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COPELABS - Cognitive and People-centric Computing R&D Unit
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Energy-Based Acoustic Localization by Improved Elephant Herding Optimization
Publication . Correia, Sérgio D.; Beko, Marko; Tomic, Slavisa; Da Silva Cruz, Luis A.; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; CTS - Centro de Tecnologia e Sistemas; Institute of Electrical and Electronics Engineers (IEEE)
The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.
Performance Analysis of Massive MIMO-OFDM System Incorporated with Various Transforms for Image Communication in 5G Systems
Publication . Kansal, Lavish; Berra, Salah; Mounir, Mohamed; Miglani, Rajan; Dinis, Rui; Rabie, Khaled; DEE2010-A1 Telecomunicações; DEE - Departamento de Engenharia Electrotécnica e de Computadores; MDPI - Multidisciplinary Digital Publishing Institute
Modern-day applications of fifth-generation (5G) and sixth-generation (6G) systems require fast, efficient, and robust transmission of multimedia information over wireless communication medium for both mobile and fixed users. The hybrid amalgamation of massive multiple input multiple output (mMIMO) and orthogonal frequency division multiplexing (OFDM) proves to be an impressive methodology for fulfilling the needs of 5G and 6G users. In this paper, the performance of the hybrid combination of massive MIMO and OFDM schemes augmented with fast Fourier transform (FFT), fractional Fourier transform (FrFT) or discrete wavelet transform (DWT) is evaluated to study their potential for reliable image communication. The analysis is carried over the Rayleigh fading channels and M-ary phase-shift keying (M-PSK) modulation schemes. The parameters used in our analysis to assess the outcome of proposed versions of OFDM-mMIMO include signal-to-noise ratio (SNR) vs. peak signal-to-noise ratio (PSNR) and SNR vs. structural similarity index measure (SSIM) at the receiver. Our results indicate that massive MIMO systems incorporating FrFT and DWT can lead to higher PSNR and SSIM values for a given SNR and number of users, when compared with in contrast to FFT-based massive MIMO-OFDM systems under the same conditions.
Fast matrix inversion based on Chebyshev acceleration for linear detection in massive MIMO systems
Publication . Berra, Salah; Dinis, Rui; Shahabuddin, Shahriar; DEE2010-A1 Telecomunicações; DEE - Departamento de Engenharia Electrotécnica e de Computadores; Institution of Engineering and Technology (IET)
To circumvent the prohibitive complexity of linear minimum mean square error detection in a massive multiple-input multiple-output system, several iterative methods have been proposed. However, they can still be too complex and/or lead to non-negligible performance degradation. In this letter, a Chebyshev acceleration technique is proposed to overcome the limitations of iterative methods, accelerating the convergence rates and enhancing the performance. The Chebyshev acceleration method employs a new vector combination, which combines the spectral radius of the iteration matrix with the receiver signal, and also the optimal parameters of Chebyshev acceleration have also been defined. A detector based on iterative algorithms requires pre-processing and initialisation, which enhance the convergence, performance, and complexity. To influence the initialisation, the stair matrix has been proposed as the first start of iterative methods. The performance results show that the proposed technique outperforms state-of-the-art methods in terms of error rate performance, while significantly reducing the computational complexity.
Simultaneous Wireless Information and Power Transfer in 5G communication
Publication . Rajaram, Akashkumar; Dinis, Rui; Jayakody, Dushantha
Green communication technology is expected to be widely adopted in future generation
networks to improve energy efficiency and reliability of wireless communication network.
Among the green communication technologies,simultaneous wireless information and
power transfer (SWIPT) is adopted for its flexible energy harvesting technology through
the radio frequency (RF) signa lthati sused for information transmission. Even though
existing SWIPT techniques are flexible and adoptable for the wireless communication
networks, the power and time resources of the signal need to be shared between infor-
mation transmission and RF energy harvesting, and this compromises the quality of the
signal. Therefore,SWIP Ttechniques need to be designed to allow an efficient resource
allocation for communication and energy harvesting.
The goal oft his thesisis to design SWIP Ttechniques that allow efficient,reliable and
secure joint communications and power transference. A problem associated to SWIPT
techniques combined with multi carrier signals is that the increased power requirements
inherent to energy harvesting purposes can exacerbate nonlinear distortion effects at the
transmitter. Therefore, we evaluate nonlinear distortion and present feasible solutions to
mitigate the impact of nonlinear distortion effects on the performance.Another goal of
the thesisis to take advantage of the energy harvesting signals in SWIP Ttechniques for
channel estimation and security purposes.Theperformance of these SWIPT techniques is
evaluated analytically, and those results are validated by simulations. It is shownthatthe
proposed SWIPT schemes can have excellent performance, out performing conventional
SWIPT schemes.
Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning
Publication . Freitas, Cristina; Eleutério, João; Soares, Gabriela; Enea, Maria; Nunes, Daniela; Fortunato, Elvira; Martins, Rodrigo; Águas, Hugo; Pereira, Eulália; Vieira, Helena L. A.; Ferreira, Lúcio Studer; Franco, Ricardo; CENIMAT-i3N - Centro de Investigação de Materiais (Lab. Associado I3N); DCM - Departamento de Ciência dos Materiais; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de Química; MDPI - Multidisciplinary Digital Publishing Institute
Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the development of low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS–plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay’s potential for immediate clinical decision-making.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/04111/2020