Optimizing linear routing in the ToLHnet protocol to improve performance over long RS-485 buses

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Improvement of RS-485 Performance Over Long Distances Using the ToLHnet Protocol

This paper presents a technique to extend the transmission range over a single RS-485 cable, without requiring installation of expensive and cumbersome RS-485 repeaters, to virtually unlimited distances, while simultaneously improving transmission speeds between closer nodes. This was accomplished by leveraging the routing capabilities embedded into each node that implements the recently released and extremely lightweight ToLHnet protocol. With it, each network node can act as a sort of "smart repeater" only when there is need to, optimizing the overall network throughput. The key ideas underlying the routing strategies are here described, together with details of a prototype node and experimental results demonstrating transmission at distances well above the traditional limit

sistemi di interazione vocale per la domotica

Una delle questioni aperte nell’ambito dell’home automation è la realizzazione di interfacce uomo-macchina che siano non solo efficaci per il controllo di un sistema, ma anche facilmente accessibili. La voce è il mezzo naturale per comunicare richieste e comandi, quindi l’interfaccia vocale presenta notevoli vantaggi rispetto alle soluzioni touch-screen, interruttori ecc. Il lavoro di tesi proposto è finalizzato alla realizzazione di un sistema di interazione vocale per l’home automation, in grado non solo di riconoscere singoli comandi veicolati da segnali vocali, ma anche di personalizzare i servizi richiesti tramite il riconoscimento del parlatore e di interagire mediante il parlato sintetizzato. Per ciascuna tipologia di interazione vocale, verranno proposte soluzioni volte a superare i limiti dell’approccio classico in letteratura. In prima analisi, verrà presentato un sistema di riconoscimento vocale distribuito (DSR) per il controllo delle luci, che implementa ottimizzazioni ad-hoc per operare nell’ambiente in modo non invasivo e risolvere le problematiche di uno scenario reale. Nel sistema DSR sarà integrato un algoritmo di identificazione del parlatore per ottenere un sistema in grado di personalizzare i comandi sulla base dell’utente riconosciuto. Un sistema di identificazione vocale deve essere in grado di classificare l’utente con frasi della durata inferiore a 5 s. A tal fine verrà proposto un algoritmo basato su truncated Karhunen-Loève transform con performance, su brevi sequenze di speech (< 3.5 s), migliori della convenzionale tecnica basata su Mel-Cepstral coefficients. Verrà infine proposto un framework di sintesi vocale Hidden Markov Model/unit-selection basato su Modified Discrete Cosine Transform, che garantisce la perfetta ricostruibilità del segnale e supera i limiti imposti dalla tecnica Mel-cepstral. Gli algoritmi ed il sistema proposto saranno applicati a segnali acquisiti in condizioni realistiche, al fine di verificarne l’adeguatezza.One of the open questions in home automation is the realization of human-machine interfaces that are not only effective for the control of the available functions, but also easily accessible. The voice is the natural way to communicate requests and commands, in this way speech interface offers considerable advantages over solutions such as touch-screen, switches etc. The proposed thesis is aimed at studying and realizing a speech interaction system for home automation to be able not only to recognize individual commands conveyed by voice signals, but also to customize the services requested through a speaker recognizer and to interact by means of synthesized speech. For each speech interaction mechanism, solutions are suggested to overcome the traditional limitations of previous work. In the first analysis, it is offered a speech distributed recognition system (DSR), for the voice control of a lighting system, that implements strategies and ad-hoc optimizations and is able to solve the typical problems of a real scenario. The DSR system can also be integrated with a speaker identification algorithm in order to obtain a system able to customize the spoken commands on the user specific settings. In the home automation, a speaker identification system must be able to classify the user with sequences of speech frames of a duration less than 5 s. To this goal, an algorithm based on truncated Karhunen-Loève transform able to produce results, with short sequences of speech frames (< 3.5 s), better than those achieved with the Mel-Cepstral coefficients, is suggested. Moreover, this work presents a novel Hidden Markov Models/unit-selection speech synthesis framework based on Modified Discrete Cosine Transform, which guarantees the perfect reconstruction of the speech signal and overcomes the main lacks of Mel-cepstral technique. The algorithms and the proposed system will be applied to signals acquired under realistic conditions, in order to verify its adequacy