Multimodal images (MIs) can capture different modalities of a scene with multiple applications in medicine, remote sensing, food inspection, among others. Over a 2D domain, these images acquire spectral/morphological/temporal information of each spatial point. Unmixing methodologies can decompose this spatial and spectral/morphological/temporal information. In this letter, a unified framework is proposed for unsupervised unmixing, explicitly accounting for Gaussian and sparse noise. Our approach is novel in three key aspects: (i) addresses the general case of multimodal images, (ii) unifies linear and multilinear mixing models, and (iii) incorporates noise effects into the synthesis schemes. The proposed methodology relies on cyclic coordinate descent optimization (CCDO), constrained quadratic estimation, and L1-regularization. For the validation stage, two types of synthetic MIs were used with additive Gaussian and sparse noise terms. Additionally, the Urban dataset was employed for further validation to consider a real-world scenario. The results show that the proposed methodologies provide accurate reconstructions of the datasets, as well as the ground-truth abundance maps and end-members with low computational time.

Today, hyperspectral (HS) imaging has become a powerful tool to identify remotely the composition of an interest area through the joint acquisition of spatial and spectral information. However, like in most imaging techniques, unwanted effects may occur during data acquisition, such as noise, changes in light intensity, temperature differences, or optical variations. In HS imaging, these problems can be attenuated using a reflectance calibration stage and optical filtering. Nevertheless, optical filtering might induce some distortion that could complicate the posterior image processing stage. In this work, we present a new proposal for reflectance calibration that compensates for optical alterations during the acquisition of an HS image. The proposed methodology was evaluated on an HS image of synthetic squares of various materials with specific spectral responses. The results of our proposal show high performance in two classification tests using the K-means algorithm with 97% and 88% accuracy; in comparison with the standard reflectance calibration from the literature that obtained 77% and 64% accuracy. These results illustrate the performance gain of the proposed formulation, which besides maintaining the characteristic features of the compounds within the HS image, keeps the resulting reflectance into fixed lower and upper bounds, which avoids a post-calibration normalization step.

Blood perfusion parameters can be used to evaluate the micro-circulatory health condition of a patient. Several non-invasive optical techniques have been used to estimate blood perfusion as near-infrared spectroscopy or pulse-oximetry. However, these techniques require contact with the patient, and the measurements are restricted to a single point evaluation. These disadvantages could be solved by multispectral imaging. Hence, this paper presents an approach based on multispectral imaging and blind linear unmixing, as an alternative to estimate blood perfusion parameters in the hand palm. This work evaluated changes in oxygenated and deoxygenated hemoglobin concentrations by employing an experimental occlusion protocol in healthy volunteers. We compared the results of several blind linear unmixing and linear regression models. The average cosine similarity values between the prediction model and the photoplethysmography estimations varied in the range 87% and 96%. The mean R-squared adjusted values for oxygenated and deoxygenated hemoglobin were greater or equal than 0.75 and 0.84, respectively. Our results demonstrated the feasibility of non-invasive estimation of hemoglobin in the hand palm, and opened the possibility for calculating other perfusion parameters that help to diagnose and monitor pathologies in large tissue regions.

Glioblastoma is considered an aggressive tumor due to its rapid growth rate and diffuse pattern in various parts of the brain. Current in-vivo classification procedures are executed under the supervision of an expert. However, this methodology could be subjective and time-consuming. In this work, we propose a classification method for in-vivo hyperspectral brain images to identify areas affected by glioblastomas based on nonlinear spectral unmixing. This methodology follows a semi-supervised approach for the estimation of the end-members in a multi-linear model. To improve the classification results, we vary the number of end-members per-class to address spectral variability of each studied type of tissue. Once the set of end-members is obtained, the classification map is generated according to the end-member with the highest abundance in each pixel, followed by morphological operations to smooth the resulting maps. The classification results demonstrate that the proposed methodology generates high performance in the regions of interest, with an accuracy above 0.75 and 0.96 in the inter and intra-patient strategies, respectively. These results indicate that the proposed methodology has the potential to be used as an assistant tool in the diagnosis of glioblastoma in hyperspectral imaging.

In this study, a new framework for multimodal image registration is proposed based on the expectation–maximisation (EM) methodology. This framework allows to address simultaneously parametric and elastic registrations independently on the modality of the target and source images without making any assumptions about their intensity relationship. The EM formulation for the image registration problem leads to a regularised quadratic optimisation scheme to compute the displacement vector field (DVF) that aligns the images and depends on their joint intensity distribution. At the first stage, a parametric transformation is assumed for the DVF, where the resulting quadratic optimisation is computed recursively to calculate its optimal parameters. Next, a general unknown deformation models the elastic part of the DVF, which is represented by an additive structure. The resulting optimisation process by the EM formulation results in a cost function that involves data and regularisation terms, which is also solved recursively. A comprehensive evaluation of the parametric and elastic proposals is carried out by comparing to state‐of‐the‐art algorithms and images from different application fields, where an advantage is visualised by the authors’ proposal in terms of a compromise between accuracy and robustness.

This paper deals with the modeling of nonstationary time-frequency (TF) dispersive multipath fading channels for vehicle-to-vehicle (V2V) communication systems. As a main contribution, the paper presents a novel geometry-based statistical channel model that facilitates the analysis of the nonstationarities of V2V fading channels arising at a small-scale level due to the time-varying nature of the propagation delays. This new geometrical channel model has been formulated following the principles of plane wave propagation (PWP) and assuming that the transmitted signal reaches the receiver antenna through double interactions with multiple interfering objects (IOs) randomly located in the propagation area. As a consequence of such interactions, the first-order statistics of the channel model’s envelope are shown to follow a worse-than-Rayleigh distribution; specifically, they follow a double-Rayleigh distribution. General expressions are derived for the envelope and phase distributions, four-dimensional (4D) TF correlation function (TF-CF), and TF-dependent delay and Doppler profiles of the proposed channel model. Such expressions are valid regardless of the underlying geometry of the propagation area. Furthermore, a closed-form solution of the 4D TF-CF is presented for the particular case of the geometrical two-ring scattering model. The obtained results provide new theoretical insights into the correlation and spectral properties of small-scale nonstationary V2V double-Rayleigh fading channels.

In this paper, we analyze the first-order (FO) statistics of two new geometry-based statistical models for small-scale nonstationary time-frequency (TF) dispersive mobile-to-mobile (M2M) fading channels. We derive exact analytic expressions for the joint and marginal probability density functions (PDFs) of the envelope and phase of both models. In addition, we analyze the asymptotic behavior of these PDFs as the number of multipath components of the received signal approaches infinity. The obtained results are of practical relevance, as they can be used as a benchmark to investigate the influence of the small-scale fading statistics of nonstationary channels on the M2M communication systems' performance.

The use of multiple transmit and receive antennas is widely recognised as an effective technology to boost the capacity of wireless communication systems. Moreover, the combination of multiple‐input multiple‐output (MIMO) systems with multicarrier code division multiple access (MC‐CDMA) offers a strong alternative to satisfy the demand for high data rates with rigorous quality‐of‐service (QoS) restrictions. In this study, this paper applies a closed‐loop pre‐equalisation methodology under a unified framework for MIMO and MC‐CDMA systems that satisfies the QoS target with a single‐user‐based detector while minimising the power of the pre‐equalisation factors. It is of particular interest to investigate the impact and limitations of combining the robustness of the feedback scheme with the degrees of freedom available in the system, given in terms of the number of subcarriers and multiple antennas. The contribution of this work includes the derivation of the distributed and centralised optimal closed‐loop pre‐equalisation solutions under the MIMO–MC‐CDMA structure. The results and analysis illustrate important gains in the form of power savings, enabled by the spatial diversity of the MIMO scheme.

In this paper, we discuss a problem in the formulation of geometry-based statistical models for multiple-input multi-ple-output (MIMO) mobile-to-mobile (M2M) fading channels that have been proposed under the plane wave propagation (PWP) assumption. The problem is caused by an oversimplification of the PWP model in the characterization of the channel complex envelope (CCE). This modeling imprecision has led to impose an unnecessary restriction on the size of the antenna arrays to warrant the mathematical tractability of the geometrical PWP channel models. The results presented in previous papers for relevant channel statistics, such as the space-time cross-correlation function (ST-CCF), are not invalidated if the problem is corrected. On the contrary, a thorough description of the physical process of PWP reduces the number of approximations required to obtain such results, extending in consequence their validity to a wider range of propagation scenarios.

In this paper, a closed-loop pre-equalization approach for multiuser multicarrier code division multiple access (MC-CDMA) systems under a multiple-input multiple-output (MIMO) framework is presented. This work investigates the impact and limitations of combining the robustness of a feedback scheme with the degrees of freedom available in the system, given in terms of the number of subcarriers and multiple antennas, while satisfying the quality of service (QoS) requirements. Simulation results show that the derived closed-loop pre-equalization scheme takes advantage of the spatial diversity, induced by the transmit antennas, to yield a notable power reduction while achieving the objective QoS under a severe system operation. Further investigations are required to identify the impact of the receive antennas in the MIMO system.

In this study, the problem of optimal synthesis of pre‐equalisation factors for a multi‐carrier code division multiple‐access wireless network is studied under different quality of service (QoS) restrictions and performance objectives. The downlink is particularly addressed, where the QoS is evaluated by the signal‐to‐interference‐noise ratio (SINR) after signal detection by matched filtering. First, the minimum energy formulation under inequality QoS constraints is analysed, and it was concluded that the optimal performance is always achieved at the lower bounds. Next, the minimisation of the peak magnitude of the pre‐equalisation factors was addressed by an optimal synthesis scheme through the infinity norm, where a linear programming strategy is proposed by using an upper bound. Finally, by combining the minimum energy and minimum peak strategies, the minimisation of an upper bound on the peak‐to‐average‐power ratio (PAPR) is suggested, which can be solved through quadratic programming by incorporating the QoS constraints. The new synthesis schemes take into account two important transmitter‐based properties: peak magnitude and PAPR. These ideas were extensively validated in simulation, where the synthesis schemes were compared by Monte Carlo tests at different load and noise levels, and objective SINRs.

This paper presents a novel non‐rigid multimodal registration method that relies on three basic steps: first, an initial approximation of the deformation field is obtained by a parametric registration technique based on particle filtering; second, an intensity mapping based on local variability measures (LVM) is applied over the two images in order to overcome the multimodal restriction between them; and third, an optical flow method is used in an iterative way to find the remaining displacements of the deformation field. Hence the new methodology offers a solution for multimodal NRR by a quadratic optimisation over a convex surface, which allows independent motion of each pixel, in contrast to methods that parameterise the deformation space. To evaluate the proposed method, a set of magnetic resonance/computed tomography clinical studies (pre‐ and post‐radiotherapy treatment) of three patients with cerebral tumour deformations of the brain structures was employed. The resulting registration was evaluated both qualitatively and quantitatively by standard indices of correspondence over anatomical structures of interest in radiotherapy (brain, tumour and cerebral ventricles). These results showed that one of the proposed LVM (entropy) offers a superior performance in estimating the non‐rigid deformation field.

This paper studies the problem of downlink pre-equalization in a multicarrier wireless network under utility and sum-rate maximization. The network utility depends on two main factors: an efficiency mapping which is a function of the signal-to-interference-noise ratio (SINR), and the power of the pre-equalization variables. Meanwhile, the sum-rate index is constructed by the summation of a logarithmic function of the SINRs of the active users. The network utility and sum-rate maximization problems are formulated here by assuming that the data estimation at the mobile units (MUs) is carried out by a simple matched filter. The optimal solutions are derived in this work for the pre-equalization factors by following a strategy that is distributed with respect to the channel state information (CSI) and data rates, but centralized with respect to the spreading sequences of all active users. The construction of these optimal solutions requires in each case a two step procedure. First, the optimal SINR is computed for the maximum utility and sum-rate solutions based on the efficiency mapping and on the bound of the transmission power, respectively. Then, exploiting the induced diversity by the multiple subcarriers, the pre-equalization algorithms cancel the multiple-access interference (MAI) and channel distortions, minimize the transmission power and achieve the optimal SINR after signal detection. A comprehensive numerical evaluation is presented at the end of the paper to validate the analytical derivations. Applications of the proposed pre-equalization schemes could be associated among others to cellular communication systems, and wireless networks of actuators.

This paper proposes a new blind end-member and abundance extraction (BEAE) method for multispectral fluorescence lifetime imaging microscopy (m-FLIM) data. The chemometrical analysis relies on an iterative estimation of the fluorescence decay end-members and their abundances. The proposed method is based on a linear mixture model with positivity and sum-to-one restrictions on the abundances and end-members to compensate for signature variability. The synthesis procedure depends on a quadratic optimization problem, which is solved by an alternating least-squares structure over convex sets. The BEAE strategy only assumes that the number of components in the analyzed sample is known a spriori. The proposed method is first validated by using synthetic m-FLIM datasets at 15, 20, and 25 dB signal-to-noise ratios. The samples simulate the mixed response of tissue containing multiple fluorescent intensity decays. Furthermore, the results were also validated with six m-FLIM datasets from fresh postmortem human coronary atherosclerotic plaques. A quantitative evaluation of the BEAE was made against two popular techniques: minimum volume constrained nonnegative matrix factorization (MVC-NMF) and multivariate curve resolution-alternating least-squares (MCR-ALS). Our proposed method (BEAE) was able to provide more accurate estimations of the end-members: 0.32% minimum relative error and 13.82% worst-case scenario, despite different initial conditions in the iterative optimization procedure and noise effect. Meanwhile, MVC-NMF and MCR-ALS presented more variability in estimating the end-members: 0.35% and 0.34% for minimum errors and 15.31% and 13.25% in the worst-case scenarios, respectively. This tendency was also maintained for the abundances, where BEAE obtained 0.05 as the minimum absolute error and 0.12 in the worst-case scenario; MCR-ALS and MVC-NMF achieved 0.04 and 0.06 for the minimum absolute errors, and 0.15 and 0.17 under the worst-case conditions, respectively. In addition, the average computation time was evaluated for the synthetic datasets, where MVC-NMF achieved the fastest time, followed by BEAE and finally MCR-ALS. Consequently, BEAE improved MVC-NMF in convergence to a local optimal solution and robustness against signal variability, and it is roughly 3.6 time faster than MCR-ALS.

This work addresses an optimal pre‐equalisation scheme to provide power and phase compensation in multi‐carrier code division multiple access wireless systems under quality of service (QoS) restrictions. Two assignation schemes are studied: independent and uniform pre‐equalised transmission (IPT and UPT) per subcarrier. First, the QoS was quantified in terms of the signal‐to‐interference noise ratio (SINR) after using a linear detector in the receiver. The SINR expressions for IPT and UPT were explicitly derived, to highlight their dependence on the pre‐equalisation factors. These relations are exploited to derive analytical procedures to obtain the required pre‐equalisation factors that satisfy an objective SINR per active user. The author's proposal for IPT achieves the lowest power while requiring a simple detector independent of the channel state information (CSI). For IPT, two minimum norm solutions are derived by translating the SINR restrictions into linear equations: one based on spectral factorisation, and a second one based on a simultaneous zero‐forcing effect plus an energy scaling. On the other hand, for UPT, multi‐user detectors like zero‐forcing (ZF) and minimum‐mean‐square error (MMSE) have to be employed at the receiver to achieve a solution to the QoS requirements. In the numerical evaluations, IPT attained roughly a 2.5–3% reduction in the overall transmission power compared to UPT with ZF and MMSE by varying the objective SINR and noise variance. Although this reduction is further expanded as the number of active users increases, achieving a reduction in transmission power larger than 50% for a fully‐loaded system.

The vast literature on power control for CDMA systems ignored the fact that propagation delays occur inevitably during data communication between the base station and the mobile units or viceversa. If these delays are not considered into the power control design stage, the performance of the resulting feedback system can be severely deteriorated or even instability could be triggered in a practical scenario. In this paper, we extend the conventional power control framework to a more practical scenario that explicitly incorporate into the analysis: 1) the propagation delays while information is exchanged between the base station and the mobile terminals, 2) the asynchronous transmissions in the uplink channels, generally neglected in the literature for power control algorithms. This framework is then used to propose a distributed power control strategy enhanced with linear multiuser receivers. It is shown that through a proper selection of an error function, the nonlinear coupling among active users is transformed into individual linear loops. Simulation results show a remarkable performance improvement by using jointly power control and signal detection strategies. It is also shown that the proposed power control algorithm is particularly robust for keeping closed-loop stability despite transmission delays.

In this paper, we investigate an optimised transmitter pre-filtering technique for downlink time-division-duplex (TDD) code division multiple access (CDMA) communications, which employs the conventional matched filter (MF) detector at the mobile receivers. The proposed pre-filtering technique eliminates the multiple-access interference and intersymbol interference (MAI/ISI) effects by applying a very simple transmission scheme that combines a signal transformation with a cyclic prefix strategy under a power constraint condition. Two constrained pre-filtering transformations are suggested depending on the information required at the mobile unit. An open-loop transmitter pre-filtering is first formulated; however, this solution does not consider the properties of the noise at the mobile receiver. A second solution is then presented via a closed-loop transmitter pre-filtering that includes an optimum gain for a given transmit and noise power. Some associated issues such as system efficiency, computational complexity and channel estimation errors are also addressed. Simulation results show that the proposed transmitter pre-filtering scheme can be used to increase the system performance and capacity. In addition, its performance is compared with another similar transmit pre-processing scheme in order to evaluate the performance improvement by the proposed algorithm.

In today's wireless systems, power control is an important problem which is related to the quality-of-service (QoS) and battery utilization at the mobile units. This problem is studied in this work for the uplink of a direct-sequence code- division multiple-access communication (DS-CDMA) system. By a proper selection of the error function, the resulting linear decoupled feedback loops can be controlled efficiently following a distributed power control approach, and these closed-loops are independent of the user's channel variations. A suboptimal LQ strategy is suggested and derived for power update. This control law has a variable structure that depends on the transmission roundtrip delay in the feedback system, which is a key parameter to establish closed-loop stability. It is demonstrated that in the limit of some synthesis parameters, the LQ-controller tends to a Dead-Beat response, improving the time response but sacrificing robustness. Meanwhile, the LQ-controller can be adjusted to increase its robustness. Simulation results are presented to compare the control algorithms using a standard single-step power correction approach.