Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes,and help people live well in mosquito-infested areas.In this study,we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things.In our method,decision-making is controlled by a deep learning model,and the proposed method uses infrared sensors and an array of pressure sensors to collect data.Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model,determining automatically the intention of the user to open or close the mosquito net.We used optical flow to extract pressure map features,and they were fed to a 3-dimensional convolutional neural network(3D-CNN)classification model subsequently.We achieved the expected results using a nested cross-validation method to evaluate our model.Deep learning has better adaptability than the traditional methods and also has better anti-interference by the different bodies of users.This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

Improving Language Translation Using the Hidden Markov Model

Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.

A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding

Since the use of a quantum channel is very expensive for transmitting large messages, it is vital to develop an effective quantum compression encoding scheme that is easy to implement. Given that, with the single-photon spin-orbit entanglement, we propose a quantum secret sharing scheme using orbital angular momentum onto multiple spin states based on Fibonacci compression encoding. In our proposed scheme, we can represent the frequency of any secret message which is typically collection of bits encodings of text or integers as a bitstring using the base Fibonacci sequence, which is encoded multiple spin states for secret shares transmitted to participants. We demonstrate that Fibonacci compression encoding carries excellent properties that enable us to achieve more robust quantum secret sharing schemes with fewer number of photons.