Consumer perceptions of food safety risk: Evidence from a segmentation study in Albania

Albania is facing serious problems with the national food safety control system in terms of legislation, control and enforcement. The objective of this paper is to analyse consumer perceptions about safety of small ruminant meat in Tirana, in a context of weak enforcement of the food safety system. Applying two-step clustering analyses, consumers were classified in four socio-demographic clusters, and it was found that the cluster composed of female consumers with lower education and income levels, and the two clusters composed of male consumers perceive consumed meat as safe. Consumers in the cluster composed of females with university education and higher income are, on average, more concerned with current meat safety measures and tend to place more trust in the veterinarian stamp on meat carcasses rather than in local butchers.

Feature Extraction of Radar Range Profiles Based on Normalized Central Moments

The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.

Improved modulation format identification based on Stokes parameters using combination of fuzzy c-means and hierarchical clustering in coherent optical communication system

In this Letter, we develop the Stokes space-based method for modulation format identification by combing power spectral density and a cluster analysis to identify quadrature amplitude modulation（QAM） and phase-shift keying（PSK） signals. Fuzzy c-means and hierarchical clustering algorithms are used for the cluster analysis.Simulations are conducted for binary PSK, quadrature PSK, 8PSK, 16-QAM, and 32-QAM signals. The results demonstrate that the proposed technique can effectively classify all these modulation formats, and that the method is superior in lowering the threshold of the optical signal-to-noise ratio. Meanwhile, the proposed method is insensitive to phase offset and laser phase noise.

A trial of using the cluster analysis to classify the ship noises and EEG (electroencephalogram)

Cluster analysis is a method often used in pattern recognition. With the aid of the signal processing and the learning of the computer, disfferent samples can be classifeid and recognized in a dimension reduction space of the characteristics because of the differences of their character -istics. To realize dimension reduction transformation, a nonlinear mapping method was discussed in this paper. To prove that the cluster analysis is suitable for quite different fields of samples, in this paper some ship noises and some EEG as the samples belong to two different fields are classified and shown. And it is worthy to point out that an adaptive step size expression of adaptive iteration deduced here will also be effective if it is applied to speed adaptive algorithm convergence of general signal processing.