An advanced metering infrastructure (AMI) consists of a meter data management system, a concentrator, and a meter. Each component can communicate with all other components, through either wired or wireless connections. In an AMI local area networks, various protocols, such as Zigbee, WiFi, and power-line communication, have the potential to be used in AMI communication. Of these protocols, Zigbee offers self-constructing networks, low costs, and superior safety levels. Zigbee is adopted as the LAN communication protocol because of these advantages in this study. In a LAN, a concentrator may encounter many emergent situations, any of which might cause the concentrator to lose its network connection function. Consequently, additional time is required for network construction, which tends to reduce the reliability of smart grids with LANs of this type. To solve these issues, this paper proposes the fast join process (FJP) and enhanced FJP to shorten the time of Zigbee network construction. The strategy employed in this paper is to let every meter join a specified concentrator. The proposed FJP does not scan unnecessary channels. By simplifying association message exchange, we further propose a new method called the enhanced FJP. By using these methods, not only can message flow be managed easily, but the time of network construction can also be improved.

This paper describes a practical dictation system that is able to compensate for the lack of a large text database and reports on the results of eld tests in which the system was used to make medical rehabilitation diagnosis reports in a hospital. Our dictation system uses two recognition engines: continuous speech recognition and isolated word (including connected words) recognition engines. When a user makes a report, the two recognition engines can be selectively operated in a single window through voice commands or mouse click. The system operates on common personal computers under the Windows O.S. A eld test conducted in noisy therapeutic work rooms that compared the performance of speech input system comparing to conventional keyboard input system for the medical rehabilitation eld, demonstrated the e ectiveness of the dictation system.

This paper presents a new search strategy for large vocabulary continuous Mandarin speech recognition considering the special structure of the Chinese language. This strategy is composed of forward and backward passes, between which a high-quality syllable lattice is generated to bridge the syllable-level and word-level decoding processes. In the forward pass, considering the small number of syllables in the Chinese language, a frame-synchronous stack decoder is used to integrate the high-order syllable N-Gram language model, so as to generate a very accurate and compact syllable lattice. In the backward pass, considering the special monosyllabic wording structure in the Chinese language, the search space for the word-level decoding is expanded dynamically from the syllable lattice, and the best word sequence is extracted based on the knowledge provided by the word pronunciation lexicon and the word N-Gram language model. In the preliminary experiments, it was found that, with this strategy, the character error rate can be reduced by more than 20% as compared with a previous system using syllable-aligned lattice approach on a speaker-adaptive continuous speech recognition task.

This paper presents a new direction for Chinese language modeling based on a different concept of the lexicon. Because every Chinese character has its own meaning and there are no "blanks" in Chinese sentences serving as word boundaries, also because the wording structure in the Chinese language is extremely flexible, the "words" in Chinese are actually not well defined, and there does not exist a commonly accepted lexicon. This makes language modeling very sophisticated in the Chinese language, and the "out of vocabulary (OOV)" problem specially serious. A new concept for the lexicon is thus proposed. The elements of this lexicon can be words or any other "segment patterns". They should be extracted from the training corpus by statistical approaches with a goal to minimize the overall perplexity. The language models can then be developed based on this new lexicon. Very encouraging experimental results have been obtained.

This correspondence presents the first known results of complete recognition of continuous Mandarin speech for the Chinese language with very large vocabulary but very limited training data. Various acoustic and linguistic processing techniques were developed, and a prototype system of a continuous speech Mandarin dictation machine has been successfully implemented. The best recognition accuracy achieved is 92.2% for finally decoded Chinese characters.