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22 Jul 2010: We have developed novel reconstruction algorithms to relax this constraint [1, 2, 15, 20, 23, 31, 33]. ... 20] R.J. Housden, A.H. Gee, R.W. Prager and G.M. Treece. Sensorless freehand 3D ultrasound for non-monotonic and intersecting frames.

19 Oct 2010: 20 September 2010. Congratulations to Roberto Cipolla Professor ofInformation Engineering and Dr Ivor Day SeniorRolls-Royce Research Fellow, at the WhittleLaboratory, who have been elected as Fellows of theRoyal Academy of

5 Jan 2010: Cambridge UniversityEngineering Department. 20. Acoustic Modelling for Speech Recognition: Hidden Markov Models and Beyond? ... IEEE Int.Conf. Acoust., Speech, Signal Process., Orlando, FL, May 2002. [20] D.

13 May 2010: effect very similar to that observed with non-axially aligned,elliptical inclusions [20, 21]. ... This is very likely the “fillin” effect [20, 21]. 6. (a) 10 mm 0.

19 Nov 2010: 19). and. Σ̂(m) =. τγ(m)(τ). (. q(m)(τ))α(m)/21. Ŵ(m)(τ). τγ(m)(τ). , (20). whereq(m)(τ) is defined in equation (10),̂W(m)(τ) ... It is not knownthat the overall likelihood is guaranteed to increase with the updategiven by (19)–(20),

13 May 2010: 1. 2.5. (f) (g) 0. 10. 20. Pixel. 0. 63. Str.

25 Oct 2010: Prior video-basedattempts [18]–[20] have shown that including a strong temporalconstraint deteriorates recognition performance when personsmove arbitrarily in a testing video sequence. ... CVPR, Madison, WI, 2003, pp.340–345. [20] K. Lee, M. Yang,

8 Sep 2010: 3.86 3.63 3.3305 11.20 10.02 9.16 8.6600 29.55 28.00 25.09 23.90.

10 Aug 2010: This compression can be achieved ina laboratory setting using an external fixture to compress the scanning subject [1, 8, 10, 15, 20, 22]. ... brain 1 14.75 15.72 20.90brain 2 8.35 8.68 14.60brain 3 11.37 11.49 17.53.

14 Sep 2010: SYN HMM 30 9.20 8.51 9.34 9.02HTS 5 8.41 8.03 8.70 8.38. ... Beyond HMM Workshop, December 2004. [20] H. AlDamarki, “Filter trees for noise robust small vocabulary speechrecognition,” M.Phil.

26 May 2010: Time (s)0 20 40 60 80. Relia. bili. ty. 60. 80.

14 Sep 2010: T)(20). µϕ =1. n. ni=1. ϕ(Yi; λ) (21). where there are n training data sequences Y1,. , ... dB) N1 N2 N3 N4. 20 1.38 †1.45 †1.25 †1.30 1.35.

26 May 2010: based on an approach applied to homogeneous media by Jensen and Svendsen [20]. ... 15.3 mm, and the other with 20.5 mm. The thickness of each top layer was evaluated later based on.

3 Sep 2010: This makes them impractical for very rapid adaptation, thoughmodifications to improve robustness are possible [7, 20]. ... 0.02. 0.04. 0.06. 0.08. 0.1. 0.12. 0.14. 0.16. 0.18. 0.2. (a) Clean Speech (speech mean=0)25 20 15 10 5

12 Jan 2010: The whole processtakes approximately 15 minutes for typical low resolution data, producing a surface with up to 20,000 ver-tices. ... results. 20 3 RESULTS. Table 4: Cortical thickness estimation error, for errors below 4 mm and thicknesses in the range 0

25 Oct 2010: Prior video-basedattempts [18]–[20] have shown that including a strong temporalconstraint deteriorates recognition performance when personsmove arbitrarily in a testing video sequence. ... CVPR, Madison, WI, 2003, pp.340–345. [20] K. Lee, M. Yang,

25 Oct 2010: where. A =. ks. n1kn2kn1k n2k (m2k m1k)(m2k m1k). T. (20). ... 20 20 pixelgray-value images were used. LDA was computed with thelabeled train data and class label estimation of the unlabeledsamples, which was obtained by the maximum

25 Feb 2010: 151. 8.20 Combination of derivative kernel with various static kernels. Viterbi statisticswere used and component posteriors were obtained from GMMs trained usingthe original observations.

25 Oct 2010: where. A =. ks. n1kn2kn1k n2k (m2k m1k)(m2k m1k). T. (20). ... 20 20 pixelgray-value images were used. LDA was computed with thelabeled train data and class label estimation of the unlabeledsamples, which was obtained by the maximum

6 Oct 2010: 20. 4 Statistical Machine Translation 214.1 Introduction to Statistical Machine Translation. ... 11. 3.1 Finite-state acceptor representation of a trigram language model. 20.

10 Jun 2010: 20. 2.3.4 Discriminative Training of HMMs. 21. 2.3.4.1 Discriminative Training Criteria. ... 20 15 10 5 0 5 10 15 200. 0.01. 0.02.

20 Oct 2010: Alignment Models and Algorithmsfor Statistical Machine Translation. James Brunning. Cambridge University Engineering Departmentand. Jesus College. August 2010. This degree is submitted to the University of Cambridgefor the degree of Doctor of