A semi-blind reception scheme based on ICA for multiuser MIMO-STBC system

When the orthogonal space-time block code (STBC), or the Alamouti code, is applied on a multiple-input multiple-output (MIMO) communications system, the optimum reception can be achieved by a simple signal decoupling at the receiver. The performance, however, deteriorates significantly in presence of co-channel interference (CCI) from other users. In this paper, such CCI problem is overcome by applying the independent component analysis (ICA), a blind source separation algorithm. This is based on the fact that, if the transmission data from every transmit antenna are mutually independent, they can be effectively separated at the receiver with the principle of the blind source separation. Then equivalently, the CCI is suppressed. Although they are not required by the ICA algorithm itself, a small number of training data are necessary to eliminate the phase and order ambiguities at the ICA outputs, leading to a semi-blind approach. Numerical simulation is also shown to verify the proposed ICA approach in the multiuser MIMO system.

Discrete time representation of continuous time ARMA processes

This paper derives exact discrete time representations for data generated by a continuous time autoregressive moving average (ARMA) system with mixed stock and flow data. The representations for systems comprised entirely of stocks or of flows are also given. In each case the discrete time representations are shown to be of ARMA form, the orders depending on those of the continuous time system. Three examples and applications are also provided, two of which concern the stationary ARMA(2, 1) model with stock variables (with applications to sunspot data and a short-term interest rate) and one concerning the nonstationary ARMA(2, 1) model with a flow variable (with an application to U.S. nondurable consumers’ expenditure). In all three examples the presence of an MA(1) component in the continuous time system has a dramatic impact on eradicating unaccounted-for serial correlation that is present in the discrete time version of the ARMA(2, 0) specification, even though the form of the discrete time model is ARMA(2, 1) for both models.

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The threats range from explicit, e.g. a person attacking the truck driver, to implicit, e.g. somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The aim of this representation was to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6 % of the tracks are correctly interpreted, when considering the temporal context.