An extraction section extracts a speech signal having ambient noise superimposed thereon as a data segment having a predetermined duration. An autocorrelation function normalizing section determines normalized autocorrelation function vectors. A normalized autocorrelation function count section counts a given number of normalized autocorrelation function vectors. A noise vector region/speech vector region/undefined vector computation section classifies the normalized autocorrelation function vectors into any of a noise vector region, a speech vector region, or undefined vectors. When the latest normalized autocorrelation function vector acquired by a normalized autocorrelation function vector determination section pertains to the noise vector region, the speech signal is determined to be a noise segment. In contrast, when the latest vector does not pertain to the noise vector region, the input signal is determined to be a speech segment.

The present invention relates to a speech segment/noise segment determination apparatus to be used with a speech device, such as a portable cellular phone or a mobile phone, which determines whether a signal of an acquired segment includes only a noise or both a noise and a speech signal. More particularly, the noise segment/speech segment determination apparatus is constructed so as to be able to determine, with a high level of reliability, whether an acquired segment is a noise segment or a speech segment.
In recent years, an apparatus capable of taking speech as input information has been used under various circumstances. For this reason, ability for use of the apparatus under the influence of noise has become important. Portable cellular phones and mobile phones are examples of such an apparatus. Thanks to progress in IC technology, there has been adopted a noise suppressor which employs a fairly high-level digital signal processing technique by use of a digital signal processor (DSP).
Such a noise suppressor is used in conjunction with a device for determining whether or not a signal of a captured segment corresponds to a noise-only segment or to a speech signal segment. The quality of the device greatly affects the performance of the noise suppressor. A noise segment/speech segment determination device employed in a conventional noise suppressor will be described by reference to the accompanying drawings.
FIG. 21 is a block diagram showing a noise suppressor having a related-art noise segment/speech segment determination device. A noise segment/speech segment determination device 1100 enclosed by dotted lines in FIG. 21 comprises an analog-to-digital conversion section 1101; an extraction section 1102; and a noise segment/speech segment determination section 1103. Further, the noise segment/speech segment determination device 1100 has an input terminal 1 for receiving an analog speech signal including noise, a speech segment determination output terminal 2, and a noise segment determination output terminal 3. The noise suppressor is constructed such that a signal output from the extraction section 1102, a signal output from the speech segment determination output terminal 2, and a signal output from the noise segment determination output terminal 3 are delivered to a noise suppression device 1104.
The first to third related-art noise segment/speech segment determination device 1100 used in the noise suppression device 1104 is now described by reference to FIG. 21.
An analog speech signal—which has been converted into an electric signal by means of an unillustrated microphone and includes ambient noise—is input to the noise segment/speech segment determination device 1100 via the input terminal 1. The analog speech signal is converted into a digital signal by means of the analog-to-digital conversion section 1101. The digital signal is taken into a frame of given interval; e.g., 10 [ms]. The digital signal taken into the frame is input simultaneously to the noise segment/speech segment determination section 1103 and to the noise suppression device 1104.
The noise segment/speech segment determination section 1103 determines whether the input signal corresponds to a noise-only signal segment or a noise-including speech signal segment, and outputs a result of determination to the noise suppression device 1104. On the basis of a determination result signal output from the noise segment/speech segment determination section 1103, the noise suppression device 1104 processes a signal delivered from the extraction section 1102, thereby outputting a noise-suppressed speech signal.
Related-art technologies pertaining to a determination operation to be performed by the noise segment/speech segment determination section 1103 will now be described. A first example of related-art technology will be described. In relation to a speech signal which is input to the noise segment/speech segment determination section 1103 and includes ambient noise, a signal segment which includes no speech signal and only noise should be lower in level than a signal segment including a speech signal. Accordingly, mean power of each frame of an input signal is compared with a predetermined threshold value. If the power exceeds the threshold value, the frame can be determined to be a noise-including speech signal segment. In contrast, if the power does not exceed the threshold value, the frame can be determined to be a noise segment.
A second example of related-art technology will next be described. A second example of related-art technology is a method of changing the threshold value to be used for determination, so as to follow changes in ambient noise. For instance, one frame takes an interval of 10 [ms], and mean power of the frame is measured. For instance, mean power is measured every five seconds, and the minimum mean power is taken as a threshold value for determining a noise segment/speech segment over the next five seconds. In this case, a threshold value for determination can be changed every five seconds. The translated versions of Japanese Patent Publication Nos. H3-500347 and H10-513030 describe a method of changing a threshold value for determining a noise segment and speech segment so as to follow changes in ambient noise.
Next will be described a third example of related-art technology; that is, a known technique of using the "number of short-time zero crossings" described in Japanese Patent Publication No. H8-294197. As shown in FIG. 21, a speech signal including ambient noise is converted into a digital signal by means of the analog-to-digital conversion section 1101. The number of times consecutive sample values corresponding to a digital signal output change from positive to negative or vice versa is accumulated for a certain period of time. If sample values include speech, an accumulated value becomes higher than that obtained by counting noise-only sample values. The accumulated value is compared with a predetermined threshold value. If the accumulated value is greater than the threshold value, a corresponding segment can be determined to be a speech signal segment. If the accumulated value is lower than the threshold value, a corresponding segment can be determined to be a noise segment. The first certain period of time at the beginning of communication is deemed as a period during which a user has not yet uttered speech and only ambient noise is present. The accumulated value of the period is determined to be an accumulated value of the noise segment. Only when an accumulated value for a certain period of time is greater than a value which is five times the accumulated value of the first segment, the period is taken as a speech period.
A method described in Japanese Patent Publication No. Sho 58-143394 will now be described as a fourth related-art example. The first and second related-art examples utilize the phenomenon that a mean level of a speech segment is greater than that of a noise segment. If ambient noise becomes great to the same level as that of the speech signal, distinguishing between a speech segment and a noise segment becomes difficult. In contrast, the fourth method enables rendering of a distinction between a noise segment and a speech segment regardless of the magnitude of ambient noise. The outline of the method will be described hereinbelow.
First, speech comprises voiced sounds and voiceless sounds. The voiced sounds correspond to ordinary vowel and consonant sounds, and the voiceless sounds correspond to fricative sounds and plosives. The voiced sounds are considered to take, as a sound source, an iterative pulse train of given cycle called a pitch and the voiceless sounds are considered to take, as a sound source, a random pulse train. Further, the pulse trains are considered to be uttered from the mouth as speech via the vocal tract. The method determines an input signal of a certain segment as a voiced sound segment, a voiceless sound segment, or a noise segment regardless of a mean power level of the segment. The method will further be described by reference to FIG. 22.
As shown in FIG. 22, a related-art fourth noise segment/speech segment termination device comprises the analog-to-digital conversion section 1101; the extraction section 1102; an auto-correlation function computation section 1201; a linear prediction section 1202; a normalized residual correlation function computation section 1203; a normalized power rating computation section 1204; and a noise segment/speech segment determination section 1205. The analog-to-digital conversion section 1101 and the extraction section 1102 are the same as those described in connection with FIG. 21. Further, the noise segment/speech segment determination section 1205 has the speech segment determination output terminal 2 and the noise segment determination output terminal 3, in the same manner as described in connection with FIG. 21. Hence, repeated explanations thereof are omitted.
A speech signal input including ambient noise is converted into a digital signal by means of the analog-to-digital conversion section 1101. The extraction section 1102 takes the thus-converted digital into a frame having an interval of, e.g., 10 [ms]. Given that a sampling frequency is 8 [kHz], 80 samples are taken. The signal is input to the auto correlation function computation section 1201, and there is obtained an autocorrelation function up to an analysis order of "p"; that is, R(0), R(1), . . . R(p). In the case of an ordinary speech signal, the analysis order "p" assumes a value of about 10. Provided that a sample value of an input signal is represented as s (n), formula (1) holds, as follows.                               R                                (            j            )                          =                              (                          1              /              80                        )                                          [                                          ∑                                  n                  =                  0                                                  n                  =                  79                                                                                s                                                        (                    n                    )                                                  *                                  s                                                        (                                          n                      -                      j                                        )                                                                        ]                                              (        1        )
The autocorrelation function R(0), R(1), . . . R(p) is input to the linear prediction section 1201. The linear prediction section 1202 linearly predicts an input signal in the following manner, through use of values of the autocorrelation function. Since an acquired speech signal has a degree of redundancy, a present sample can be predicted from a sample taken in the past. However, perfect prediction of a present sample is impossible, and hence an error remains. A predicted value "S′(n)" is expressed by the following formula (2).                                           s            ′                                          (            n            )                          =                  -                                    ∑                              j                =                1                                            j                =                p                                                                                a                j                                                          s                                                  (                                      n                    -                    j                                    )                                                                                (        2        )
Data up to a sample "p" in the past are predicted. A prediction error e(n) is expressed by the following formula (3).                               e                                (            n            )                          =                                            s                                            (                n                )                                      -                                          s                ′                                                          (                n                )                                              =                                    ∑                              j                =                0                                            j                =                p                                                                                a                j                                                          s                                                  (                                      n                    -                    j                                    )                                                                                (        3        )                            where, a0=1
Here, a1, a2, . . . ap are selected such that a root mean square (RMS) of formula (3) is minimized.
To this end, values of a1, a2, . . . ap sought by solution of the following formula (4) are employed.                                           [                                                                                R                                                              (                      0                      )                                                                                R                                                              (                      1                      )                                                                                R                                                              (                      2                      )                                                                                ⋯                                                                      R                                                              (                                              p                        -                        1                                            )                                                                                R                                                              (                      1                      )                                                                                R                                                              (                      0                      )                                                                                R                                                              (                      1                      )                                                                                ⋯                                                                      R                                                              (                                              p                        -                        2                                            )                                                                                R                                                              (                      2                      )                                                                                R                                                              (                      1                      )                                                                                R                                                              (                      0                      )                                                                                ⋯                                                                      R                                                              (                                              p                        -                        3                                            )                                                                                R                                                              (                      3                      )                                                                                R                                                              (                      2                      )                                                                                R                                                              (                      1                      )                                                                                ⋯                                                                      R                                                              (                                              p                        -                        4                                            )                                                                                ⋮                                                  ⋮                                                  ⋮                                                  ⋰                                                  ⋮                                                                                R                                                              (                                              p                        -                        1                                            )                                                                                R                                                              (                                              p                        -                        2                                            )                                                                                R                                                              (                                              p                        -                        3                                            )                                                                                ⋯                                                                      R                                                              (                      0                      )                                                                                ]                    *                      [                                                                                a                    1                                                                                a                    2                                                                                a                    3                                                                                a                    4                                                                                ⋮                                                                                a                    p                                                                        ]                          =                  [                                                                      R                                                        (                    1                    )                                                                                R                                                        (                    2                    )                                                                                R                                                        (                    3                    )                                                                                R                                                        (                    4                    )                                                                                ⋮                                                                                R                                                        (                    p                    )                                                                                ]                                    (        4        )
A partial autocorrelation function kj(j=1, 2, . . . p) and a normalized residual signal are obtained during the course of seeking linear prediction coefficients, a1, a2, . . . ap. The partial autocorrelation function kj is expressed by the following formulas (5) and (6).k1=R(1)/R(0)  (5)k2={(R(2)/R(0))−(R(1)/R(0))2}/{1−(R(1)/R(0))2}  (6)
Partial autocorrelation functions k3 and beyond are omitted and can be expressed through use of R(0), R(1), . . . R(p). As can be seen from formulas (5) and (6), the value of kj is normalized by R(0) representing mean power and is irrelevant to the power of an input signal. A normalized residual signal is expressed by formula (7).                                           e            r                                          (            n            )                          =                              ∑                          j              =              0                                      j              =              p                                                                    a              j                                                                  s                                                  (                                      n                    -                    j                                    )                                            /                                                (                                      R                                                              (                      0                      )                                                        )                                                  1                  /                  2                                                                                (        7        )                            where, a0=1
Here, ai (i=1, 2, . . . p) is a linear prediction coefficient and is to be computed by the linear prediction section 1202. To be more precise, a partial autocorrelation function kj (j=1, 2, . . . p) is sought during the course of seeking the linear prediction coefficient ai (i=1, 2, . . . p). The linear prediction coefficient is input to the normalized residual coefficient function computation section 1203. The partial autocorrelation function kj (j=1, 2, . . . p) is input to the normalized power rating computation section 1204, and k1 is input to the noise segment/speech segment determination section 1205. The normalized power rating computation section 1204 computes a normalized power rating according to formula (8), and the thus-computed normalized power rating is input to the noise segment/speech segment determination section 1205.                               E          N                =                              ∑                          j              =              1                                      j              =              p                                                      (                          1              -                              k                j                2                                      )                                              (        8        )                            where, p is an analysis order
The normalized residual correction function computation section 1203 computes an autocorrelation function of a normalized residual signal expressed by the following formula (9).                               Φ                                (            j            )                          =                              (                          1              /              80                        )                                                        ∑                              n                =                0                                            n                =                79                                                                [                                                                    e                    r                                                                          (                    n                    )                                                  *                                                      e                    r                                                                          (                                          n                      -                      j                                        )                                                              ]                                                          (        9        )
Next, the maximum value φ of Φ (j) computed by formula (9) is selected, and the thus-selected maximum value φ is input to the noise segment/speech segment determination section 1205. The maximum value φ of Φ (j) is expressed by the following formula (10).                     ϕ        =                              Max                                      {                              Φ                                                  (                  j                  )                                            }                                =                      Max                                      {                                                (                                      1                    /                    80                                    )                                                                  [                                                            ∑                                              n                        =                        0                                                                    n                        =                        79                                                                                e                          r                                                                                (                          n                          )                                                                    *                                                                        e                          r                                                                                (                                                      n                            -                            j                                                    )                                                                                ]                                            }                                                          (        10        )
The noise segment/speech segment determination section 1205 determines whether or not a signal of an acquired segment is a noise segment or a speech segment by using the following computed three parameters as described above, regardless of a mean power level of the segment.k1=R(1)/R(0)  (5)                              E          N                =                              ∑                          j              =              1                                      j              =              p                                                      (                          1              -                              k                j                2                                      )                                              (        8        )                            where, p is an analysis order                     ϕ        =                              Max                                      {                              Φ                                                  (                  j                  )                                            }                                =                      Max                                      {                                                (                                      1                    /                    80                                    )                                                                  [                                                            ∑                                              n                        =                        0                                                                    n                        =                        79                                                                                e                          r                                                                                (                          n                          )                                                                    *                                                                        e                          r                                                                                (                                                      n                            -                            j                                                    )                                                                                ]                                            }                                                          (        10        )
If necessary, for the significance of formulas (5), (8), and (10), please refer to "Speech Sound" by Kazuo NAKATA (Corona Publishing Co. Ltd.), 3.2.5 and 3.2.6, Chapter 3, 1977, or "Computer Speech Processing" by AGUI and NAKAJIMA (Sanpo Publication Inc.), Chapter 2, 1980.
FIG. 23 shows details of a decision. As shown in FIG. 23, the horizontal axis represents EN, and the vertical axis represents k1. Regions which can be determined by combination of these values EN and k1 are determined as a voiced sound, a voiceless sound, or noise. Regions which cannot be determined through use of only EN and k1 are determined as a voiced sound/voiceless sound or a voiced sound/noise. By means of the value of φ, when φ assumes a value greater than 0.3, a corresponding region is taken as a voiced sound, and when φ assumes a value lower than 0.3, a corresponding region is taken as a voiceless sound or noise.
The noise segment/speech segment determination devices set forth suffer the following problems.
(1) The noise segment/speech segment determination devices relating to the first and second related-art examples cannot determine whether a signal of an acquired segment is a noise segment or a speech segment, when noise becomes high to the same level as that of a speech signal.
(2) The noise segment/speech segment determination device relating to the third related-art example enables rendering of a determination as to whether a signal of acquired segment is a noise segment or a speech segment, regardless of a noise level. However, in practice, the determination device is influenced by a signal-to-noise ratio of a speech signal, and hence acquisition of a determination of sufficient accuracy is difficult.
(3) The noise segment/speech segment determination device relating to the fourth related-art example enables rendering of a determination as to whether a signal of an acquired segment is a noise segment or a speech segment, regardless of a noise level. However, in practice, the reliability of determination is insufficient for reasons of variations, and hence an accurate determination as to whether or not a signal of an acquired segment is a noise segment or a speech segment cannot be made.

Noise segment/speech segment determination apparatus

Matsushita Electric Industrial Co., Ltd.


A method for correcting non-uniformities of one or more display panels of a HMD (e.g., a VR headset or an AR headset) is disclosed, where the method is based on a luminance level of content being displayed. The method includes obtaining the calibration data for correcting non-uniformity of a display panel of the HMD at various brightness levels and storing the data. In response to receiving a request for providing content to be presented on the HMD, the host applies the calibration data based on a luminance level of content being rendered to correct for any non-uniformities of the one or more display panels while rendering the content for display.

The present disclosure generally relates to display panels in head-mounted displays (HMDs), and specifically to correcting non-uniformity errors in such display panels.
HMDs may be used to present augmented and/or virtual information to a user. For example, a virtual reality (VR) headset can be used to simulate virtual environments. During a manufacturing process of the HMDs, there may be some non-uniformity that exists across any individual display panel as well as across panels of the HMDs. HMDs may use two display panels to present information to the user with each display panel presenting information to each eye. In order to create a stereoscopic image, images displayed on the left and right panels are fused together (overlaid) to create a perception of depth. It is desirable that the corresponding points between the two display panels have the same color and brightness. If non-uniformity exists between the brightness (or luminance) of corresponding pairs of pixels or between color matching of corresponding pairs of sub-pixels of the left and right display panels, the user's eyes may not be able to fuse the images together properly, causing eye strain on the user. Any such non-uniformity is corrected by applying non-uniformity calibration data while a host system renders content for display on the HMDs.
The non-uniformity in display panels becomes more or less noticeable as its luminance level varies, such that panels displaying content at lower luminance levels have more noticeable non-uniformity relative to when displaying content at higher luminance levels. Conventionally, non-uniformity calibration is applied at a reference luminance level for content irrespective of a luminance level of the content being displayed on the HMD, reducing user experience.

Luminance-based non-uniformity correction for display panels in head-mounted displays


The vocabulary size of a speech recognizer for a large task is reduced by providing a recognizer only for the most common vocabulary items. Uncommon items are catered for by providing aliases from the common items. This allows accuracy to remain high while also allowing uncommon items to be recognized when necessary.

1. Technical Field
The present invention is concerned with speech recognition, particularly although not exclusively for use in automated voice-interactive services for use over a telephone network.
2. Related Art
A typical application is an enquiry service where a user is asked a number of questions in order to elicit replies which, after recognition by a speech recogniser, permit access to one or more desired entries in an information bank. An example of this is a directory enquiry system in which a user, requiring the telephone number of a customer, is asked to give the town name and road name of the subscriber's address, and the customer's surname.
The problem with a system which is required to operate for a large number of customer entries, the whole of the UK which has about 500 thousand different surnames, for example, is that once the surname vocabulary becomes very large the recognition accuracy falls considerably. Additionally the amount of memory and processing power required to perform such a task in real time becomes prohibitive.
One way of overcoming this problem is described in our co-pending patent application EP 95934749.3 in which,                (i) the user speaks the name of a town;        (ii) a speech recogniser, by reference to stored town data identifies several towns as having the closest matches to the spoken town name, and produces a "score" or probability indicating the closeness of the match;        (iii) a list is compiled of all road names occurring in the identified towns;        (iv) the user speaks the name of a road;        (v) the speech recogniser identifies several road names, of the ones in the list, having the closest matches to the spoken road name, again with scores;        (vi) the road scores are each weighted accordingly to the score obtained for the town the road is located in, and the most likely "road" result considered to be the one with the best weighted score.
A disadvantage of such a system is that if the correct town is not identified as being one of the closest matches then the enquiry is bound to result in failure.

British Telecommunication public limited company


A scroll and screen centrifuge especially suited for processing abrasive materials in which the surfaces of the screw flights on the scroll members contain recessed abrasion resistant shields. The abrasion resistant shields have an upper exposed surface flush with the front surfaces of the screw flights. The scroll members also have feed openings designed for material flow which further reduces the abrasion of the scroll member.

1. Field of the Invention
The present invention relates generally to the area of fluid handling and solid/liquid separation and, more particularly, to a scroll and screen centrifuge having abrasion resistant scroll inserts and feed openings designed to reduce abrasion of the scroll.
2. Description of Related Art
It is well known to use a scroll and screen centrifuge to separate a crystalline, a granular or a fibrous product from a liquid in which the crystalline, granular or fibrous product is carried. Scroll and screen centrifuges are typically used with a continuous process in which a combined liquid and solid material is continuously fed into and continuously discharged from the centrifuge. The continuous process is distinguished from a batch process in which a batch of the liquid and solid material is fed into the centrifuge; and the liquid and solid materials are separated. The centrifuge is stopped, and the separated materials are removed before another batch is loaded into the centrifuge. Both the continuous and batch separation processes are also considered to dry the solid material. The separation process is used in a wide range of applications including the production of soap powders, coal, gypsum, plastic granules, potash, salt, etc.
In a typical scroll and screen centrifuge, a frustoconical scroll and cage assembly is mounted for rotation within a housing. A frustoconical scroll member having a tubular side wall with outwardly projecting helical screw flights is mounted for rotation within an independently rotating frustoconical screen cage, and a screen basket is mounted within the screen cage. The scroll member and cage member rotate at a slightly different angular velocities so that the helical screw flights on the scroll member pass close to and over an inner directed surface of the screen. The helical length of the screw flights varies in a range of from a fraction of a revolution to several revolutions around the scroll member. A liquid containing solids to be separated is fed into a closed rearward portion of the frustoconical scroll and cage assembly. The rotation of the screen cage produces a centrifugal force causing the liquid to pass through openings in the screen basket thereby separating the liquid from the solids. The rotational velocity difference between the scroll member and cage member causes the screw flights to push the solid material across the screen from the smaller, closed, rear end of the scroll and cage assembly to its larger, open, forward end from which the solid material is discharged. The processing time or retention time of the material in the centrifuge is controlled by the pitch of the helical screw flights on the scroll member and the difference in angular velocities between the scroll and cage members. Retention time is also influenced by the design of the feed openings in the scroll. Gravity pulls the solid particles through the plenum to another processing station, a conveyor or a collection unit.
The most vigorous interaction between the combined liquid and solid material occurs at the smaller, closed end of the scroll and cage assembly. At that point, the combined liquid and solid material passes from the interior of the scroll member through feed openings in the tubular side wall to a volume bounded by the end wall of the scroll member, the outer surface of the tubular side wall of the scroll member, the helical screw flights extending from the scroll member and the screen. As the combined liquid and solid material passes through the feed openings, it contacts the screw flights over their full width, which extends from the outer surface of the tubular side wall of the scroll member to the screen; and the combined liquid and solid material contact the screw flights over a significant portion of their length. In some applications, for example, the separation of coal from a coal and water slurry, the physical characteristics of the coal and slurry water, are such that vigorous contact of the coal slurry with the screw flights will result in the structural material of the screw flights being abraded away.
The rotation of the scroll and cage assembly is effective to quickly separate the liquid from the solid material after a translation of the combined liquid and solid material from the smaller, closed end of the scroll and cage assembly toward the larger, open discharge end. In a coal slurry, the greatest wear occurs at the feed area where the slurry impacts on the rotating scroll. In addition to the impact, the acceleration of the solids from a static state to a rotating state produces high abrasion. Motion of the solid material toward the discharge end is controlled by the difference in angular velocities between the scroll member and the screen cage. The abrading action is lower at the discharge end because the relative velocities of the rotating components and the coal is less vigorous.
The extent and severity of the abrading action will depend on the physical characteristics, that is, the abrasion characteristics of the combined liquid and solid material, the design of the scroll and cage assembly and the operating parameters of the centrifuge. To reduce the wear and tear by the solid material on the screw flights of the scroll member, abrasion resistant elements comprising abrasion resistant tiles are bonded on top of the forward directed surfaces of the screw flights proximate the rear end of the scroll member. However, the bonding agent in the joints between the tiles and in the joints between the underside of the tiles and the surfaces of the screw flights projects above the surface of the screw flight and is subject to the same abrading action by the solid material. Consequently, the bonding agent experiences the same wear and tear from the abrading action of the solid material and is abraded away. Therefore, scroll members with such abrasion resistant elements have the disadvantage of that after a short period of use, for example, several months, the combined liquid and solid material abrades away the bonding agent in the joints between and below the tiles; and the tiles become separated from the top surfaces of the screw flights. Therefore, the useful life of the scroll member is shortened, and the centrifuge must be rebuilt with a replacement scroll member at a substantial expense.
The designs of prior scroll members have feed openings with lips adjacent the end wall that trap solids inside the scroll member. The trapped solids move in circular paths inside the scroll member and undercut the lip and abrade the adjacent end wall. Therefore, such designs have the disadvantage of having unnecessary abrasion and wear and tear on the scroll and cage assembly, thereby reducing its useful life.

Centrifuge scroll with abrasion resistant inserts


A power converter module includes a baseplate, a substrate on the baseplate, one or more silicon carbide switching components on the substrate, and a housing over the baseplate, the substrate, and the one or more silicon carbide switching components. The housing has a footprint less than 25 cm2. Including a baseplate in a power converter module with a footprint less than 25 cm2 runs counter to accepted design principles for silicon and silicon carbide-based power converter modules, but may improve performance of the power converter module and/or decrease the cost of the power converter module.

Power converter modules (which, as referred to herein may also include power inverter modules) are standalone devices that may perform a variety of functions within a power converter system. For example, power converter modules may include boost converters, buck converters, half-bridge converters, and full-bridge converters. Conventional power converter modules generally include power converter circuitry utilizing silicon switching components. While effective in many applications, using power converter circuitry with silicon switching components generally limits the switching frequency at which the power converter circuitry can operate. The lower the switching frequency of the components in the power converter circuitry, the larger the filtering components such as inductors and capacitors utilized in a power converter system need to be. Accordingly, filtering components used along with power converter circuitry using silicon switching components must be quite large, thereby driving up the cost of the power converter system. Further, at high switching frequency, silicon switching components are often associated with relatively low efficiency and low power density.
Conventional design principles for silicon-based power converter modules focus on ways to reduce the size and/or cost of or remove additional components of the module. This is because the cost associated with silicon switching components in a conventional power converter module is negligible when compared to additional components of the module such as thermal management components (e.g., baseplates, substrates, etc.). In many cases, following this design approach necessitates increasing the size of the silicon switching components used in the module in order to compensate for the alteration of the additional components. For example, conventional small footprint power converter modules (e.g., those with a footprint of approximately 31 mm×66 mm×16 mm) do not include a baseplate to save cost. These small footprint power converter modules have thus become well known in the industry as "baseplate-less" power converter modules. Additionally, these conventional small footprint power converter modules may opt for cheaper materials in the construction of a power substrate therein, which may have reduced thermal performance when compared to more expensive materials. To compensate for any loss in thermal performance due to the alteration of additional components in a conventional power converter module described above, the size of the silicon switching components in the module may be increased. By increasing the size of the silicon switching components, the heat generated by these components is spread out over a larger area, such that the components do not require additional thermal management. Since the additional components in conventional power converter modules are often significantly more expensive than the silicon added by increasing the size of the switching components, such a trade-off results in an overall reduction in cost of the module.


A method for estimating a property of an earth formation penetrated by a borehole, the method comprising generating a first transient electromagnetic fields with a transmitter on a carrier located outside of the borehole and receiving associated signals, calculating a bucking coefficient using the measured signals, conveying the carrier through a borehole, generating a second transient electromagnetic field and receiving associated signals, calculating, a bucked signal using the bucking coefficient and the downhole signals, calculating synthetic formation signals, calculating synthetic formations signals by applying a Duhamel's integral to the measured signals and the synthetic formation signals, calculating a synthetic bucked signal, and performing an inversion of a formation model to estimate a property of the formation such that an inverted synthetic bucked signal and an inverted bucked signal coincide within a predetermined range.

The ultimate objective of any geophysical experiment is to find properties of a formation using the measured data. To accomplish the objective a processing technique, called inversion, is applied. Inversion requires a mathematical model, which is used to produce synthetic data. The model has to include all of the necessary parameters that affect the measurements because an accurate model is needed for a successful inversion. Because the amount of recordable data is limited, no geophysical inverse problem is uniquely solvable and inferences of formation properties are statistical.
The possibility to reduce the range of uncertainty in the inverted models is driven by the sensitivity of the data to parameters of interest and a level of noise in the data. The noise can be either random or systematic. Because of this, mechanisms for improving sensitivity to the parameters of a formation have been developed. For example, a technique referred to as bucking was developed to improve sensitivity to the parameters of the formation while eliminating systematic noise caused by a primary field and/or the conductive tools used to make the measurements. If bucking does not account for electromagnetic interference between a conductive tool body and a formation, this interference will create or be a source of additional systematic noise. For this reason, it is beneficial to develop techniques for reducing inconsistency between measured and synthetic model responses and thus reduce uncertainty in estimated parameters of a formation.

Method and apparatus for estimating formation properties using transient electromagnetic measurements while drilling


A segmented scleral expansion band adapted for implantation within or fastening to a segment of the sclera of an eye lying outside of and adjacent to the ciliary body of the eye, is formed from a number of arcuate segments, curved to match the curvature of the globe of the eye, and joined together at each end to form a complete scleral expansion band. The band is implanted in the sclera of the eye by forming circumferential tunnels, inserting the band segments through the tunnels, and joining the ends of the segments to form a complete scleral expansion band. The scleral expansion band is useful in treating presbyopia and other ocular disorders.

1. Field of the Invention
This invention relates to methods of treating presbyopia, hyperopia, primary open angle glaucoma and ocular hypertension and more particularly to methods of treating these diseases by increasing the effective working distance of the ciliary muscle. The invention also relates to increasing the amplitude of accommodation of the eye by increasing the effective working range of the ciliary muscle and to prosthetic devices for achieving such increased amplitude of accommodation.
2. Brief Description of the Prior Art
In order for the human eye to have clear vision of objects at different distances, the effective focal length of the eye must be adjusted to keep the image of the object focused as sharply as possible on the retina. This change in effective focal length is known as accommodation and is accomplished in the eye by varying the shape of the crystalline lens. Generally, in the unaccommodated emmetropic eye the curvature of the lens is such that distant objects are sharply imaged on the retina. In the unaccommodated eye, near objects are not focused sharply on the retina because their images lie behind the retinal surface. In order to visualize a near object clearly, the curvature of the crystalline lens is increased, thereby increasing its refractive power and causing the image of the near object to fall on the retina.
The change in shape of the crystalline lens is accomplished by the action of certain muscles and structures within the eyeball or globe of the eye. The lens is located in the forward part of the eye, immediately behind the pupil. It has the shape of a classical biconvex optical lens, i.e., it has a generally circular cross section having two convex refracting surfaces, and is located generally on the optical axis of the eye, i.e., a straight line drawn from the center of the cornea to the macula in the retina at the posterior portion of the globe. In the unaccommodated human eye the curvature of the posterior surface of the lens, i.e., the surface adjacent to the vitreous body, is somewhat greater than that of the anterior surface. The lens is closely surrounded by a membranous capsule that serves as an intermediate structure in the support and actuation of the lens. The lens and its capsule are suspended on the optical axis behind the pupil by a circular assembly of very many radially directed elastic fibers, the zonules, which are attached at their inner ends to the lens capsule and at their outer ends to the ciliary body, a muscular ring of tissue, located just within the outer supporting structure of the eye, the sclera. The ciliary muscle is relaxed in the unaccommodated eye and therefore assumes its largest diameter. According to the classical theory of accommodation, originating with Helmholtz, the relatively large diameter of the ciliary muscle in this condition causes a tension on the zonules which in turn pulls radially outward on the lens capsule, causing the equatorial diameter of the lens to increase slightly and decreasing the anterior-posterior dimension of the lens at the optical axis. Thus, the tension on the lens capsule causes the lens to assume a flattened state wherein the curvature of the anterior surface, and to some extent the posterior surface, is less than it would be in the absence of the tension. In this state the refractive power of the lens is relatively low and the eye is focused for clear vision of distant objects.
When the eye is intended to be focused on a near object, the ciliary muscles contract. According to the classical theory, this contraction causes the ciliary muscle to move forward and inward, thereby relaxing the outward pull of the zonules on the equator of the lens capsule. This reduced zonular tension allows the elastic capsule of the lens to contract, causing an increase in the antero-posterior diameter of the lens (i.e., the lens becomes more spherical) resulting in an increase in the optical power of the lens. Because of topographical differences in the thickness of the lens capsule, the central anterior radius of curvature decreases more than the central posterior radius of curvature. This is the accommodated condition of the eye wherein the image of near objects falls sharply on the retina.
Presbyopia is the universal decrease in the amplitude of accommodation that is typically observed in individuals over 40 years of age. In the person having normal vision, i.e., having emmetropic eyes, the ability to focus on near objects is gradually lost, and the individual comes to need glasses for tasks requiring near vision, such as reading.
According to the conventional view the amplitude of accommodation of the aging eye is decreased because of the loss of elasticity of the lens capsule and/or sclerosis of the lens with age. Consequently, even though the radial tension on the zonules is relaxed by contraction of the ciliary muscles, the lens does not assume a greater curvature. According to the conventional view, it is not possible by any treatment to restore the accommodative power to the presbyopic eye. The loss of elasticity of the lens and capsule is seen as irreversible, and the only solution to the problems presented by presbyopia is to use corrective lenses for close work, or bifocal lenses, if corrective lenses are also required for distant vision.
Certain rings and/or segments have been used in ocular surgery for various purposes. Rings and/or segments of flexible and/or elastic material, attached or prepared in situ by fastening the ends of strips of the material around the posterior portion of the globe, posterior to the pars plana (over the underlying retina), have been used to compress the sclera in certain posterior regions. Supporting rings of metal, adapted to fit the contour of the sclera have been used as temporary supporting structures during surgery on the globe. However, none of these known devices has been used for surgical treatment of presbyopia, and none has been adapted to the special needs of prosthetic devices used in treating for presbyopia.
A scleral band adapted to be fastened to the sclera of the eye in the region of the ciliary body in order to expand the sclera in that region and thereby increase the working distance of the ciliary muscle is described in applicant""s U.S. Pat. No. 5,354,331, the entire disclosure of which is incorporated herein by reference. The scleral band of that patent is manufactured as a single unitary device that is placed on the surface of the sclera and fastened thereto, e.g., by suturing. Although the band is effective, alternative designs are possible that permit greater flexibility in installing the scleral band.
Accordingly, a need has continued to exist for a scleral band for treating presbyopia and other eye disorders that allows the surgeon the opportunity of other methods of installing or implanting the band.
An alternative design for a scleral band that can be implanted to treat presbyopia and other eye disorders is found in the band of this invention wherein a scleral band comprises a plurality of segments that can be assembled on the eye to form a complete scleral band. The invention also comprises a method for installing such a segmented band comprising the steps of forming tunnels in the substance of the sclera in the region overlying the ciliary body, inserting segments of the segmented band into the tunnels, and uniting the ends of the segments to form a unitary scleral band.
Accordingly, it is an object of the invention to provide a treatment for presbyopia.
A further object is to provide a treatment for presbyopia by increasing the effective working distance of the ciliary muscle in the presbyopic eye.
A further object is to provide a treatment for presbyopia by increasing the radial distance between the equator of the crystalline lens and the ciliary body.
A further object is to provide a treatment for presbyopia by implanting in the sclera in the region overlying the ciliary body a generally rigid band whereby the sclera is expanded in the region of the ciliary body.
A further object is to provide a segmented scleral band that can be assembled on the eye to provide a generally rigid band.
A further object is to provide a segmented scleral band having segments that can be inserted into tunnels prepared in the substance of the sclera and assembled thereafter to provide a generally rigid band.
A further object is to provide a treatment for hyperopia.
A further object is to provide a treatment for primary open angle glaucoma.
A further object is to provide a treatment for ocular hypertension.
A further object is to provide a treatment for increasing the amplitude of accommodation of the eye.
Further objects of the invention will become apparent from the description of the invention which follows.

Segmented scleral band for treatment of presbyopia and other eye disorders


A material for an organic electroluminescent device and an organic electroluminescent device including the same, the material including a monoamine compound represented by the following Formula 1:

1. Field
Embodiments relate to a material for an organic electroluminescent device and an organic electroluminescent device using the same.
2. Description of the Related Art
Recently, the development of an organic electroluminescent display is being actively conducted. In addition, the development of an organic electroluminescent device, which is a self-luminescent device used in the organic electroluminescent display, is also being actively conducted.
As the organic electroluminescent device, a structure may include, e.g., an anode, a hole transport layer disposed on the anode, an emission layer disposed on the hole transport layer, an electron transport layer disposed on the emission layer and a cathode disposed on the electron transport layer.

This patent is subject to a terminal disclaimer.

Material for organic electroluminescent device and organic electroluminescent device using the same


A method and system for performing closed loop simulation of a computer model of a physical system and a hardware component of the physical system is provided. An input waveform for the component from the simulated model of the physical system using an initial waveform as a response waveform of the component is generated at a simulation processor. The input waveform is sent from the simulation processor through a network to a real-time playback and record device (RTPR). The RTPR plays back the input waveform in real time to the component and receives a response waveform of the component and provides the response waveform to the simulation processor. Waveform relaxation (WR) converging methods are utilized at the simulation processor to enable convergence of the generated waveforms affected by hardware induced distortions. A WR method with the generated waveforms are performed to provide a closed loop response of the component.

Real-time Hardware-in-the-Loop (HIL) Simulation is a tool to test and/or optimize a piece of hardware, for example a controller, within a simulated system. HIL simulation is applicable in many industries such as aerospace, automotive, maritime, offshore, robotics and electronics and power systems during development, testing and troubleshooting systems where the physical system can be expensive and simulation systems can be utilized in place of the physical system. Typical real-time digital simulation employ simulation hardware using very fast processors in order to perform the simulated systems in real-time suitable for the hardware in the loop simulation. The processors required for existing technologies present the major expense of the device. HIL simulation is faster and safer from an equipment safety point of view than connecting the hardware into the real physical system. Moreover, this technique is useful in validating implementation of designed controllers in embedded systems. However, the degree of complexity and the size of the simulated system may need to be limited to assure the real-time simulation of a model, otherwise, multiple real-time simulator devices are required in order to split the large systems into smaller subsystems simulated in real-time, which implies more costs to provide the simulation.
Systems such as modern power systems can cover large geographic areas and are an example of an industry where HIL simulation can be of significant benefit in deployment, testing and maintenance. The large size of the simulated power networks models coupled with the need for small simulation time-steps imposes significant challenges for performing real-time simulations as it requires very fast parallel processing computers. In real-time digital simulation the simulation software and the real-world hardware must exchange data in every time step, this requires sophisticated and nontrivial interface design between the real and simulated worlds. When real-time digital simulator is interfaced to external hardware, the interface can often result in an inaccurate simulation and even instability due to issues such as amplifier bandwidth and delays. There is no unique interface algorithm that provides the best stable and accurate results for every simulated system, therefore, depending on the system to be simulated, an appropriate interface algorithm must be selected. As a consequence, HIL simulation with real-time digital simulation is infeasible where the hardware under test is geographically remote from the real-time simulator, such as in another city or even another country. In addition, although advances in development of fast processors such as digital signal processing (DSP) and FPGA technologies has helped address the challenges of simulating large power systems significant hardware and software expenses are incurred.
Waveform Relaxation (WR) based HIL simulation is considerably cost effective compared to the state of the art technologies. Waveform relaxation is traditionally an iterative method of solving systems of ordinary nonlinear differential equations. WR based HIL simulation can also be used for the iterative simulation of systems, for a large system, the system can be divided into two or more different subsystems and each be simulated independent of the other(s). The results are exchanged when simulations end, and the simulations are repeated. If certain convergence criteria are met, after enough number of iterations, the simulation results show no more changes, and can be said to have converged.
A significant drawback of the WR based HIL simulation is the possible slow convergence of the simulation. The WR convergence speed highly depends on the tightness between the subsystems. If the hardware-under-test is loosely connected to the simulation, then only a few iterations are required to reach convergence. Another disadvantage of the WR based HIL simulation is the limited total simulation time. Available real-time HIL simulators exchange the data points at every simulation time step placing considerable processor and memory demands. However, there is usually no need for a long simulation period if the transient response of the HUT is of interest. Another shortcoming of the WR based HIL simulation is that it is inherently an off-line simulation, for example once a step change is applied to the simulated system or to the parameters of the hardware, the results cannot be immediately followed. Instead, the WR should be allowed to converge in a course of iterations. The main question regarding the WR based HIL simulation is whether the simulation convergences for a specific system. The tight connection of the subsystems can slow down the convergence speed and the simulation may sometimes not converge. In addition, the WR simulation can encounter instabilities when applied to the HIL simulation with the presence of noise and analog-digital converters inaccuracies even for theoretical stable regions of simulation. The instabilities can occur when the simulation and hardware subsystems are connected fairly tight and can impact the accuracy and speed of the simulation.
Accordingly, systems and methods that enable improved hardware-in-loop (HIL) simulations remain highly desirable.
It will be noted that throughout the appended drawings, like features are identified by like reference numerals.

Closed loop simulation of a computer model of a physical system and an actual real-time hardware component of the physical system