Food companies worldwide must constantly engage in product development to stay competitive, cover existing markets, explore new markets, and meet key consumer requirements. This ongoing development places high demands on achieving quality at all levels, particularly in terms of food safety, integrity, quality, nutrition, and other health effects. Food product research is required to convert the initial product idea into a formulation for upscaling production with ensured significant results. Sensory evaluation is an effective component of the whole process. It is especially important in the last step in the development of new products to ensure product acceptance. In that stage, measurements of product aroma play an important role in ensuring that consumer expectations are satisfied. To this end, the electronic nose (e-nose) can be a useful tool to achieve this purpose. The e-nose is a combination of various sensors used to detect gases by generating signals for an analysis system. Our research group has investigated the scent factor in some foodstuff and attempted to develop e-noses based on low-cost technology and compact size. In this paper, we present a summary of our research to date on applications of the e-nose in the food industry.

By any measure, wireless communications is one of the most evolving fields in engineering. This, in return, has imposed many challenges, especially in handling the hunger for higher data rates in the next generation wireless networks. Among these challenges is how to provide the needed resources in terms of the electromagnetic radio spectrum for these networks. In this regard, cognitive radio (CR) based on dynamic spectrum access (DSA) has been attracting huge attention as a promising solution for more efficient utilization of the available radio spectrum. DSA is based on finding and opportunistically accessing the free-of-use portions of spectrum. To facilitate DSA, spectrum sensing can be used. However, spectrum sensing faces many challenges in different aspects. Such aspects include blind sensing and sensing optimization, which are both to a great extent measurement challenges. We discuss different contributions in addressing these two challenges in this article.