Teleportation is a new and exciting field of future communication. Security in data communication is a major headache now a day. Among the encryption technologies that are available at present, shared key is the most reliable which depends on secure key generation and distribution. Teleportation/ Entanglement is a perfect solution in this regard as for the no cloning theorem of quantum mechanics any attempt to intercept the key by the eavesdropper will be detectable immediately. To use the quantum key distribution efficiently and effectively, rigorous study and research on Teleportation/Entanglement is needed. We have reviewed and presented Teleportation concept, its process, road blocks, and successes that are achieved recently in a straightforward manner.
IntroductionFor artificial synapses whose strengths are assumed to be bounded and can only be updated with finite precision, achieving optimal memory consolidation using primitives from classical physics leads to synaptic models that are too complex to be scaled in-silico. Here we report that a relatively simple differential device that operates using the physics of Fowler-Nordheim (FN) quantum-mechanical tunneling can achieve tunable memory consolidation characteristics with different plasticity-stability trade-offs.MethodsA prototype FN-synapse array was fabricated in a standard silicon process and was used to verify the optimal memory consolidation characteristics and used for estimating the parameters of an FN-synapse analytical model. The analytical model was then used for large-scale memory consolidation and continual learning experiments.ResultsWe show that compared to other physical implementations of synapses for memory consolidation, the operation of the FN-synapse is near-optimal in terms of the synaptic lifetime and the consolidation properties. We also demonstrate that a network comprising FN-synapses outperforms a comparable elastic weight consolidation (EWC) network for some benchmark continual learning tasks.DiscussionsWith an energy footprint of femtojoules per synaptic update, we believe that the proposed FN-synapse provides an ultra-energy-efficient approach for implementing both synaptic memory consolidation and continual learning on a physical device.
IntroductionSecuring wireless communications in internet-of-things (IoT) requires both generation and synchronization of random numbers in real-time. However, resource constraints on an IoT device limit the use of computationally intensive random number generators and the use of global positioning systems (GPS) for synchronization. In this paper, we propose a synchronized pseudo-random number generator (SPRNG) that uses a combination of a fast, low-complexity linear-feedback-shift-register (LFSR) based PRNG and a slow but secure, synchronized seed generator based on self-powered timers.MethodsA prototype synchronized self-powered timer (SSPT) array was fabricated in a standard silicon process and was used to generate dynamic random seeds for the LFSR. The SSPTs use quantum-mechanical tunneling of electrons to operate without any external power and are practically secure against tampering, snooping, and side-channel attacks (both power and electromagnetic).ResultsIn this work, we explore protocols to periodically and securely generate random bits using the self-powered timers for seeding the LFSR. We also show that the time-varying random seeds extend and break the LFSR periodic cycles, thus making it difficult for an attacker to predict the random output or the random seed. Using the National Institute of Standards and Technology (NIST) test suite we verify the randomness of the measured seeds from the fabricated ensemble of SSPTs together with the random bit sequences generated by a software-seeded LFSR.DiscussionsIn this modality, the proposed SPRNG could be used as a trusted platform module (TPM) on IoTs and used for verifying and authenticating secure transactions (e.g., software upgrades). Since the SPRNG system does not require access to GPS for synchronization, therefore it could be used in many resource-constrained and adversarial environments.
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