Secret-Key Agreement by Asynchronous EEG over Authenticated Public Channels.

In this paper, we propose a new system for a sequential secret key agreement based on 6 performance metrics derived from asynchronously recorded EEG signals using an EMOTIV EPOC+ wireless EEG headset. Based on an extensive experiment in which 76 participants were engaged in one chosen mental task, the system was optimized and rigorously evaluated. The system was shown to reach a key agreement rate of 100%, a key extraction rate of 9%, with a leakage rate of 0.0003, and a mean block entropy per key bit of 0.9994. All generated keys passed the NIST randomness test. The system performance was almost independent of the EEG signals available to the eavesdropper who had full access to the public channel.

A Generic Model of the Pseudo-Random Generator Based on Permutations Suitable for Security Solutions in Computationally-Constrained Environments.

Symmetric cryptography methods have an important role in security solutions design in data protection. In that context, symmetric cryptography algorithms and pseudo-random generators connected with them have strong influence on designed security solutions. In the computationally constrained environment, security efficiency is also important. In this paper we proposed the design of a new efficient pseudo-random generator parameterized by two pseudo-random sequences. By the probabilistic, information-theoretic and number theory methods we analyze characteristics of the generator. Analysis produced several results. We derived sufficient conditions, regarding parameterizing sequences, so that the output sequence has uniform distribution. Sufficient conditions under which there is no correlation between parameterizing sequences and output sequence are also derived. Moreover, it is shown that mutual information between the output sequence and parameterizing sequences tends to zero when the generated output sequence length tends to infinity. Regarding periodicity, it is shown that, with appropriately selected parameterizing sequences, the period of the generated sequence is significantly longer than the periods of the parameterizing sequences. All this characteristics are desirable regarding security applications. The efficiency of the proposed construction can be achieved by selection parameterizing sequences from the set of efficient pseudo-random number generators, for example, multiple linear feedback shift registers.

Digital Magnetic Compass Integration with Stationary, Land-Based Electro-Optical Multi-Sensor Surveillance System.

Multi-sensor imaging systems using the global navigation satellite system (GNSS) and digital magnetic compass (DMC) for geo-referencing have an important role and wide application in long-range surveillance systems. To achieve the required system heading accuracy, the specific magnetic compass calibration and compensation procedures, which highly depend on the application conditions, should be applied. The DMC compensation technique suitable for the operation environment is described and different technical solutions are studied. The application of the swinging procedure was shown as a good solution for DMC compensation in a given application. The selected DMC was built into a system to be experimentally evaluated, both under laboratory and field conditions. The implementation of the compensation procedure and magnetic sensor integration in systems is described. The heading accuracy measurement results show that DMC could be successfully integrated and used in long-range surveillance systems providing required geo-referencing data.

Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors.

In this paper, a new adaptive robustified filter algorithm of recursive weighted least squares with combined scale and variable forgetting factors for time-varying parameters estimation in non-stationary and impulsive noise environments has been proposed. To reduce the effect of impulsive noise, whether this situation is stationary or not, the proposed adaptive robustified approach extends the concept of approximate maximum likelihood robust estimation, the so-called M robust estimation, to the estimation of both filter parameters and noise variance simultaneously. The application of variable forgetting factor, calculated adaptively with respect to the robustified prediction error criterion, provides the estimation of time-varying filter parameters under a stochastic environment with possible impulsive noise. The feasibility of the proposed approach is analysed in a system identification scenario using finite impulse response (FIR) filter applications.

Emission factor estimation of ca. 160 emerging organic microcontaminants by inverse modeling in a Mediterranean river basin (Llobregat, NE Spain).

Starting from measured river concentrations, emission factors of 158 organic compounds out of 199 analyzed belonging to different groups of priority and emerging contaminants [pesticides (25), pharmaceuticals and hormones (81), perfluoroalkyl substances (PFASs) (18), industrial compounds (12), drugs of abuse (8) and personal care products (14)] have been estimated by inverse modeling. The Llobregat river was taken as case study representative of Mediterranean rivers. Industrial compounds and pharmaceuticals are the dominant groups (range of 10(4) mg·1000 inhab(-1)·d(-1)). Personal care products, pesticides, PFASs and illegal drugs showed a load approximately one order of magnitude smaller. Considered on a single compound basis industrial compounds still dominate (range of ca. 10(3) mg·1000 inhab(-1)·d(-1)) over other classes. Generally, the results are within the range when compared to previously published estimations for other river basins. River attenuation expressed as the percentage fraction of microcontaminants eliminated was quantified. On average they were around 60-70% of the amount discharged for all classes, except for PFASs, that are poorly eliminated (ca. 20% on average). Uncertainties associated with the calculated emissions have been estimated by Monte-Carlo methods (15,000 runs) and typically show coefficients of variation of ca. 120%. Sensitivities associated with the various variables involved in the calculations (river discharge, river length, concentration, elimination constant, hydraulic travel time and river velocity) have been assessed as well. For the intervals chosen for the different variables, all show sensitivities exceeding unity (1.14 to 3.43), tending to amplify the variation of the emission. River velocity and basin length showed the highest sensitivity value. Even considering the limitations of the approach used, inverse modeling can provide a useful tool for management purposes facilitating the quantification of release rates of chemicals into the aquatic environment.