Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Adaptive compensation of analog front-end I/Q mismatches in digital receivers

I and Q Channel phase and gain misniatches are of great concern in communications receiver design. In this paper we analyse the effects of I and Q channel mismatches and propose a low-complexity blind adaptive algorithm to minimize this problem. The proposed solution consists of two, 2-tap adaptive filters, arranged in Adaptive Noise Canceller (ANC) set-up, with the output of one cross-fed to the input of the other. The system works as a de-correlator eliminating I and Q mismatch errors.