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Combat helmets are the primary system for protecting the head against ballistic impacts. Generally, combat helmets have been evaluated using a ballistic plasticine head surrogate based on international standards. More realistic human head models have recently been introduced to assess combat helmet performance considering biomechanical requirements. In this work, the Hybrid III dummy head and neck has been introduced to evaluate the performance of the combat helmet against the ballistic impact of live ammunition at different impact locations, considering two different thicknesses of the padding system. A numerical model including a helmet and a Hybrid III head and neck, is developed and validated with our experimental data. The results reveal the influence of the location, where the rear impact leads to the highest risk of brain damage. The effect of pad thickness is closely related to the energy absorbed by the helmet, the backface deformation (BFD), the contact force and the acceleration measured on the head. The authors acknowledge the Ministry of Economy and Competitiveness of Spain and FEDER program under Project RTC-2015-3887-8 and Project DPI2017-88166-R for the financial support of the work. M Rodriguez-Millan acknowledges the Spanish Ministry of Universities, National Program for the Promotion of Talent and its Employability in R&D&I, National Mobility Subprogram of the National Plan for Scientific and Technical Research and Innovation 2021-2023, for the professor's mobility program (PRX21/00329).

A great interest in the research of audio-visual speech recognition (AVSR) systems is driven by the increase in the number of multimedia applications that require robust speech recognition systems. The use of visual features in AVSR is justified by both the audio and visual modality of the speech generation and the need for features that are invariant to acoustic noise perturbation. The performance of the AVSR system relies on a robust set of visual features obtained from the accurate detection and tracking of the mouth region. Therefore the mouth tracking plays a major role in AVSR systems. This paper presents an improvement version of mouth tracking technique using radial basis function neural network (RBF NN) with its applications to AVSR systems. A modified extended Kalman filter (EKF) is used to adjust the parameters of the RBF NN. Simulation results have revealed good performance of the proposed method.

Aims: Intravascular lymphomatosis (IVL) is a very rare non-Hodgkin's lymphoma characterized by proliferation of lymphoma cells in the vascular lumina without involvement of adjacent parenchymal tissue. IVL is predominantly of B-cell lineage, but occasional cases of T lineage IVL involving almost exclusively the skin have been described. A case of IVL that occurred initially in the epididymis of a patient with an antecedent nasopharyngeal carcinoma was studied to define the clinicopathological features associated with this unique presentation. Methods and results: This lymphoma was studied by standard histological and immunophenotyping methods. The results showed lymphoma cells confined within the blood vessels, which expressed leucocyte common antigen, and T-cell markers CD3 and UCHL-1. The T-cell origin of the IVL prompted investigations for an assciation with Epstein–Barr virus infection (EBV). In-situ hybridization with digoxigenin-labelled anti-sense RNA probes to EBV encoded RNA (EBER) showed strong signals in the nuclei of virtually all of the lymphoma cells. Conclusions: EBV infection of the malignant cells was demonstrated by in-situ hybridization. This case suggests that T-cell IVL may be another EBV related human neoplasm. This observation will need to be validated by further studies.

This paper describes a constrained adaptive scheme for time delay estimation in the presence of multipath propagation. Two configurations are proposed. In either case, the role of the adaptive filters is to provide appropriate time shifts to the input signals. For a filter to function as a pure time shifter, its coefficients must take on the values of the samples of a sinc function. Applying this constraint on the coefficients, which are adapted by the LMS algorithm, reduces the computations and speeds up the convergence rate considerably. Indeed, since the number of adaptive coefficients is typically in the order of 40 or more, convergence time would be excessive without constraints. The effectiveness of the scheme is demonstrated by Simulation results, showing its ability to track accurately time-varying parameters