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Ciro D'Elia

Bio: Ciro D'Elia is an academic researcher from University of Cassino. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 10, co-authored 32 publications receiving 490 citations.

Papers
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Journal ArticleDOI
TL;DR: Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.
Abstract: Recently, the so-called cell-free (CF) massive multiple-input multiple-output (MIMO) architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs). The paper extends the CF approach to the case in which both the APs and the MSs are equipped with multiple antennas, proposing a beamfoming scheme that, relying on the zero-forcing strategy, does not require channel estimation at the MSs. We contrast the originally proposed formulation of CF massive MIMO with a user-centric (UC) approach wherein each MS is served only by a limited number of APs. Exploiting the framework of successive lower-bound maximization, the paper also proposes and analyzes power allocation strategies aimed at either sum-rate maximization or minimum-rate maximization, both for the uplink and downlink. Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.

192 citations

Journal ArticleDOI
TL;DR: Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.
Abstract: We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.

159 citations

Journal ArticleDOI
29 Mar 2019
TL;DR: In this paper, a multiuser clustered millimeter wave channel model is introduced to account for the correlation among the channels of nearby users, and an uplink multi-user channel estimation scheme along with low-complexity hybrid analog/digital beamforming architectures are described along with power allocation for downlink global energy efficiency maximization.
Abstract: In a cell-free massive MIMO architecture a very large number of distributed access points simultaneously and jointly serves a much smaller number of mobile stations; a variant of the cell-free technique is the user-centric approach, wherein each access point just serves a reduced set of mobile stations. This paper introduces and analyzes the cell-free and user-centric architectures at millimeter wave frequencies, considering a training-based channel estimation phase, and the downlink and uplink data transmission phases. First of all, a multiuser clustered millimeter wave channel model is introduced in order to account for the correlation among the channels of nearby users; second, an uplink multiuser channel estimation scheme is described along with low-complexity hybrid analog/digital beamforming architectures. Third, the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. Interestingly, in the proposed schemes no channel estimation is needed at the mobile stations, and the beamforming schemes used at the mobile stations are channel-independent and have a very simple structure. Numerical results show the benefits granted by the power control procedure, that the considered architectures are effective, and permit assessing the loss incurred by the use of the hybrid beamformers and by the channel estimation errors.

76 citations

Journal ArticleDOI
TL;DR: In this paper, a multiuser clustered millimeter wave channel model is introduced to account for the correlation among the channels of nearby users, and an uplink multi-user channel estimation scheme along with low-complexity hybrid analog/digital beamforming architectures are described along with power allocation for downlink global energy efficiency maximization.
Abstract: In a cell-free massive MIMO architecture a very large number of distributed access points simultaneously and jointly serves a much smaller number of mobile stations; a variant of the cell-free technique is the user-centric approach, wherein each access point just serves a reduced set of mobile stations. This paper introduces and analyzes the cell-free and user-centric architectures at millimeter wave frequencies, considering a training-based channel estimation phase, and the downlink and uplink data transmission phases. First of all, a multiuser clustered millimeter wave channel model is introduced in order to account for the correlation among the channels of nearby users; second, an uplink multiuser channel estimation scheme is described along with low-complexity hybrid analog/digital beamforming architectures. Third, the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. Interestingly, in the proposed schemes no channel estimation is needed at the mobile stations, and the beamforming schemes used at the mobile stations are channel-independent and have a very simple structure. Numerical results show the benefits granted by the power control procedure, that the considered architectures are effective, and permit assessing the loss incurred by the use of the hybrid beamformers and by the channel estimation errors.

57 citations

Journal ArticleDOI
TL;DR: A novel system for computer-aided detection of clusters of microcalcifications on digital mammograms that exhibits some remarkable advantages both in segmentation and classification phases is described.

46 citations


Cited by
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Journal ArticleDOI
TL;DR: An extensive state-of-the-art survey on OBIA techniques is conducted, discussed different segmentation techniques and their applicability to OBIB, and selected optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects.
Abstract: Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques.

325 citations

Journal ArticleDOI
TL;DR: This paper quantifies the advantages of CF massive MIMO systems in terms of their energy- and cost-efficiency and the signal processing techniques invoked for reducing the fronthaul burden for joint channel estimation and for transmit precoding.
Abstract: Cell-free (CF) massive multiple-input-multiple-output (MIMO) systems have a large number of individually controllable antennas distributed over a wide area for simultaneously serving a small number of user equipments (UEs). This solution has been considered as a promising next-generation technology due to its ability to offer a similar quality of service to all UEs despite its low-complexity signal processing. In this paper, we provide a comprehensive survey of CF massive MIMO systems. To be more specific, the benefit of the so-called channel hardening and the favorable propagation conditions are exploited. Furthermore, we quantify the advantages of CF massive MIMO systems in terms of their energy- and cost-efficiency. Additionally, the signal processing techniques invoked for reducing the fronthaul burden for joint channel estimation and for transmit precoding are analyzed. Finally, the open research challenges in both its deployment and network management are highlighted.

322 citations

Journal ArticleDOI
TL;DR: This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts inThe field.
Abstract: The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.

236 citations

01 Jan 2010
TL;DR: Different image segmentation techniques applied on optical remote sensing images are reviewed and conclusions are drawn summarizing commonly used techniques and their complexities in application.
Abstract: With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques applied on optical remote sensing images are reviewed. The selection of papers include sources from image processing journals, conferences, books, dissertations and thesis out of more than 3000 journals, books and online research databases available at UNB. The conceptual details of the techniques are explained and mathematical details are avoided for simplicity. Both broad and detailed categorisations of reviewed segmentation techniques are provided. The state of art research on each category is provided with emphasis on developed technologies and image properties used by them. The categories defined are not always mutually independent. Hence, their interrelationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application

224 citations

Journal ArticleDOI
01 Mar 2013
TL;DR: Focusing on the applicative problem of land-cover mapping from very-high-resolution (VHR) remote sensing images, which is a relevant problem in many applications of environmental monitoring and natural resource exploitation, Markov models convey a great potential, thanks to their capability to effectively describe and incorporate the spatial information associated with image data into an image-classification process.
Abstract: Markov models represent a wide and general family of stochastic models for the temporal and spatial dependence properties associated to 1-D and multidimensional random sequences or random fields. Their applications range over a wide variety of subareas of the information and communication technology (ICT) field, including networking, automation, speech processing, genomic-sequence analysis, or image processing. Focusing on the applicative problem of land-cover mapping from very-high-resolution (VHR) remote sensing images, which is a relevant problem in many applications of environmental monitoring and natural resource exploitation, Markov models convey a great potential, thanks to their capability to effectively describe and incorporate the spatial information associated with image data into an image-classification process. In this framework, the main ideas and previous work about Markov modeling for VHR image classification will be recalled in this paper and processing results obtained through recent methods proposed by the authors will be discussed.

218 citations