The performance of a wireless industrial process plant is affected by wired and wireless link's reliability. While recent related works focus mainly on wireless link's reliability, this paper focuses on wired link's reliability between the controller and the wireless highway addressable remote transducer (WirelessHART) node. In control application for a process plant with fast sampling, when the wired link delay is too short, message transmissions to the node could occur concurrently resulting in packet collision and packet dropout, thus leading to local contention. This can cause process plant with critical control application to be unstable. Therefore, this paper proposes an effective design of exponentially weighted moving average filter to mitigate the contention's effect and improve the process plant's performance. In order to make evident and validate the existence of local contention, an experiment is carried out using a WirelessHART device. From the experiment, the controller delay is 0.1475 s and the estimated packet dropout is 12.29%. Simulation results with the designed filter show a significant improvement in process performance in terms of percentage of overshoot compared with performance of the process plant using traditionally optimized PID controller.

In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study focuses on trust management (TM) in the IoV/VANETs/ITS (intelligent transport system). Trust has always been important in vehicular networks to ensure safety. A variety of techniques for TM and evaluation have been proposed over the years, yet few comprehensive studies that lay the foundation for the development of a “standard” for TM in IoV have been reported. The motivation behind this study is to examine all the TM models available for vehicular networks to bring together all the techniques from previous studies in this review. The study was carried out using a systematic method in which 31 papers out of 256 research publications were screened. An in-depth analysis of all the TM models was conducted and the strengths and weaknesses of each are highlighted. Considering that solutions based on AI are necessary to meet the requirements of a smart city, our second objective is to analyze the implications of incorporating an AI method based on “context awareness” in a vehicular network. It is evident from mobile ad hoc networks (MANETs) that there is potential for context awareness in ad hoc networks. The findings are expected to contribute significantly to the future formulation of IoVITS standards. In addition, gray areas and open questions for new research dimensions are highlighted.

In this paper, an FPT.AI-based text-to-speech (TTS) application is developed that converts Vietnamese text into spoken words. The application is developed based on Django for Python and in the form of an interactive web page which is connected to an FPT.AI server through its application programming interface (API). The application supports conversion of text to seven different Vietnamese speeches. Four out of seven voices can be used to convert up to 500 characters in a single transaction while the others support that of 400 characters. Based on the results obtained, the first conversion time takes up to 10 s to convert 400-character text into speech while the subsequent times, given same text, it takes under 1.8 s for the conversion. This is applicable to all voices.

Recent trends in voicebot application development have enabled utilization of both speech-to-text and text-to-speech (TTS) generation techniques. In order to generate a voice response to a given speech, one needs to use a TTS engine. The recently developed TTS engines are shifting towards end-to-end approaches utilizing models such as Tacotron, Tacotron-2, WaveNet, and WaveGlow. The reason is that it enables a TTS service provider to focus on developing training and validating datasets comprising of labelled texts and recorded speeches instead of designing an entirely new model that outperforms the others which is time-consuming and costly. In this context, this work introduces the first Vietnamese FPT Open Speech Data (FOSD)-Tacotron-2-based TTS model dataset. This dataset comprises of a configuration file in *.json format; training and validating text input files (in *.csv format); a 225,000-step checkpoint of the trained model; and several sample generated audios. The published dataset is extremely worth for serving as a model for benchmarking with other newly developed TTS models / engines. In addition, it opens an entirely new TTS research optimization problem to be addressed: How to effectively generate speech from text given: a black box TTS (trained) model and its training and validation input texts.