Abstract: Provided is an intermediate-frequency analog-to-digital conversion device, including: a gain attenuation module, a gain amplification module, a filter and an analog-to-digital conversion module. The gain attenuation module is configured to perform attenuation processing on a received intermediate-frequency signal. The gain amplification module is connected to the gain attenuation module, and configured to perform amplification processing on a signal that is output from the gain attenuation module. The filter is a variable filter, connected to the gain amplification module, and configured to perform filter processing on a signal that is amplified by a gain amplifier. The analog-to-digital conversion module is connected to the filter, and configured to convert a signal that is filtered by the filter into a digital signal.

Abstract: Provided is an intermediate-frequency analogue-to-digital conversion device, including: a gain attenuation module, a gain amplification module, a filter and an analogue-to-digital conversion module. The gain attenuation module is configured to perform attenuation processing on a received intermediate-frequency signal. The gain amplification module is connected to the gain attenuation module, and configured to perform amplification processing on a signal that is output from the gain attenuation module. The filter is a variable filter, connected to the gain amplification module, and configured to perform filter processing on a signal that is amplified by a gain amplifier. The analogue-to-digital conversion module is connected to the filter, and configured to convert a signal that is filtered by the filter into a digital signal.

Abstract: Techniques described enable answering a natural language question using machine learning-based methods to gather and analyze evidence from web searches. A received natural language question is analyzed to extract query units and to determine a question type, answer type, and/or lexical answer type using rules-based heuristics and/or machine learning trained classifiers. Query generation templates are employed to generate a plurality of ranked queries to be used to gather evidence to determine the answer to the natural language question. Candidate answers are extracted from the results based on the answer type and/or lexical answer type, and ranked using a ranker previously trained offline. Confidence levels are calculated for the candidate answers and top answer(s) may be provided to the user if the confidence levels of the top answer(s) surpass a threshold.