Abstract:In this paper, we investigate the use of electroencephalograhic signals for the purpose of recognizing unspoken speech. The term unspoken speech refers to the process in which a subject imagines speaking a given word without moving any articulatory muscle or producing any audible sound. Early work by Wester (Wester, 2006) presented results which were initially interpreted to be related to brain activity patterns due to the imagination of pronouncing words. However, subsequent investigations lead to the hypothesis that the good recognition performance might instead have resulted from temporal correlated artifacts in the brainwaves since the words were presented in blocks. In order to further investigate this hypothesis, we run a study with 21 subjects, recording 16 EEG channels using a 128 cap montage. The vocabulary consists of 5 words, each of which is repeated 20 times during a recording session in order to train our HMM-based classifier. The words are presented in blockwise, sequential, and random order. We show that the block mode yields an average recognition rate of 45.50%, but it drops to chance level for all other modes. Our experiments suggest that temporal correlated artifacts were recognized instead of words in block recordings and back the above-mentioned hypothesis.
Freight exchanges are central to the logistics industry, as they reduce empty runs and meet spot demands. To improve their efficiency in terms of automation and enhance trust between the participants, we propose a decentralized freight exchange implemented using public blockchains. With our solution, we also address shortcomings of public blockchains, such as scalability and privacy. We present two artifacts: a general architecture for an electronic logistics marketplace (ELM) and a concrete implementation as the proof of concept for a freight exchange. The solution is implemented using two off-the-shelf public blockchains and a public distributed file system. Additionally, we investigate the implications for the general ELM model and show that an ELM based on a blockchain can be viewed as infrastructure rather than a market participant.
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