An Empirical Air-to-Ground Channel Model Based on Passive Measurements in LTE

In this paper, a recently conducted measurement campaign for unmanned-aerial-vehicle (UAV) channels is introduced. The downlink signals of an in-service long-time-evolution (LTE) network which is deployed in a suburban scenario were acquired. Five horizontal and five vertical flight routes were considered. The channel impulse responses (CIRs) are extracted from the received data by exploiting the cell specific signals (CRSs). Based on the CIRs, the parameters of multipath components (MPCs) are estimated by using a high-resolution algorithm derived according to the space-alternating generalized expectation-maximization (SAGE) principle. Based on the SAGE results, channel characteristics including the path loss, shadow fading, fast fading, delay spread and Doppler frequency spread are thoroughly investigated for different heights and horizontal distances, which constitute a stochastic model.Comment: 15 pages, submitted version to IEEE Transactions on Vehicular Technology. Current status: Early acces

Low Altitude Air-to-Ground Channel Characterization in LTE Network

Low altitude unmanned aerial vehicle (UAV)-aided applications are promising in the future generation communication systems. In this paper, a recently conducted measurement campaign for characterizing the low-altitude air-to-ground (A2G) channel in a typical Long Term Evolution (LTE) network is introduced. Five horizontal flights at the heights of 15, 30, 50, 75, and 100 m are applied, respectively. The realtime LTE downlink signal is recorded by using the Universal Software Radio Peripheral (USRP)-based channel sounder onboard the UAV. Channel impulse responses (CIRs) are extracted from the cell specific signals in the recorded downlink data. To shed lights on the physical propagation mechanisms, propagation graph simulation is exploited. Moreover, path loss at different heights are investigated and compared based on the empirical data. The simulated and empirical results provide valuable understanding of the low altitude A2G channels

Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 million (M) hours spanning over 300 languages, and fine-tuning on a smaller labeled dataset. We use multilingual pre-training with random-projection quantization and speech-text modality matching to achieve state-of-the-art performance on downstream multilingual ASR and speech-to-text translation tasks. We also demonstrate that despite using a labeled training set 1/7-th the size of that used for the Whisper model, our model exhibits comparable or better performance on both in-domain and out-of-domain speech recognition tasks across many languages.Comment: 20 pages, 7 figures, 8 table

A Comparative Study on Transformer vs RNN in Speech Applications

Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting outcomes.Comment: Accepted at ASRU 201

Healthy Breeding Service System of Vannamei Based on TD-SCDMA

Vannamei is recognized as one of the excellent variety of prawns in the world and has sanguine development foreground. However, the popularization of healthy farming technology in China is limited because of the weak agricultural information infrastructure. This paper takes full advantages of mobile network infrastructure and the features of surfing the Internet with smart phone, designs and implements the healthy breeding service system of vannamei based on TD-SCDMA, WAP and sentence similarity algorithm. Farmers can obtain real-time knowledge of healthy breeding, download training videos and receive early-warning information from the system. Furthermore, farmers can get SMS messages which contain video download addresses or early-warning information URLs chosen by users’ customized interests. This system proposes a better way for farmers to get the breeding knowledge and it can provide service whenever and wherever users want