Dimitris Ampeliotis

• Ph.D.
• Professor (Assistant) at Ionian University

Soft interference cancellers (SICs) have been proposed in the literature as a means for reducing the computational complexity of the so-called turbo equalization receiver architecture. Soft-input-soft output (SISO) equalization algorithms based on linear filters have a tremendous complexity advantage over trellis-diagram-based SISO equalizers, espe...

This work addresses the problem of estimating the locations of multiple acoustic sources by a network of distributed energy measuring sensors. The maximum likelihood (ML) solution to this problem is related to the optimization of a non-convex function of, usually, many variables. Thus, search-based methods of high complexity are required in order t...

Localization of an isotropic source using energy measurements from randomly deployed sensors is considered. In particular, an optimization problem that does not require knowledge of the underlaying energy decay model is proposed, and a condition under which the optimal solution can be computed is given. This condition employs a new geometric constr...

The problem of computing a common point that lies in the intersection of a finite number of closed convex sets, each known to one agent in a network, is studied. This issue, known as the distributed convex feasibility problem or the distributed constrained consensus problem, constitutes an important research goal mainly due to the large number of p...

The emergence of deep learning has sparked notable strides in the quality of synthetic media. Yet, as photorealism reaches new heights, the line between generated and authentic images blurs, raising concerns about the dissemination of counterfeit or manipulated content online. Consequently, there is a pressing need to develop automated tools capabl...

X, formerly, Twitter is considered a valuable tool for journalists for real-time interaction with their followers. Especially, in the case of political journalists, the degree of their influence and persuasion is of great importance. In this paper, we deal with identifying the journalists’ political charisma. More specifically we propose an algorit...

In this work, we study the problem of skin cancer diagnosis from images by employing a network of collaborating institutions (e.g, hospitals) that cooperate under the emerging federated learning protocol. In such a scenario, the problems of not exposing sensitive patient information as well as the heterogeneity of the participating devices are of p...

Considering the high cost of high-resolution Lidar sensors, in this work, a novel Lidar super-resolution method is proposed to improve the performance on numerous autonomous vehicle perception tasks, including that of a Lidar odometer. Specifically, we propose a regularized optimization problem employing a learnable regularizer (neural network) to...

Recent advances in generative adversarial networks (GANs) allow for the synthesis of extremely photo-realistic face im- ages, deceiving even the most experienced observers, let alone the unsuspecting internet user. Due to this, there has been a considerable effort by the image forensics community to design appropriate tools for the detection of the...

A federated dictionary learning problem, in which the edge devices have data with different statistical properties, is considered. A dictionary learning cost function that can lead to dictionaries that do not exhaust their full representation capabilities is proposed. The edge devices utilize this algorithm to design the local dictionaries. Likewis...

In this paper, we propose a novel methodology for addressing the hyperspectral image deconvolution problem. This problem is highly ill-posed, and thus, requires proper priors (regularizers) to model the inherent spectral-spatial correlations of the HSI signals. To this end, a new optimization problem is formulated, leveraging a learnable regularize...

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due t...

In this paper, a novel technique for the hyperspectral image deconvolution problem is developed. First, considering the highly ill-posed nature of the examined problem, it is imperative to incorporate proper priors (regularizers) to capture the strong spectral and spatial dependencies of the hyperspectral images. Then, in light of this, a novel opt...

In this study, the problem of computing a sparse representation of multi-dimensional visual data is considered. In general, such data e.g., hyperspectral images, color images or video data consists of signals that exhibit strong local dependencies. A new computationally efficient sparse coding optimization problem is derived by employing regulariza...

In this study, the problem of computing a sparse representation of multi-dimensional visual data is considered. In general, such data e.g., hyperspectral images, color images or video data consists of signals that exhibit strong local dependencies. A new computationally efficient sparse coding optimization problem is derived by employing regulariza...

The problem of learning a common dictionary by following a federated learning framework, in a network where each edge user may have statistically different data, is considered. In such a challenging setting, the Federated Averaging solution is shown to exhibit poor performance. To alleviate the drawbacks of this approach, two more elaborate schemes...

This work studies the problem of designing computer-generated holograms using phase-shifting masks limited to represent only a small number of discrete phase levels. This problem has various applications, notably in the emerging field of optogenetics and lithography. A novel regularized cost function is proposed for the problem at hand that penaliz...

Research in the field of Optogenetics has matured to the extent that in vivo implementations are more frequent in the literature. The majority of these employs different variants of optical fibers, including standard optical fibers, fiber bundles and tapered fibers. In the present contribution we review the application of optical fibers in optogene...

In this work, the problem of designing proper Phase-Shifting Masks (PSMs) suitable for optogenetic applications is considered. In such applications, structured light is used to stimulate neurons or groups of neurons while short-term excitation is required to study the dynamics of the neuronal activity. In practice, such fast response times can be a...

In this work we develop a cost-efficient coupled dictionary learning based method for reconstructing multispectral images using only a single RGB commercial camera, without requiring the sensitivity function of the camera sensor. Considering the very high cost, the acquisition time and reduced mobility of multispectral cameras we claim that this is...

A novel design method for the in-vivo optogenetic photostimulation of neurons is presented. The method accounts for the brain tissue scattering effects for the holographic illumination of neurons.

Given two datasets that belong to different feature spaces and both correspond to the same underlying phenomenon, the scope of coupled dictionary learning is to compute two dictionaries, one for each dataset, so that each dataset is approximated using the respective dictionary but the same sparse coding matrix. In this work, the focus is on a parti...

The problem of computing a proper sparse representation matrix for a signal matrix that obeys some local smoothness property, given an over-complete dictionary, is considered. The focus is on piece-wise smooth signals, defined as signals that comprise a number of blocks that each fulfills the considered smoothness property. A computationally effici...

In recent years, several successful schemes have been proposed to solve the song identification problem. These techniques aim to construct a signal’s audio-fingerprint by either employing conventional signal processing techniques or by computing its sparse representation in the time-frequency domain. This paper proposes a new audio-fingerprinting s...

Distributed estimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. The ambiguities are modelled by following a set-theoretic approach, that leads to each sensor employing a non-convex constraint set on the parameter vector. Consensus can be used to reach an estimate consistent with the measurements...

Distributed estimation of a parameter vector in a network of sensors with ambiguous measurements is considered. In our model, when nodes rely solely on their own measurements, they obtain a number of possible convex sets where the parameter vector may lie in. This ambiguity may be due to interference, poor calibration, high noise levels or any othe...

We study a problem in which the nodes of a network, each with different data, are interested in computing a common dictionary that is suitable for the efficient sparse coding of all their data. To this end, distributed processing is employed, that is, the nodes merge properly local and neighboring information. We formulate this as a convex feasibil...

In this paper, the parameter estimation problem based on diffusion least mean squares strategies is analyzed from a coalitional game theoretical perspective. Specifically, while selfishly minimizing only their own mean-square costs, the nodes in a network form coalitions that benefit them. Due to its nature, the problem is modeled as a non-transfer...

In this paper, a supervised energy disaggregation method is proposed. The appliances that are monitored, are modelled by multi-state finite state machines. Each state of an appliance is described by exactly one vector of power consumptions from a carefully designed set of such vectors (called atoms), that comprise a dictionary. The latter is constr...

Diffusion-based distributed dictionary learning methods are studied in this work. We consider the classical mixed l 2 -l 1 cost function, that employs an l 2 representation error term and an l 1 sparsity promoting regularizer. First, we observe that this cost function suffers from an inherent permutation ambiguity. This ambiguity may deteriorate si...

In this work, we consider a V2V commnunication system operating over high relative speeds. In this scenario, the wireless channel is characterized by double selectivity, which results into intercarrier interference (ICI) at the receiver. To mitigate this effect, several equalization schemes have been proposed, which usually adopt a banded approxima...

We consider a scenario in which a number of sensor nodes monitor an area, where several sources are active. Each node has an interest to estimate the signal of a particular source using measurements that, unavoidably, are mixtures of the source signals. Nodes could improve the quality of the signal of interest if they were able to use the signals m...

In this paper, the parameter estimation problem based on diffusion least mean squares strategies is studied from a coalitional game theoretical perspective. The problem has been modeled as a non-transferable coalitional game and two scenarios have been considered, one where the value function includes only a suitable estimation accuracy criterion...

In this work, we address the so-called sensor reachback problem for Wireless Sensor Networks, which consists in collecting the measurements acquired by a large number of sensor nodes into a sink node which has major computational and power capabilities. Focused on applications such as Structural Health Monitoring, we propose a cooperative communica...

Distributed generation and the urge for a more efficient grid operation will increase the frequency of network topology reconfigurations in tomorrow's power grids. High-throughput synchrophasor and intelligent electronic device readings provide unprecedented instrumentation capabilities for generalized state estimation (GSE), which deals with ident...

Wireless sensor networks have recently received great attention from the scientic community, because they hold the key to revolutionize many aspects of our economy and life. On the other hand, the design, implementation and operation of a wireless sensor network in an SHM system requires the synergy of many disciplines, including civil engineering,...

Wireless Sensor Networks (WSNs) have recently received great attention from the scientific community, because they hold the key to revolutionize many aspects of the industry and our life. The process of collecting the measurements, acquired by a sensor network into a central sink node, constitutes one of the main challenges in this area of research...

Satellite communication channels can be well described as non-linear functions with memory. The Volterra series representation provides accurate modeling of satellite channel dynamics, and thus, it constitutes a widely used approach to mathematically describe them. In this work, iterative correction of the non-linear distortion introduced by such c...

Localization of an isotropic acoustic source using energy measurements from distributed sensors is considered. While most acoustic source localization algorithms require that distance estimates between the sensors and the source of interest are available, we propose a linear least squares criterion that does not make such an assumption. The new cri...

This paper presents an overview of a computer- aided system for the detection of carcinomas in the prostate gland. The proposed system incorporates information from two different types of magnetic resonance images (MRIs), namely the T2-weighted morphological images and the T1-weighted dynamic contrast enhanced (DCE) images, to extract discriminativ...

Localization of an isotropic source using energy measure-ments from distributed sensors is considered. Usually, such localization techniques require that the distances between the sensor nodes and the source of interest have been previously estimated. This, in turn, requires that sufficient information about the energy decay model as well as the tr...

This paper presents a computer-aided diagnosis scheme for the detection of prostate cancer. The pattern recognition scheme proposed, utilizes fused dynamic and morphological features extracted from magnetic resonance images (MRIs). The performance of the proposed scheme has been evaluated through extensive training and testing on several patient ca...

A low-complexity technique for multiple-source localization by wireless sensor networks is presented. The proposed technique is based on the Received Signal Strength (RSS) measurements of sig-nals emitted by the sources. Also, all processing is performed at the sensor nodes in a decentralized fashion, and hence no "Fusion Center" is required. The p...

Soft-input soft-output (SISO) equalizers based on linear filters have proven to be good, low complexity, alternatives to trellis-based SISO equalizers. In particular, the soft interference canceller (SIC) has recently received great interest, especially for receivers performing turbo equalization. In this paper, we modify the way in which the SIC i...

In this paper a new Soft Input – Soft Output (SISO) equalizer of linear complexity is developed. The algorithm can be used in the so-called Turbo Equalization scheme as a low cost solution in place of the Maximum A-Posteriori (MAP) equalization algorithm which has a prohibitive complexity for most real world applications. The proposed equalizer con...

In this paper a new soft input -soft output (SISO) equalizer of linear complexity is developed. The algorithm can be used in the so-called Turbo equalization scheme as a low cost solu-tion in place of the Maximum A-Posteriori (MAP) equaliza-tion algorithm which has a prohibitive complexity for most real world applications. The proposed equalizer co...