1.-8. (canceled)
9. A method for deriving parasitic signals from modulated digital signals, with each signal being determined from digital data to be transmitted by using a modulation schema, comprising:
receiving, at a receiver end, modulated digital signals transmitted by a transmitting end, the received signals including parasitic signals and the transmitted modulated signals;
reconstructing the transmitted modulated digital signals at a receiver end;
subtracting the reconstructed modulated digital signals from the received modulated digital signals; and
estimating the parasitic signals from a result of the subtraction without influence from prior filtering at the receiver end,
wherein mid frequency, bandwidth andor power is determined from the estimated parasitic signals.
10. The method as claimed in claim 9, wherein at the receiver end, values of the digital data to be transmitted determined are used in the reconstructing.
11. The method as claimed in claim 9, wherein at the receiver end, influencing parameters of a transmission channel are used in the reconstructing.
12. The method as claimed in claim 11, wherein the influencing parameter include an attenuation of the transmission channel andor a time delay through the transmission channel.
13. The method as claimed in claim 12, wherein the transmitted modulated digital signals are shown as a time-related function andor as a linear combination of N modulated digital signals with an index i=1, 2, . . . , N with the digital data to be transmitted as parameters.
14. The method as claimed in claim 10, wherein at the receiver end, influencing parameters of a transmission channel are used in the reconstructing.
15. The method as claimed in claim 15, wherein the influencing parameter include an attenuation of the transmission channel andor a time delay through the transmission channel.
16. The method as claimed in claim 15, wherein the transmitted modulated digital signals are shown as a time-related function andor as a linear combination of N modulated digital signals with an index i=1, 2, . . . , N with the digital data to be transmitted as parameters.
17. The method as claimed in claim 9, wherein the transmitted modulated digital signals are shown as a time-related function andor as a linear combination of N modulated digital signals with an index i=1, 2, . . . , N with the digital data to be transmitted as parameters.
18. The method as claimed in claim 17, wherein a received signal is represented as
Asmi(c, t\u2212td)+s\u2032ai(t), wherein
A is the attenuation of the transmission channel,
smi is a transmitted modulated digital signal with an index i, by which values of 1 to N are assumed,
c the digital data to be transmitted,
td the time delay of the transmission channel, and
s\u2032ai(t) the parasitic signals, by which the i-th transmitted modulated digital signal smi is influenced.
19. The method as claimed in claim 18, wherein at the receiver end, the reconstruction of the transmitted modulated digital signals
\xc2smi(\u0109, t\u2212{circumflex over (t)}d), wherein
\xc2 is the estimation of the attenuation,
\u015dmi reconstructed transmitted modulated digital signal with the index i, by which values from 1 to N are assumed,
\u0109 the values of the digital data to be transmitted, determined at the receiver end, and
{circumflex over (t)}d the estimation of the time delay.
20. The method as claimed in claim 19, wherein an estimation of the parasitic signals \xa7a is determined according to the formula
s
^
a
=
s
a
+
\u2211
i
=
1
N
\ue89e
A
i
\ue89e
s
mi
\ue8a0
(
c
i
,
t
–
t
di
)
–
A
^
i
\ue89e
s
mi
\ue8a0
(
c
^
i
,
t
–
t
^
di
)
\uf613
s
01
\ue8a0
(
t
)
\uf613
s
0
\ue8a0
(
t
)
,
wherein
\xc2 is the estimation of the attenuation,
\u015dmi reconstructed transmitted modulated digital signal with the index i, by which values from 1 to N are assumed,
\u0109 the values of the digital data to be transmitted, determined at the receiver end, and
{circumflex over (t)}d the estimation of the time delay.
sa is the parasitic signals,
the minuend of the subtraction designating a proportion of the received modulated digital signals,
the subtrahend of the subtraction designating, and
the reconstructed modulated digital signals \u015dmi and s0(t) approaching zero with increasing estimation quality.
21. The method as claimed in claim 9, wherein a received signal is represented as
Asmi(c, t\u2212td)+s\u2032ai(t), wherein
A is the attenuation of the transmission channel,
smi is a transmitted modulated digital signal with an index i, by which values of 1 to N are assumed,
c the digital data to be transmitted,
td the time delay of the transmission channel, and
s\u2032ai(t) the parasitic signals, by which the i-th transmitted modulated digital signal smi is influenced.
22. The method as claimed in claim 9, wherein at the receiver end, the reconstruction of the transmitted modulated digital signals
\xc2smi(\u0109, t\u2212{circumflex over (t)}d), wherein
\xc2 is the estimation of the attenuation,
\u015dmi reconstructed transmitted modulated digital signal with the index i, by which values from 1 to N are assumed,
\u0109 the values of the digital data to be transmitted, determined at the receiver end, and
{circumflex over (t)}d the estimation of the time delay.
23. The method as claimed in claim 9, wherein an estimation of the parasitic signals \u015da is determined according to the formula
s
^
a
=
s
a
+
\u2211
i
=
1
N
\ue89e
A
i
\ue89e
s
mi
\ue8a0
(
c
i
,
t
–
t
di
)
–
A
^
i
\ue89e
s
mi
\ue8a0
(
c
^
i
,
t
–
t
^
di
)
\uf613
s
01
\ue8a0
(
t
)
\uf613
s
0
\ue8a0
(
t
)
,
wherein
\xc2 is the estimation of the attenuation,
\u015dmi reconstructed transmitted modulated digital signal with the index i, by which values from 1 to N are assumed,
\u0109 the values of the digital data to be transmitted, determined at the receiver end, and
{circumflex over (t)}d the estimation of the time delay.
\u015da is the parasitic signals,
the minuend of the subtraction designating a proportion of the received modulated digital signals,
the subtrahend of the subtraction designating, and
the reconstructed modulated digital signals \u015dmi and s0(t) approaching zero with increasing estimation quality.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A text inputting method, comprising:
obtaining a user input;
generating a candidate sentence list according to the user input;
for each candidate sentence in the candidate sentence list, respectively calculating a standard conditional probability of each word in the candidate sentence according to a universal language model;
respectively calculating a cache conditional probability of each word in the candidate sentence according to a preconfigured modeling policy, the user input and a precached user input;
calculating a mixed conditional probability of each word according to the standard conditional probability and the cache conditional probability;
obtaining an on-screen probability of the candidate sentence according to the mixed conditional probability;
sorting candidate sentences in the candidate sentence list according to their on-screen probabilities;
and outputting the sorted candidate sentence list;
wherein:
the cache conditional probability of each word in the candidate sentence is positively related to a time function value of the word, and the time function value is related to a time when the word enters a cache area used for the pre-cached user input;
wherein the time function value is a result obtained by dividing a preconfigured constant by a time interval between an ith word enters the cache area and a word currently inputted by the user.
2. The text inputting method of claim 1, wherein:
the pre-cached user input corresponds to a user identifier; and the user identifier is an account registered by a user in an inputting software, or an identification number assigned for the user, or an IP address or MAC address associated with a device used by the user.
3. The text inputting method of claim 1, wherein the pre-cached user input adopts a queue data structure, the value of the time interval of the i th word is a position of the i th word in the cache queue.
4. The text inputting method of claim 1, wherein:
in the sorted candidate sentence list, a sentence with a largest on-screen probability is selected and outputted, and the sentence with the largest on-screen probability is cached in the pre-cached user input.
5. The text inputting method of claim 1, wherein the process of respectively calculating the standard conditional probability of each word in the candidate sentence according to the universal language model comprises: respectively calculating the standard conditional probability of each the candidate sentence according to a pre-created standard Ngram language model, including:
obtaining a number of times k\u2032i that a word sequence which includes the i th word and a preconfigured constant number of words before the i th word emerges in training material of the standard N gram language model;
obtaining a number of times k\u2032i\u22121 that a word sequence which includes the preconfigured constant number of words before the ith word emerges in the training material of the standard Ngram language model;
calculating a ratio of k\u2032i to k\u2032i\u22121, and taking the ratio as the standard conditional probability of the ith word of the candidate sentence.
6. The text inputting method of claim 1, wherein calculating a cache conditional probability of an ith word in the candidate sentence comprises:
obtaining a number of times ki; that a word sequence which includes the i th word and a predefined number of consecutive words before the i th word emerges in the pre-cached user input;
obtaining a number of times ki\u22121 that a word sequence which includes the predefined number of words before the i th word emerges in the pre-cached user input;
obtaining the time function value of the i th word; and
calculating a ratio of ki to ki\u22121, multiplying the ratio with the time function value of the i th word to obtain the cache-based conditional probability of the i th word in the candidate sentence.
7. The text inputting method of claim 1, wherein calculating the mixed conditional probability of an ith word in the candidate sentence comprises:
A1, determining an interpolation parameter which is between 0 and 1;
A2, determining a product of the interpolation parameter and the standard conditional probability of the ith word;
A3, calculating a product of a difference between 1 and the interpolation parameter and the cache conditional probability of the ith word; and
A4, calculating a sum of the products obtained in A2 and A3 and taking the sum as the mixed conditional probability of the ith word.
8. A text inputting method comprising:
obtaining a user input;
generating a candidate sentence list according to the user input;
for each candidate sentence in the candidate sentence list, respectively calculating a standard conditional probability of each word in the candidate sentence according to a universal language model;
respectively calculating a cache conditional probability of each word in the candidate sentence according to a preconfigured modeling policy, the user input and a pre-cached user input;
calculating a mixed conditional probability of each word according to the standard conditional probability and the cache conditional probability;
obtaining an on-screen probability of the candidate sentence according to the mixed conditional probability;
sorting candidate sentences in the candidate sentence list according to their on-screen probabilities;
and outputting the sorted candidate sentence list;
wherein:
in the sorted candidate sentence list, a sentence with a largest on-screen probability is selected and outputted, and the sentence with the largest on-screen probability is cached;
the process of respectively calculating a standard conditional probability of each word in the candidate sentence according to the universal language model comprises:
respectively calculating the standard conditional probability of each word in the candidate sentence according to a pre-created standard Ngram language model, and calculating the cache conditional probability of an ith word in the candidate sentence comprises:
obtaining a number of times ki that a word sequence which includes the ith word and a predefined constant number of words emerges before the ith word in cached training material;
obtaining a number of times ki\u22121 that a word sequence which includes a predefined constant number of words emerges before the ith word in cached training material;
obtaining a time function value of the ith word; and
calculation a ratio of ki to ki\u22121, multiplying the ratio with the time function value of the ith word to obtain the cache conditional probability of the ith word in the user input.
9. The text inputting method of claim 8, wherein the time function value is a result obtained by dividing a preconfigured constant by a time interval between the ith word enters the cache area and a word currently inputted by the user.
10. The text inputting method of claim 8, Wherein calculating a standard conditional probability of an ith word in the candidate sentence comprises:
obtaining a number of times k\u2032; that a word sequence which includes the i th word and a preconfigured constant number of words before the i th word emerges in training material of the standard N gram language model;
obtaining a number of times k\u2032i\u22121 that a word sequence which includes the preconfigured constant number of words before the ith word emerges in the training material of the standard N gram language model;
calculating a ratio of k\u2032i to k\u2032i\u22121, and taking the ratio as the standard conditional probability of the ith word of the candidate sentence.
11. The text inputting method of claim 8, wherein calculating the mixed conditional probability of an ith word in the candidate sentence comprises:
A1, determining an interpolation parameter which is between 0 and 1;
A2, determining a product of the interpolation parameter and the standard conditional probability of the ith word;
A3, calculating a product of a difference between 1 and the interpolation parameter and the cache conditional probability of the ith word; and
A4, calculating a sum of the products obtained in A2 and A3 and taking the sum as the mixed conditional probability of the ith word.
12. The text inputting method of claim 9, wherein the pre-cached user input adopts a queue data structure, the value of the time interval of the i th word is a position of the i th word in the cache queue.
13. The text inputting method of claim 8, wherein if the precached user input is null, the cache conditional probability of each word in the candidate sentence equals to the standard conditional probability of the word.
14. A text processing apparatus, comprising:
one or more processors;
memory; and
one or more program modules stored in the memory and to be executed by the one or more processors, the one or more program modules including:
a universal language model module, a cache module, a cache-based language modeling module and a mixed model module; wherein the universal language model module is to receive a user input, generate a candidate sentence list according to the user input, calculate a standard conditional probability of each word in the each candidate sentence respectively and output the standard conditional probability to the mixed model module;
the cache module is to cache a sentence outputted by the mixed model module;
the cache-based language modeling module is to respectively calculate a cache conditional probability of each word in each candidate sentence according to a preconfigured cache-based language modeling policy, the user input and the sentences cached in the cache module, and output the cache conditional probability to the mixed model module;
the mixed model module is to calculate a mixed conditional probability of each word according to the standard conditional probability and the cache conditional probability, calculate a sentence probability of each candidate sentence according to the mixed conditional probability, and select and output the sentence with the largest sentence probability; and
the cache-based language modeling module is further configured to calculate a time function value of each word, wherein the cache-based conditional probability of each word is positively related to the time function value of the word, and the time function value is related to a time when the word enters the cache module;
wherein the time function value is a result obtained by dividing a preconfigured constant by a time interval between an ith word enters the cache area and a word currently inputted by the user.
15. The text processing apparatus of claim 14, wherein the universal language model is a standard Ngram language model module, the standard Ngram language model module comprising: a first word sequence frequency counting unit, a second word sequence frequency counting unit and a standard conditional probability calculating unit;
the first word sequence frequency counting unit is to obtain a number of times k\u2032i that a word sequence which includes an ith word and a predefined constant number of words before the ith word emerges in the training material of the standard Ngram language model, and output k\u2032i to the standard conditional probability calculating unit;
the second word sequence frequency counting unit is to obtain a number of times k\u2032i\u22121 that a word sequence which includes the predefined constant number of words before the ith word emerges in the training material of the standard Ngram language model, and output k\u2032i\u22121 to the standard conditional probability calculating unit; and
the standard probability calculating unit is to calculate a ratio of k\u2032i to k\u2032i\u22121, and take the ratio as the standard conditional probability of the ith word in the candidate sentence.
16. The text processing apparatus of claim 15, the cache-based language modeling module comprises: a third word sequence frequency counting unit, a fourth word sequence frequency counting unit, a time function value obtaining unit and a cache conditional probability calculating unit;
the third word sequence frequency counting unit is to obtain a number of times ki that a word sequence which includes the i th word and the predefined constant number of words before the i th word emerges in cached training material from the cache module, and output ki to the cache conditional probability calculating unit;
the fourth word sequence frequency counting unit is to obtain a number of times ki\u22121 that a word sequence which includes the predefined constant number of words before the i th word emerges in the cached training material, and output ki\u22121 to the cache conditional probability calculating unit;
the time function value obtaining unit is to obtain a time function value of the ith word and output the value to the cache conditional probability calculating unit; and
the cache conditional probability calculating unit is to calculate a ratio of ki to ki\u22121, multiply the ratio with the time function value of the ith word to obtain the cache conditional probability of the ith word in the candidate sentence.
17. The text processing apparatus of claim 16, wherein the mixed model module includes: an interpolation parameter storage unit, a first multiplying unit, a second multiplying unit, a mixed conditional probability calculating unit, a sentence probability calculating unit and an output sentence selecting unit;
the interpolation parameter storage unit is to store an interpolation parameter whose value is configured between 0 and 1 in advance;
the first multiplying unit is to calculate a product of the interpolation parameter stored in the interpolation parameter storage unit and the standard conditional probability of the ith word in the candidate sentence and output the product to the mixed conditional probability calculating unit;
the second multiplying unit is to calculate a difference between 1 and the interpolation parameter and calculate a product of the difference and the cache conditional probability of the ith word, and output the product to the mixed conditional probability calculating unit;
the mixed conditional probability calculating unit is to add the products related to the ith word and take the sum as the mixed conditional probability of the ith word;
the sentence probability calculating unit is to calculate a product of the mixed conditional probability of each word to obtain the sentence probability of the candidate sentence;
the output sentence selecting unit is to select and output the sentence with the largest sentence probability calculated by the sentence probability calculating unit.
18. The text processing apparatus of claim 14, wherein the cache module adopts a queue data structure, the value of the time interval of the i th word is a position of the i th word in the cache queue.