33 results about "Significant error" patented technology

The present invention mainly relates to a current limiting circuit, also known as over-current protection circuit, and a power regulator using the same. The purpose for the circuit is to protect the power device and the loading circuit for the power regulator. The conventional current limiting circuit takes advantage of a resistor and a MOS to convert the detected over current into a voltage and then turn on a P-typed MOS to clamp the gate voltage of a power transistor so as to achieve the goal of current limiting. However, the process variation for the resistor and said MOS and their temperature variation lead to a significant error to the limiting current. The present invention, therefore, takes advantage of the current comparison to enhance the accuracy for the current limiting circuit.

In some embodiments, the recognition results produced by a speech processing system (which may include two or more recognition results, including a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential significant errors. In some embodiments, the recognition results may be evaluated using one or more sets of words and / or phrases, such as pairs of words / phrases that may include words / phrases that are acoustically similar to one another and / or that, when included in a result, would change a meaning of the result in a manner that would be significant for a domain. The recognition results may be evaluated using the set(s) of words / phrases to determine, when the top result includes a word / phrase from a set of words / phrases, whether any of the alternative recognition results includes any of the other, corresponding words / phrases from the set.

In some embodiments, the recognition results produced by a speech processing system (which may include two or more recognition results, including a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential significant errors. In some embodiments, the recognition results may be evaluated to determine whether a meaning of any of the alternative recognition results differs from a meaning of the top recognition result in a manner that is significant for a domain, such as the medical domain. In some embodiments, words and / or phrases that may be confused by an ASR system may be determined and associated in sets of words and / or phrases. Words and / or phrases that may be determined include those that change a meaning of a phrase or sentence when included in the phrase / sentence.

The invention is a method for detecting failures in an analyzer for conducting clinical assays. Potential errors that can result in assay failures in an analyzer are identified, as are their potential sources. The probability that an error source so identified will result in a clinically significant error is also determined. Available potential detection measures corresponding to the source of potential errors are identified with a combination of such measures selected and implemented based on their probability of detecting such errors within an acceptable limit with a concomitant low probability of the false detection of an assay failure. Each of the measures selected are functionally independent of others chosen to address the source of the error and are not subject to the same inherent means of failed detection. Applications of the method in a clinical analyzer are also presented.

In some embodiments, a recognition result produced by a speech processing system based on an analysis of a speech input is evaluated for indications of potential errors. In some embodiments, sets of words / phrases that may be acoustically similar or otherwise confusable, the misrecognition of which can be significant in the domain, may be used together with a language model to evaluate a recognition result to determine whether the recognition result includes such an indication. In some embodiments, a word / phrase of a set that appears in the result is iteratively replaced with each of the other words / phrases of the set. The result of the replacement may be evaluated using a language model to determine a likelihood of the newly-created string of words appearing in a language and / or domain. The likelihood may then be evaluated to determine whether the result of the replacement is sufficiently likely for an alert to be triggered.

Error concealment is used to hide the effects of errors detected within digital video information. A complex error concealment mode decision is disclosed to determine whether spatial error concealment (SEC) or temporal error concealment (TEC) should be used. The error concealment mode decision system uses different methods depending on whether the damaged frame is an intra-frame or an inter-frame. If the video frame is an intra-frame then a similarity metric is used to determine if the intra-frame represents a scene-change or not. If the video frame is an intra-frame, a complex multi-termed equation is used to determine whether SEC or TEC should be used. A novel spatial error concealment technique is disclosed for use when the error concealment mode decision determines that spatial error concealment should be used for reconstruction. The novel spatial error concealment technique divides a corrupt macroblock into four different regions, a corner region, a row adjacent to the corner region, a column adjacent to the corner region, and a remainder main region. Those regions are then reconstructed in that order and information from earlier reconstructed regions may be used in later reconstructed regions. Finally, a macroblock refreshment technique is disclosed for preventing error propagation from harming non-corrupt inter-blocks. Specifically, an inter-macroblock may be ‘refreshed’ using spatial error concealment if there has been significant error caused damage that may cause the inter-block to propagate the errors.

In some embodiments, the recognition results produced by a speech processing system (which may include a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential significant errors. In some embodiments, the recognition results may be evaluated to determine whether a meaning of any of the alternative recognition results differs from a meaning of the top recognition result in a manner that is significant for the domain. In some embodiments, one or more of the recognition results may be evaluated to determine whether the result(s) include one or more words or phrases that, when included in a result, would change a meaning of the result in a manner that would be significant for the domain.

Systems, methods, and software are provided for assessing manufacturing errors of a prosthetic socket to facilitate a clinical assessment of the socket. The embodiments disclosed herein may align and compare a manufactured socket shape to a desired socket shape to determine whether clinically significant errors are present in the manufactured socket. A mean radial error (MRE) may be calculated and compared to a set threshold. If the MRE falls below the threshold an interquartile range (IQR) may be calculated and compared to an IQR threshold. If the IQR falls below the IQR threshold, surface normal angle errors (SNAE) may be calculated and plotted to the surface model. If the SNAE plot does not include closed contour regions, the socket may proceed to patient fitting. If the MRE or IQR thresholds are exceeded, or if the SNAE plot indicates closed contour regions, the socket may be reshaped accordingly, prior to fitting.

In some embodiments, recognition results produced by a speech processing system (which may include two or more recognition results, including a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential errors. In some embodiments, the indications of potential errors may include discrepancies between recognition results that are meaningful for a domain, such as medically-meaningful discrepancies. The evaluation of the recognition results may be carried out using any suitable criteria, including one or more criteria that differ from criteria used by an ASR system in determining the top recognition result and the alternative recognition results from the speech input. In some embodiments, a recognition result may additionally or alternatively be processed to determine whether the recognition result includes a word or phrase that is unlikely to appear in a domain to which speech input relates.

The invention proposes a method for adjusting soft data in a multimedia data stream, a soft data demapper, and a multimedia data stream computing system. By determining the adjustment factor for adjusting the signal based on the signal with a low bit error rate, and using the adjustment factor to adjust the soft data, the soft data can be effectively simulated by a Gaussian model with obvious errors from the real situation. It amplifies the influence of signals with low bit error rate and relatively reduces the influence of signals with high bit error rate, so that the obvious errors caused by using the Gaussian model and the derived digital TV signal playback quality problems are solved together.

In the implementation of timing recovery in conventional communication systems, significant errors are generated from modulo operations under certain extreme conditions by taking input signals of a slicer as datum points. In order to prevent such errors, the input signal of a modulo processing circuit is taken as the datum point in place of the input signal of a slicer. This technique could also be applied to communication systems adopting the minimum mean-square error algorithm, the zero-forcing algorithm, or other relevant algorithms.

The invention is a method for detecting failures in an analyzer for conducting clinical assays. Potential errors that can result in assay failures in an analyzer are identified, as are their potential sources. The probability that an error source so identified will result in a clinically significant error is also determined. Available potential detection measures corresponding to the source of potential errors are identified with a combination of such measures selected and implemented based on their probability of detecting such errors within an acceptable limit with a concomitant low probability of the false detection of an assay failure. Each of the measures selected are functionally independent of others chosen to address the source of the error and are not subject to the same inherent means of failed detection. Applications of the method in a clinical analyzer are also presented.

The present invention differentiates between systematic and non-systematic conditions by observing a figure of merit over a series of many observation events. In a data storage recording environment, the particular figure of merit used is the number of data segments that must be re-written (due to errors) to a recording medium in order to assure that an entire data set is correctly written. A larger number of re-written segments is indicative of a significant error condition. After each data set is completely and correctly written, the number of re-written segments for the data set is reported as an “event.” A running history of the classified events (or the events themselves) is maintained. Then, at a predetermined time, the history is analyzed and a decision made as to whether any observed events meets predetermined criteria for a systematic condition.