the designated representatives of Leads Institution);

A statement on the use of resources, in particular highlighting and explaining deviations between actual and planned person-months per work package and per partner;

If applicable, propose corrective actions.

WP1: Establishing the research infrastructure and strengthening human capacities The laboratory for Compressive Sensing and Emerging Technologies staff was engaged in intensive research and innovation activities according to the project work plan. WP2: Mobility program for strengthening research skills and fostering knowledge transfer -In the reporting period, the following activities from WP2 have been performed:

- CS-ICT project team organized Workshop on Compressive Sensing methods and applications, that was held in Budva, 25th - 28th May 2017;

- Study visit to the National Polytechnic Institute of Grenoble (INP Grenoble) was organized in the period 13th - 24th January, 2017, by the CS-ICT project participants (Prof. dr Ljubiša Stanković, Prof. Dr Miloš Daković, M.Sc. Anđela Draganić and M.Sc. Miloš Brajović)

- Prof. Dr Srdjan Stanković was in the study visit to the University of Heidelberg, Heidelberg Collaboratory for Image Processing, from 10th November 2016 to 5th December 2016.

WP3: Developing new algorithms and identifying the most appropriate applications

The activities related to the WP3, from December 2016 to June 2017 resulted in several published journal papers and 5 papers presented at the international conferences. The detailed research activities are explained below.

The research activities in the previous period were focused to the following:

Signals with time-varying frequency content are generally well represented in the joint time-

frequency domain, with the components instantaneous frequency laws being their key nonstationary features. However, most commonly used methods for time-frequency distribution (TFD) calculation generate unwanted artifacts, making the TFD interpretation more difficult. In

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order to overcome this limitation, a number of methods have been proposed which utilize compressive sensing (CS) for artifact removal, while the resolution loss is reduced by the signal reconstruction based on unconstrained optimization algorithms. The most critical step in these methods is a proper CS area selection. However, its size and shape selection are generally chosen experimentally. Being motivated by this fact, in this paper we propose a method for automatic data driven CS area selection. Moreover, we have shown that by using the proposed method, we have been able to reduce the optimization requirements, resulting in highly concentrated TFDs. I. Volarić, V. Sučić, S. Stanković, “A Data Driven Compressive Sensing Approach for Time-Frequency Signal Enhancement,” Signal Processing, accepted for publication, 2017.

Figure 1: TFD of the considered signal: (a) WVD (MS z = 3.1258), (b) RGK with kernel volume parameter α = 3 (MS z = 0.3372)

Figure 2: Reconstructed sparse TFD with SpaRSA algorithm [18]: (a) Fixed CS-AF area with ǫ =

10%, MS z = 0.5074; (b) Automatically selected CS-AF area with ǫ = 10%, MS z = 0.3591; (c) Fixed CS-AF area with ǫ = 0.1%, MS z = 0.2053; (d) Automatically selected CS-AF area

with ǫ = 0.1%, MS z = 0.1419

An adaptive three-mode system based on Go-Back-N (GBN) protocol is analyzed within this paper. An ideal mode selection procedure based on a-priori known packet error probability is

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defined. When packet error probability is unknown the system state transition is controlled by several system parameters. A procedure for optimal parameters selection is proposed and tested on a simulated system. The procedure is based on minimization of mean square deviation of the system throughput from the ideal one. R. Vojinović, M. Daković, "Optimization of adaptive three-mode GBN scheme control parameters," Radioengineering, Vol. 26, No. 2, September 2017.

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its’ common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well. A. Draganic, I. Orovic, S. Stankovic, "On some common compressive sensing recovery algorithms and applications - Review paper," Facta Universitatis, Series: Electronics and Energetics, accepted for publication, 2017.

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Figure 3: Real radar signal : a) STFT, b) sorted STFT, c) STFT that remains after discarding certain region from the sorted STFT, d) the original DFT transform - blue, and the reconstructed DFT.

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d) initial S-method and e) CS based S-method Classification of interfering signals that belong to different wireless standards is important topic

in wireless communications. The procedure for separation and classification of wireless signals belonging to the Bluetooth and to the IEEE 802.11b standards is proposed. These signals operate in the same frequency band and may interfere with each other. The procedure is made of a few steps. In the first step, the separation of signal components is done using the eigenvalue decomposition approach. The second stage is based on the compressive sensing approach, used to reduce the number of transmitted samples. A suitable transform domain is chosen for each separated component using ℓ1 -norm as a measure of sparsity. Since the Bluetooth signals are less sparse compared to the IEEE 802.11b signals, after choosing sparse domain, additional sparsification needs to performed to further enhance the sparsity. In the last step of the procedure, the classification is performed by observing the time-frequency characteristics of the reconstructed separated components. The theory is proved by the experimental results. A. Draganic, I. Orovic, S. Stankovic, X. Li, Z. Wang, "An approach to classification and under-sampling of the interfering wireless signals," Microprocessors and Microsystems, in press, Volume 51, June 2017, Pages 106–113, doi: 10.1016/j.micpro.2017.04.010.

Figure 5: The S-method of the original signal; x-axis is time, y-axis is frequency

Figure 6: S-method of the separated components: first 4 figures correspond to the S-method of the FHSS modulated signal, while remaining 3 figures correspond to the S-method of the DSSS

modulated signal (x-axis is time, y-axis is frequency)

The watermark detection procedure for images corrupted by impulsive noise is proposed. The procedure is based on the compressive sensing (CS) method for the reconstruction of corrupted

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pixels. It is shown that the proposed procedure can extract watermark with a moderate impulsive noise level. It is well known that most of the images are approximately sparse in the 2D DCT domain. Moreover, we can force sparsity in the watermarking procedure and obtain almost strictly sparse image as a desirable input to the CS based reconstruction algorithms. Compared to the state-of-the-art methods for impulse noise removal, the proposed solution provides much better performance in watermark extraction. B. Lutovac, M. Daković, S. Stanković, I. Orović, "Watermark Detection in Impulsive Noise Environment Based on the Compressive Sensing Reconstruction,” Radioengineering, vol. 26, no. 1, pp. 309-315, DOI: 10.13164/re.2017.0309, ISSN: 1805-9600, 2017.

A method for denoising and reconstruction of sparse images based on a gradient-descent algorithm is proposed. It is assumed that the original (non-noisy) image is sparse in the two-dimensional Discrete Cosine Transform (2D-DCT) domain. It is also assumed that a number of image pixels is corrupted by a salt and pepper noise. In addition, we assume that there are pixels corrupted by a noise of any value. A method to find the positions of the corrupted pixels when the noise is not of the salt and pepper form is proposed as well. The proposed algorithm for noisy pixels detection and reconstruction works blindly. It does not require the knowledge about the positions of corrupted pixels. The only assumption is that the image is sparse and that the noise degrades this property. The advantage of this reconstruction algorithm is that we do not change the uncorrupted pixels in the process of the reconstruction, unlike common reconstruction methods. Corrupted pixels are detected and removed iteratively using the gradient of sparsity measure as a criterion for detection. After the corrupted pixels are detected and removed, the gradient algorithm is employed to reconstruct the image. The algorithm is tested on both grayscale and color images. Additionally, the case when both salt and pepper noise and a random noise, within the pixel values range, are combined is considered. The proposed method can be used without explicitly imposing the image sparsity in a strict sense. Quality of the reconstructed image is measured for different sparsity and noise levels using the structural similarity index, the mean absolute error, mean-square. I. Stanković, I. Orović, M. Daković, S. Stanković, "Denoising of sparse images in impulsive disturbance environment,” Multimedia Tools and Applications, pp 1–21, First Online: 22 February 2017, DOI: 10.1007/s11042-017-4502-7.

Figure 7: Reconstruction of image corrupted with 50% salt and pepper noise: Original image (top left); Sparse image (top right); Noisy image (bottom left); Reconstructed image (bottom

right)

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A new generalized concept of cognitive inspired learning motivated by the basic principles used in the compressive sensing theory is proposed. The aim is to introduce a new perspective on the learning process which uses sparsity as a main premise. Cognitive inspired learning is observed as one of the possible learning modes, where the subject learns about the unknown phenomenon by identifying a sparse set of features belonging to different known basis. Rather than offering an algorithm for gaining the knowledge, we would like to draw attention to the new learning model which could potentially be used in the areas of learning applications. S. Stankovic, I. Orovic, "Cognitive Inspired Learning based on the Compressive Sensing Postulates", 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

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Figure 8: An illustration of a space that that is not sparse in any of the basis Bi neither as a linear combination of different basis functions

The development of a system that would ease the diagnosis of heart diseases would also fasten

the work of the cardiologic department in hospitals and facilitate the monitoring of patients with portable devices. This paper presents a tool for ECG signal analysis which is designed in MATLAB. The Hermite transform domain is exploited for the analysis. The proposed transform domain is very convenient for ECG signal analysis and classification. Parts of the ECG signals, i.e. QRS complexes, show shape similarity with the Hermite basis functions, which is one of the reasons for choosing this domain. Also, the information about the signal can be represented using a small set of coefficients in this domain, which makes data transmission and analysis faster. The signal concentration in the Hermite domain and consequently, the number of samples required for signal representation, can additionally be reduced by performing the parametrization of the Hermite transform. For the comparison purpose, the Fourier transform domain is also implemented within the software, in order to compare the signal concentration in two transform domains. The application of the proposed method in clinical practice includes arrhythmia and heart failure detection, as well as other abnormalities of the cardiac rhythm. Z. Vulaj, A. Draganic, M. Brajovic, I. Orovic, "A tool for ECG signal analysis using standard and optimized Hermite transform," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

18

Figure 9: The Virtual instrument

A precise and accurate monitoring of different parameters such as temperature, relative

humidity or gas level, in cold chain logistic, is important for preserving the quality of the transported goods. Constant parameters monitoring requires a large number of sensors and a large storage capacities, and can cause overloading during the communication. Therefore, in this paper we have observed an under-sampled signal describing the level of CO2 in the cold chain, with an aim to recover the missing information by applying the Compressive Sensing approach. The reduced number of measurements will lead to decreased number of required sensors, reduced storage demands and will speed up the communication. A. Draganic, I. Orovic, S. Stankovic, X. Zhang, X. Wang, "Compressive Sensing Approach in the Table Grape Cold Chain Logistics," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

19

a) b)

Figure 10: a) First row: original image; Second row: image reconstructed using 45 % of the available samples; b) First row: original signal; Second row: signal reconstructed using 45% of the available

samples; Third row: Zoomed regions of the original signal –blue and reconstructed signal – red

Although the protection of ownership and the prevention of unauthorized manipulation of digital images becomes an important concern, there is also a big issue of image source origin authentication. This paper proposes a procedure for the identification of the image source and content by using the Public Key Cryptography Signature (PKCS). The procedure is based on the PKCS watermarking of the images captured with numerous automatic observing cameras in the Trap View cloud system. Watermark is created based on 32-bit PKCS serial number and embedded into the captured image. Watermark detection on the receiver side extracts the serial number and indicates the camera which captured the image by comparing the original and the extracted serial numbers. The watermarking procedure is designed to provide robustness to image optimization based on the Compressive Sensing approach. Also, the procedure is tested under various attacks and shows successful identification of ownership. A. Draganic, M. Maric, I. Orovic, S. Stankovic, "Identification of Image Source Using Serial-Number-Based Watermarking under Compressive Sensing Conditions", 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017.

Serial

number

embedding

SN

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embedding

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20

Images are commonly analysed by the discrete cosine transform (DCT) on a number of blocks of smaller size. The blocks are then combined back to the original size image. Since the DCT of blocks have a few nonzero coefficients, the images can be considered as sparse in this transformation domain. The theory of compressive sensing states that some corrupted pixels within blocks can be reconstructed by minimising the blocks sparsity in the DCT domain. Block edges can affect the quality of the reconstruction. In some blocks, a few pixels from an object which mostly belongs to the neighbouring blocks may appear at the edges. Compressive sensing reconstruction algorithm can recognise these pixels as disturbance and perform their false reconstruction in order to minimise the sparsity of the considered block. To overcome this problem, a method with overlapping blocks is proposed. Images are analysed with partially overlapping blocks and then reconstructed using their non-overlapped parts. We have demonstrated the improvements of overlapping blocks on images corrupted with combined noise. A comparison between the reconstructions with non-overlapping and overlapping blocks is presented using the structural similarity index. I. Stankovic, M. Dakovic, I. Orovic, “Overlapping Blocks in Reconstruction of Sparse Images”, 40th International Convention on Information and Communication Technology, Electronics and Microelectronics , MIPRO 2017.

Figure 13: Zoomed reconstructed images: with 32 × 32 non-overlapping blocks (top); with

overlapping blocks (bottom)

A compressed sensing approach for the reconstruction of 2D sparse signals with missing samples is presented. The statistical behavior of transform coefficients in the case of randomly undersampled signals is exploited as the basis of a simple algorithm for the signal support detection. With the detected signal support various reconstruction methods can be applied in the signal recovery: non-iterative reconstruction for signals with close transform coefficient values, matching pursuit based iterative reconstruction, or the combination of these two methods. As the case study, 2D discrete Fourier transform is observed, commonly appearing in radar imaging applications. The theory is confirmed through several numerical experiments, including the illustration of the applicability in the ISAR image reconstruction. M. Brajović, I. Orović, M. Daković, S. Stanković, “The Reconstruction of 2D Sparse Signals By Exploiting Transform Coefficients Variances”, EUROCON 2017, 6–8 July 2017, Ohrid, Macedonia.

21

Figure 14: The ISAR image reconstruction - MIG 25 example. The reconstruction is performed based on signals having 25%, 49%, and 68% of samples/pulses unavailable. Images without reconstruction

are shown left, whereas the reconstructed images are shown right

An architecture for hardware realization of a system for sparse signal reconstruction is

presented in the paper. The threshold based reconstruction method is considered. Noise, appearing in the signal as consequence of the missing signal samples, is used to derive the threshold. The threshold is then used in order to separate signal and non-signal components, and it is dependent on the number of missing samples. The algorithm is modified in this paper with an aim to reduce the system complexity, and to provide easier hardware realization. Instead of using the partial random Fourier transform matrix, the minimization problem is reformulated using only the triangular R matrix from the QR decomposition. The triangular R matrix can be efficiently implemented in hardware without calculating the orthogonal Q matrix. A flexible and scalable realization of matrix R is proposed, such that the size of R changes with the number of available samples and sparsity level. I. Orovic, A. Draganic, N. Lekic, S. Stankovic, “A System for Compressive Sensing Signal Reconstruction”, IEEE EUROCON 2017, 6-8 july 2017, Ohrid, Macedonia.

22

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A new algorithm implemented as combination of gradient based and single iteration

reconstruction algorithms for compressively sensed sparse signals is proposed. A good feature of the gradient algorithm to perform reconstruction for a wide range of applications is combined with the speed of single iteration algorithm in order to perform faster reconstruction in the cases where single iteration algorithm cannot performs reconstruction. The proposed method is of special importance for any application where it is not possible to separate signal components from noise in sparse domain. S. Stankovic, S. Vujovic, I. Orovic, M. Dakovic Lj. Stankovic, “Combination of Gradient Based and Single Iteration Reconstruction Algorithms for Sparse Signals”, IEEE EUROCON 2017, 6-8 july 2017, Ohrid, Macedonia.

23

Figure 17: Single iteration reconstruction performed during gradient algorithm iterations: (a) initial

state, (b) after first GA iteration, (c) after second GA iteration, (d) after third GA iteration, (e) after 10th GA iteration, (f) after 40th GA iteration. Green circles indicate positions of nonzero coefficients of

original signal. Red marks ”x” indicate maximal coefficients of signal to be reconstructed.

Rounding error in the discrete Fourier transform calculated with fast fixed-point algorithms is considered. It is shown that the variance of the rounding error depends on frequency index. Theoretically obtained results for error variance are statistically checked on decimation-in-time fast Fourier transform with two rounding methods. M. Dakovic, Lj. Stankovic, B. Lutovac, I. Stankovic, “On the Fixed-point Rounding in the DFT”, IEEE EUROCON 2017, 6-8 july 2017, Ohrid, Macedonia

An application of the median form ambiguity function in direct sequence spread spectrum modulated signals denoising is proposed. The observed signals are multicomponent and consisted of short duration sinusoidal components, appearing on different frequencies. The analysis of such multicomponent signals in the time-frequency plane could be disturbed by the unwanted cross-terms. It is shown that the filtering based on the median ambiguity function can completely eliminate the cross-terms. Moreover, beside the cross-terms the observed signal can be disturbed by different types of noise. Impulse noise is considered, due to the fact that this type of noise is common in communications. The signal terms are located around the origin in the ambiguity plane, being symmetric around the y-axis. Since the noise/cross-terms are

24

dislocated from the origin, the filtering does not affect the signal terms providing satisfactory results. After denoising and cross-terms removal, the signal parameters (hop bandwidth and hop time duration) are estimated. The theory is proved by the experimental results. A. Draganić, I. Orovic, S. Stankovic, “Spread-spectrum-modulated signal denoising based on median ambiguity function”, ELMAR 2017 conference.

a) b) c)

Figure 18: a) Wigner distribution and b) ambiguity function of the signal corrupted by noise, c) Median ambiguity function

A standard Compressive sensing scenario assumes a single sparsifying basis used to reconstruct

the signals from a small set of incoherent measurements. However, in many cases the signal cannot be sparsely represented using a single transformation. Particularly, in ECG signal analysis, each signal segment is specific in nature reflecting different physical phenomena and using the same transformation for all segments may be inappropriate for efficient analysis and reconstruction. Moreover, in the CS scenario, it would be necessary to combine different transforms to achieve compact signal support and to provide successful reconstruction from randomly under-sampled data. Using the concept of Orthogonal Matching Pursuit, a hybrid compressive sensing reconstruction algorithm that combines different transform basis is proposed. The performance of the proposed approach is verified experimentally using the combination of Fourier and Hermite transform on a real ECG signal. S. Stanković, I. Orović: Hybrid compressive sensing procedure with application to ECG signals reconstruction.

Computer based recognition and detection of abnormalities in ECG signals is proposed. For this purpose, the Support Vector Machines (SVM) are combined with the advantages of Hermite transform representation. SVM represent a special type of classification techniques commonly used in medical applications. Automatic classification of ECG could make the work of cardiologic departments faster and more efficient. It would also reduce the number of false diagnosis and, as a result, save lives. The working principle of the SVM is based on translating the data into a high dimensional feature space and separating it using a linear classificator. In order to provide an optimal representation for SVM application, the Hermite transform domain is used. This domain is proved to be suitable because of the similarity of the QRS complex with Hermite basis functions. The maximal signal information is obtained using a small set of features that are used for detection of irregular QRS complexes. The aim of the paper is to show that these features can be employed for automatic ECG signal analysis. Zoja Vulaj, Miloš Brajović, Anđela Draganić, Irena Orović, “Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine”, ELMAR 2017 conference, Zadar, Croatia.

WD

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25

Figure 19: The classification healthy – diseased (first figure – Time domain waveform of healthy people ECG signal (top sub-figure) and Hermite coefficients of its QRS complexes (bottom sub-

figure)), second figure – The performance of the classificatory, third figure – Time domain waveform of diseased people ECG signal (top sub-figure) and Hermite coefficients of its QRS

complexes (bottom sub-figure)))

WP4: Practical implementation and design of innovative software products

- All activities conducted toward the development of practical applications, i.e., the software tools (Virtual instruments) comprising different data reconstruction algorithms, different reconstruction scenarios, and different source data of interest (types of signals) are finalized. WP5: - The web site of the project has been constantly updated. The information about the study visits and trainings that were organized are uploaded. Also, the information and pictures from the conferences at which Project team participated, as well as any other news and events related to the project implementation are displayed. The list of publications related to the Project is also constantly updated. The web site address is: http://www.cs-ict.ac.me/). WP6: Similar as during the previous semi-annual periods, the management activities were related to the communication activities with the HERIC office: preparation of relevant documents for the project staff members as well as the necessary supporting documents required by the HERIC Office. There were several modifications of the third training plan, as well as budget reallocation between the positions for the staff involved in the project implementation. Also, we prepared documentation for the extension of the project implementation period, as well as training plan for the period of extension. The documentation for the financial evidences of the mobility program and travel costs has been continuously prepared and stored, as well as documentation for the honorariums payment to the researchers. During this period, the documentation for the instalment payment has been prepared and submitted to the Ministry of Science and Ministry of Finance of Montenegro.

WP7: Monthly reports which were submitted to the project coordinator, as well as internal weekly meetings that are performed, act as an internal quality control.

26

University of Montenegro Ministry of Science of Montenegro World Bank

For the Project Management work package please include the following:

Partnership management tasks and achievements; If applicable, problems which have occurred and how they were solved or envisaged solutions; Changes in the partnership, if any; List of project meetings, dates and venues; Project planning and status; Impact of possible deviations from the planned milestones and deliverables, if any; The section should also provide short comments and information on co-ordination activities during

the period in question, such as communication between partners, possible co-operation with other projects/programmes etc.

- Partnership management tasks and achievements; The main part of management activities

was related to the communication activities with the HERIC office, Ministry of Science of Montenegro and the technical support unit from the Ministry of Finance of Montenegro (during the preparation of the documents for the next instalment payment). The CS-ICT project team members organized periodical meetings to discuss the issues regarding the project implementation (the research results, spending forecast, mobility periods, the conference participation plan and other planned activities for the next period).

- If applicable, problems which have occurred and how they were solved or envisaged solutions;

- There were no problems in the project implementation during this semi-annual period.

- Changes in the partnership, if any; - No changes in the partnership

- List of project meetings, dates and venues;

- Meeting with the CS-ICT project participants in Grenoble, France (13th - 24th January, 2017,

Grenoble, France) - Workshop on Compressive Sensing methods and applications (25th - 28th May 2017, Budva,

Montenegro) – meeting with the project participants as well as some of the eminent scientists from the world

- Project planning and status

The project activities are realized according to the plan.

- Dissemination of the CS-ICT project is done through the participation at the conferences:

o Participation to the Workshop on compressive sensing methods and applications is successfully accomplished

o Participation to the MECO 2017 and MIPRO 2017 conferences is successfully accomplished

27

- Study visit to University of Heidelberg, Germany, with the purpose of fostering collaboration with the eminent research institutions from the Germany, as well as establishing collaboration with the research institutions and groups dealing in the same or similar fields as our group, is organized, 10th November - 6th December, 2016 - Prof. Dr. Srdjan Stanković

- Study visit to INP Grenoble was performed by the CS-ICT project participants (Prof. Dr Ljubiša

Stanković, Prof. Dr Miloš Daković, M.Sc. Anđela Draganić and M.Sc. Miloš Brajović). The purpose of this study visit was continuation of the initiated research and beginning the new research activities, with discussion of possible practical applications of the developed solutions.

- Impact of possible deviations from the planned milestones and deliverables, if any; No significant impacts encountered.

- The section should also provide short comments and information on co-ordination

activities during the period in question, such as communication between partners, possible co-operation with other projects/programmes etc.

-In the period 10th November - 5th December 2016, Prof. Dr. Srđan Stanković was on the study visit to the University of Heidelberg - Heidelberg Collaboratory for Image Processing (HCI). During this study visit, Prof. Stanković introduced staff from the HCI laboratory with the CS-ICT project, its ideas and achievements, as well as with the project objectives for the further period. Prof. Stanković worked with the Prof. Fred Hamprecht in the field of biomedical signal processing, particularly the ECG and EEG signals, which has been intensively used within the host institution (HCI Heidelberg). The aim was to combine the algorithms and solutions developed by the CS-ICT project team members, with the approaches developed in the host institution and to focus the research activities toward the development of powerful application for biomedical signal analysis. - The study visit to the National Polytechnic Institute of Grenoble (INP Grenoble), organized in the period 13th - 24th January, 2017, was organized with the purpose of continuation of the initiated research and beginning the new research activities, with discussion of possible practical applications of the developed solutions.

- Budget reallocation has been done in the previous period, in order to make plan for the period of the project extension. -Documentation for the next instalment has been prepared in coordination with the HERIC office and Ministry of finance -Weekly internal meetings with the UoMFEE project staff were organized, in order to summarize the research results, to define the training plan for all project participants, make the conference participation plan for all project participants.

-Skype meetings were periodically organized with partners from Slovenia and France, and e-mail communication has been performed as well.

28

University of Montenegro Ministry of Science of Montenegro

World Bank

For the Dissemination work package please include the following:

Use of ’foreground’ and dissemination activities during this period. This should include: o List of publications resulting from the project in the period (published or submitted). o List of events organised by the project in the period or planned for the next period. o List of exploitable results (if relevant): provide a short description and an overview in each

case of how the knowledge could be exploited or used in further research. Indicate if Intellectual Property Rights protection measures have been/will be sought (patents, design rights, database rights – include references and any relevant details).

List of publications resulting from the project in the period (published or submitted):

Leading International Journals and Book Chapters

[1] I. Volarić, V. Sučić, S. Stanković, “A Data Driven Compressive Sensing Approach for Time-Frequency Signal Enhancement,” Signal Processing, accepted for publication, 2017.

[2] R. Vojinović, M. Daković, "Optimization of adaptive three-mode GBN scheme control parameters," Radioengineering, Vol. 26, No. 2, September 2017.

[3] A. Draganic, I. Orovic, S. Stankovic, "On some common compressive sensing recovery algorithms and applications - Review paper," Facta Universitatis, Series: Electronics and Energetics, accepted for publication, 2017.

[4] A. Draganic, I. Orovic, S. Stankovic, X. Li, Z. Wang, "An approach to classification and under-sampling of the interfering wireless signals," Microprocessors and Microsystems, in press, Volume 51, June 2017, Pages 106–113, doi: 10.1016/j.micpro.2017.04.010.

[5] B. Lutovac, M. Daković, S. Stanković, I. Orović, "Watermark Detection in Impulsive Noise Environment Based on the Compressive Sensing Reconstruction,” Radioengineering, vol. 26, no. 1, pp. 309-315, DOI: 10.13164/re.2017.0309, ISSN: 1805-9600, 2017.

[6] I. Stanković, I. Orović, M. Daković, S. Stanković, "Denoising of sparse images in impulsive disturbance environment,” Multimedia Tools and Applications, pp 1–21, First Online: 22 February 2017, DOI: 10.1007/s11042-017-4502-7.

[7] LJ. Stanković, M. Daković, S. Stanković, and I. Orović, “Sparse Signal Processing - Introduction ,” Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley, 2017.

International Conferences

[8] S. Stankovic, I. Orovic, "Cognitive Inspired Learning based on the Compressive Sensing Postulates", 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

[9] Z. Vulaj, A. Draganic, M. Brajovic, I. Orovic, "A tool for ECG signal analysis using standard and optimized Hermite transform," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

[10] A. Draganic, I. Orovic, S. Stankovic, X. Zhang, X. Wang, "Compressive Sensing Approach in the Table Grape Cold Chain Logistics," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

[11] A. Draganic, M. Maric, I. Orovic, S. Stankovic, "Identification of Image Source Using Serial-Number-Based Watermarking under Compressive Sensing Conditions", 40th International Convention on Information and Communication Technology, Electronics and Microelectronics,

29

MIPRO 2017. [12] I. Stankovic, M. Dakovic, I. Orovic, “Overlapping Blocks in Reconstruction of Sparse Images”,

40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017.

[13] M. Brajović, I. Orović, M. Daković, S. Stanković, “The Reconstruction of 2D Sparse Signals By Exploiting Transform Coefficients Variances”;

[14] I. Orovic, A. Draganic, N. Lekic, S. Stankovic, “A System for Compressive Sensing Signal Reconstruction”;

[15] S. Stankovic, S. Vujovic, I. Orovic, M. Dakovic Lj. Stankovic, “Combination of Gradient Based and Single Iteration Reconstruction Algorithms for Sparse Signals”;

[16] M. Dakovic, Lj. Stankovic, B. Lutovac, I. Stankovic, “On the Fixed-point Rounding in the DFT”. [17] A. Draganić, I. Orovic, S. Stankovic, “Spread-spectrum-modulated signal denoising based on

median ambiguity function”; [18] Zoja Vulaj, Miloš Brajović, Anđela Draganić, Irena Orović, “Detection of irregular QRS

complexes using Hermite Transform and Support Vector Machine”, ELMAR 2017 conference, Zadar, Croatia

List of events organised by the project in the period or planned for the next period.

Organized events (December 2016 –June 2017):

Organized:

o Participation to the MIPRO 2017 conference (22nd – 26th May 2017, Opatija, Croatia) o Participation to the meco 2017 conference (11th - 15th June 2017, Bar, Montenegro) o WORKSHOP ON COMPRESSIVE SENSING METHODS AND APPLICATIONS (25th - 28th

May 2017, Budva, Montenegro) o Study visit to the National Polytechnic Institute of Grenoble (INP grenoble, 13th – 24th

January, 2017, Grenoble, France) o Study visit to the University of Heidelberg - Prof. dr. Srdjan Stanković (10th November -

5th December 2016, Heidelberg, Germany)

Planned:

o Participation to the EUROCON 2017 conference (6th-8th July, Ohrid, Macedonia) o Participation to the DSP 2017 conference (London, United Kingdom) o Participation to the Application of Information and Communication Technologies -

AICT Moscow (conference September 2017); o Participation to the ELMAR 2017 conference (18th-20th September, Zadar, Croatia)

List of exploitable results (if relevant): provide a short description and an overview in each case of how the knowledge could be exploited or used in further research. Indicate if Intellectual Property Rights protection measures have been/will be sought (patents, design rights, database rights – include references and any relevant details).

1. The solutions developed through the project implementation are adapted for use in specific applications in the area of multimedia, communications and biomedical signal processing. Therefore, several papers focused on solving the actual problems in applications using the research basis accumulated within the previous research activities, are published.

2. All achieved results, algorithms, approaches enclosed within the project publications, most of which are published by the eminent scientific journals scoring high impact factor, opens the possibility to explore these achievements into the new attractive areas of Big Data processing which is an emerging area in many contemporary applications dealing with large amounts of data. Namely, as a

30

result of expertize exchanged during the project workshop, the idea of employing compressive sensing algorithms in Big Data analysis arose as one of the promising idea for further research activities.

31

Deliverables and milestones tables

Deliverables (do not include submission of periodic and final reports in this deliverable report)

Please list all the deliverables due in this reporting period, as indicated in Annex 1 of the Grant Agreement.

Summary Deliverables List

Del. No.1 Deliverable name WP No. Nature2 Projected delivery date

(from Annex 1)3 Actual/Forecast delivery

date3 Comments

D2.1 Individual mobility reports

about knowledge transfer WP2 R PU M34

D3.1 New algorithms for Compressive Sensed signal reconstruction

WP3 O M34 M30

D3.2 New approaches for signal analysis based on compressive sensing principle

WP3 O M34 M30

D3.3 High quality scientific publications: SCI journals and conference proceedings

WP3 O M34 M30 The project team

published 18 scientific papers during the last semi-annual period:

7 Papers published in leading international

journals and 11 Papers published at international

conferences.

1 Deliverable numbers in order of delivery dates. Please use the numbering convention <WP number>.<number of deliverable within that WP>. For example, deliverable 4.2 would be the second deliverable from work

package 4. 2 Please indicate the nature of the deliverable using one of the following codes: R = Report; P = Prototype; D = Demonstrator; O = Other; 3 Measured in months from the project start date

32

5 Papers are under review in leading

international journals

D4.1 Software product/Virtual instrument for compressive sensing signal reconstruction

WP4 P M36 M18 Software product performing the reconstruction of different information/signal type from a small random set of measurements is developed.

D4.2 Software product/Virtual

instrument for combined

time-frequency analysis

and compressive sensing

signal reconstruction

WP4 P PU M34 Finished

D4.3 Hardware design using programmable logical devices for compressive sensing and time-frequency analysis

WP4 P M34 Finished

D4.4 Multimedia e-learning platform for compressive sensing

WP4 P M34 Finished

D5.2 Presentations from the participations on international conferences

WP5 D M34 M30 The project team members participated to the several conferences during this period, and presented CS-ICT project results to the wide scientific community during these scientific meetings

D6.4 Reports from the WP6 R PU M31

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Management meeting 3

If a deliverable has been delayed, cancelled or regrouped with another one, or if its content has been modified, please indicate and explain this in the column “Comments”. If a new deliverable is proposed, please indicate this in appropriate way in the table, allocating the appropriate ordinal number in the first column and explanation in the column “Comments”.

Milestones

Please complete this table with milestones reached in this reporting period as specified in Annex 1 of the Grant Agreement. Milestones will be assessed against the specific criteria and performance indicators as defined in Annex 1. Please record any deviations in the comments column.

No. Name Work

packages involved

Achieved? (Y/N)

Planned completion

date

Realised completion

date Comments

Means of verification (from Annex 1, table 1.2c)4

2.1 Plan of mobilities WP2 Y [07.2014] [01.2017] Transfer of knowledge, experience, methodology.

Knowledge transfer completed

3.1 Preparing journal and conference papers

WP3 Y [06.2014] [05.2017]

The paper publications is done according to the project plan

Published papers (18 papers in this semi-annual period)

4.1 Developing software products for specific application in multimedia, biomedicine and communications

WP4

Y [05.2015] [05.2017]

The software products have been finalized. Software released and validated by the user group

4.2 Developing a hardware design using programmable logical devices

WP4 Y [05.2015] [03.2017]

Hardware design has been finalized.

4.3 Developing Multimedia e-learning platform for compressive sensing

WP4 Y [07.2015] [05.2017] Software product Platform released and validated by the user groups

6.1 Preparing the project promotion, presentation of the results and developed products

WP5 Y [10.2016] [03.2017] Dissemination of the CS-ICT project is done through the participation at the conferences, Workshop that is organized in May 2017.

Promotional event completed

7.1 Project management activities WP6, WP7 Y [06.2014] [05.2017]

All project management activities are continuously performed according to the plan.

Project has been well managed and the quality has been validated

4 Indicate how you can confirm that the milestone has been attained. Refer to indicators if appropriate. For example: a laboratory prototype completed and running flawlessly; software released and validated by a user

group; field survey complete and data quality validated.

34

University of Montenegro Ministry of Science of Montenegro

World Bank

Key activities planned for next reporting period

Please provide a concise overview of the objectives and activities planned for the next reporting period. Indicate if any major issues that have arisen in the last reporting period will be addressed here. Suggested length 1 page maximum.

For the next project period, following activities are planned:

- Participation to the 17th IEEE International Conference on Smart Technologies IEEE EUROCON 2017 conference;

- Participation to the 22nd International Conference on Digital Signal Processing, London (August

2017);

- Participation to the Application of Information and Communication Technologies - AICT Moscow (conference September 2017);

- Participation to the ELMAR 2017 conference, 18th-20th September 2017, Zadar, Croatia.

Recommendations to the PMT and MoS (suggested length 1 page maximum)

Make any recommendation you find appropriate; In addition, you may list the manner in which in your opinion the PMT and MoS can assist in

promoting project implementation.

35

REFERENCES

[1] I. Volarić, V. Sučić, S. Stanković, “A Data Driven Compressive Sensing Approach for Time-Frequency Signal Enhancement,” Signal Processing, pp. 229-239, Volume 141, December 2017.

[2] A. Draganic, I. Orovic, S. Stankovic, "On some common compressive sensing recovery algorithms and applications - Review paper," Facta Universitatis, Series: Electronics and Energetics, Vol 30, No 4 (2017), pp. 477-510, DOI Number 10.2298/FUEE1704477D, December 2017.

[3] R. Vojinović, M. Daković, "Optimization of adaptive three-mode GBN scheme control parameters," Radioengineering, Vol. 26, No. 2, September 2017.

[4] A. Draganic, I. Orovic, S. Stankovic, X. Li, Z. Wang, "An approach to classification and under-sampling of the interfering wireless signals," Microprocessors and Microsystems, Volume 51, June 2017, Pages 106–113, doi: 10.1016/j.micpro.2017.04.010.

[5] B. Lutovac, M. Daković, S. Stanković, I. Orović, "Watermark Detection in Impulsive Noise Environment Based on the Compressive Sensing Reconstruction,” Radioengineering, vol. 26, no. 1, pp. 309-315, DOI: 10.13164/re.2017.0309, ISSN: 1805-9600, 2017.

[6] I. Stanković, I. Orović, M. Daković, S. Stanković, "Denoising of sparse images in impulsive disturbance environment,” Multimedia Tools and Applications, pp 1–21, First Online: 22 February 2017, DOI: 10.1007/s11042-017-4502-7.

[7] LJ. Stanković, M. Daković, S. Stanković, I. Orović, “Sparse Signal Processing - Introduction ,” Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley, 2017.

[8] S. Stankovic, I. Orovic, "Cognitive Inspired Learning based on the Compressive Sensing Postulates", 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

[9] Z. Vulaj, A. Draganic, M. Brajovic, I. Orovic, "A tool for ECG signal analysis using standard and optimized Hermite transform," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

[10] A. Draganic, I. Orovic, S. Stankovic, X. Zhang, X. Wang, "Compressive Sensing Approach in the Table Grape Cold Chain Logistics," 6th Mediterranean Conference on Embedded Computing MECO 2017, Bar, Montenegro.

[11] A. Draganic, M. Maric, I. Orovic, S. Stankovic, "Identification of Image Source Using Serial-Number-Based Watermarking under Compressive Sensing Conditions", 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017.

[12] I. Stankovic, M. Dakovic, I. Orovic, “Overlapping Blocks in Reconstruction of Sparse Images”, 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017.

[13] M. Brajović, I. Orović, M. Daković, S. Stanković, “The Reconstruction of 2D Sparse Signals By Exploiting Transform Coefficients Variances”, 17th IEEE International Conference on Smart Technologies, IEEE EUROCON 2017, 6th-8th July 2017, Ohrid, Macedonia.

[14] I. Orovic, A. Draganic, N. Lekic, S. Stankovic, “A System for Compressive Sensing Signal Reconstruction”, 17th IEEE International Conference on Smart Technologies, IEEE EUROCON 2017, 6th-8th July 2017, Ohrid, Macedonia.

[15] S. Stankovic, S. Vujovic, I. Orovic, M. Dakovic Lj. Stankovic, “Combination of Gradient Based and Single Iteration Reconstruction Algorithms for Sparse Signals”, 17th IEEE International Conference on Smart Technologies, IEEE EUROCON 2017, 6th-8th July 2017, Ohrid, Macedonia.

[16] M. Dakovic, Lj. Stankovic, B. Lutovac, I. Stankovic, “On the Fixed-point Rounding in the DFT”, 17th IEEE International Conference on Smart Technologies, IEEE EUROCON 2017, 6th-8th July 2017, Ohrid, Macedonia.

[17] A. Draganić, I. Orovic, S. Stankovic, “Spread-spectrum-modulated signal denoising based on median ambiguity function”, 59th International Symposium ELMAR-2017, Zadar, Croatia, 2017.

[18] Z. Vulaj, M. Brajović, A. Draganić, I. Orović, “Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine”, 59th International Symposium ELMAR-2017, Zadar, Croatia, 2017.