Quantum key distribution (QKD), another name for quantum cryptography, is the most advanced subfield of quantum information and communication technology (QICT). The first QKD protocol was proposed in 1984, and since then, more protocols have been proposed. It uses quantum mechanics to enable secure exchange of cryptographic keys. In order to have high confidence in the security of the QKD protocols, such protocols must be proven to be secure against any arbitrary attacks. In this chapter, we discuss and demonstrate security proofs for QKD protocols. Security analysis of QKD protocols can be categorised into two techniques, namely infinite-key and finite-key analyses. Finite-key analysis offers more realistic results than the infinite-key one, while infinite-key analysis provides more simplicity. We briefly provide the background of QKD and also define the basic notion of security in QKD protocols. The cryptographic key is shared between Alice and Bob. Since the key is random and unknown to an eavesdropper, Eve, she is unable to learn anything about the message simply by intercepting the ciphertext. This phenomenon is beyond the ability of classical information processing. We then study some tools that are used in the derivation of security proofs for the infinite- and finite-length key limits.
Part of the book: Advanced Technologies of Quantum Key Distribution
Weather variations play a significant role in peoples’ short-term, medium-term or long-term planning. Therefore, understanding of weather patterns has become very important in decision making. Short-term weather forecasting (nowcasting) involves the prediction of weather over a short period of time; typically few hours. Different techniques have been proposed for short-term weather forecasting. Traditional techniques used for nowcasting are highly parametric, and hence complex. Recently, there has been a shift towards the use of artificial intelligence techniques for weather nowcasting. These include the use of machine learning techniques such as artificial neural networks. In this chapter, we report the use of deep learning techniques for weather nowcasting. Deep learning techniques were tested on meteorological data. Three deep learning techniques, namely multilayer perceptron, Elman recurrent neural networks and Jordan recurrent neural networks, were used in this work. Multilayer perceptron models achieved 91 and 75% accuracies for sunshine forecasting and precipitation forecasting respectively, Elman recurrent neural network models achieved accuracies of 96 and 97% for sunshine and precipitation forecasting respectively, while Jordan recurrent neural network models achieved accuracies of 97 and 97% for sunshine and precipitation nowcasting respectively. The results obtained underline the utility of using deep learning for weather nowcasting.
Part of the book: Data Mining