The aims of this study are to investigate dietary factors, food intolerance, and the body mass index data, as an indicator of nutritional status, in functional dyspepsia patients. Forty-one functional dyspepsia patients and 30 healthy volunteers answered a standardized questionnaire to identify eating habits and food intolerance, and then completed a 7-day alimentary diary. There was no significant difference in daily total caloric intake between patients and controls. Patients associated their symptoms with the ingestion of several foods, but in general maintained their regular intake, with the exception of a small reduction in the proportion of fat in comparison with controls (median 28 vs. 34%; P = 0.001). No patient was underweight. In conclusion, our results suggest that food intolerance has no remarkable influence on food pattern and nutritional status in most functional dyspepsia patients. Further studies are necessary to clarify the role of fat in the generation of dyspeptic symptoms.

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In this paper we discuss the ability of channel codes to enhance cryptographic secrecy. Toward that end, we present the secrecy metric of degrees of freedom in an attacker's knowledge of the cryptogram, which is similar to equivocation. Using this notion of secrecy, we show how a specific practical channel coding system can be used to hide information about the ciphertext, thus increasing the difficulty of cryptographic attacks. The system setup is the wiretap channel model where transmitted data traverse through independent packet erasure channels with public feedback for authenticated ARQ (Automatic Repeat reQuest). The code design relies on puncturing nonsystematic low-density parity-check codes with the intent of inflicting an eavesdropper with stopping sets in the decoder. Furthermore, the design amplifies errors when stopping sets occur such that a receiver must guess all the channel-erased bits correctly to avoid an expected error rate of one half in the ciphertext. We extend previous results on the coding scheme by giving design criteria that reduces the effectiveness of a maximum-likelihood attack to that of a message-passing attack. We further extend security analysis to models with multiple receivers and collaborative attackers. Cryptographic security is enhanced in all these cases by exploiting properties of the physical-layer. The enhancement is accurately presented as a function of the degrees of freedom in the eavesdropper's knowledge of the ciphertext, and is even shown to be present when eavesdroppers have better channel quality than legitimate receivers.

Driver Assistance Systems have achieved a high level of maturity in the latest years. As an example of that, sophisticated pedestrian protection systems are already available in a number of commercial vehicles from several OEMs. However, accurate pedestrian path prediction is needed in order to go a step further in terms of safety and reliability, since it can make the difference between effective and non-effective intervention. In this paper, we consider the three-dimensional pedestrian body language in order to perform path prediction in a probabilistic framework. For this purpose, the different body parts and joints are detected using stereo vision. We propose the use of GPDM (Gaussian Process Dynamical Models) for reducing the high dimensionality of the input feature vector (composed by joints and displacement vectors) in the 3D pose space and for learning the pedestrian dynamics in a latent space. Experimental results show that accurate path prediction can be achieved at a time horizon of ≈ 0.8 s.

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