Enantiospecific synthesis of functionalised bicyclo[3.3.1] nonanes from 10-bromocarvones via tandem intermolecular alkylation-intramolecular Michael addition sequence

A simple and convenient tandem methodology for the enantiospecific generation of functionalised bicyclo[3.3.1] nonanes 9,14-18, via intermolecular alkylation of Michael donors with 10-bromocarvones 7, 10 and 11, followed by intramolcular Michael addition, is achieved. An unsuccessful attempt for the extension of the methodology for a possible short enantiospecific approach to AB-ring system 22 of taxanes via the allyl bromide 21, is also described.

Two new families of low-correlation interleaved QAM sequence

Two families of low correlation QAM sequences are presented here. In a CDMA setting, these sequences have the ability to transport a large amount of data as well as enable variable-rate signaling on the reverse link. The first family Á2SQ - B2− is constructed by interleaving 2 selected QAM sequences. This family is defined over M 2-QAM, where M = 2 m , m ≥ 2. Over 16-QAM, the normalized maximum correlation [`(q)]maxmax is bounded above by <~1.17 ÖNUnknown control sequence '\lesssim' , where N is the period of the sequences in the family. This upper bound on [`(q)]maxmax is the lowest among all known sequence families over 16-QAM.The second family Á4SQ4 is constructed by interleaving 4 selected QAM sequences. This family is defined over M 2-QAM, where M = 2 m , m ≥ 3, i.e., 64-QAM and beyond. The [`(q)]maxmax for sequences in this family over 64-QAM is upper bounded by <~1.60 ÖNUnknown control sequence '\lesssim' . For large M, [`(q)]max <~1.64 ÖNUnknown control sequence '\lesssim' . These upper bounds on [`(q)]maxmax are the lowest among all known sequence families over M 2-QAM, M = 2 m , m ≥ 3.

Finite Signal-set Capacity of Two-user Gaussian Multiple Access Channel

The capacity region of a two-user Gaussian Multiple Access Channel (GMAC) with complex finite input alphabets and continuous output alphabet is studied. When both the users are equipped with the same code alphabet, it is shown that, rotation of one of the user’s alphabets by an appropriate angle can make the new pair of alphabets not only uniquely decodable, but will result in enlargement of the capacity region. For this set-up, we identify the primary problem to be finding appropriate angle(s) of rotation between the alphabets such that the capacity region is maximally enlarged. It is shown that the angle of rotation which provides maximum enlargement of the capacity region also minimizes the union bound on the probability of error of the sumalphabet and vice-verse. The optimum angle(s) of rotation varies with the SNR. Through simulations, optimal angle(s) of rotation that gives maximum enlargement of the capacity region of GMAC with some well known alphabets such as M-QAM and M-PSK for some M are presented for several values of SNR. It is shown that for large number of points in the alphabets, capacity gains due to rotations progressively reduce. As the number of points N tends to infinity, our results match the results in the literature wherein the capacity region of the Gaussian code alphabet doesn’t change with rotation for any SNR.

GMM basedbayesian approach to speech enhancement in signal/transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator.The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Integer Sequence window based Reconfigurable FIR filters

Conventional hardware implementation techniques for FIR filters require the computation of filter coefficients in software and have them stored in memory. This approach is static in the sense that any further fine tuning of the filter requires computation of new coefficients in software. In this paper, we propose an alternate technique for implementing FIR filters in hardware. We store a considerably large number of impulse response coefficients of the ideal filter (having box type frequency response) in memory. We then do the windowing process, on these coefficients, in hardware using integer sequences as window functions. The integer sequences are also generated in hardware. This approach offers the flexibility in fine tuning the filter, like varying the transition bandwidth around a particular cutoff frequency.

Antenna Selection Training in MIMO-OFDM/OFDMA Cellular Systems

Antenna selection allows multiple-antenna systems to achieve most of their promised diversity gain, while keeping the number of RF chains and, thus, cost/complexity low. In this paper we investigate antenna selection for fourth-generation OFDMA- based cellular communications systems, in particular, 3GPP LTE (long-term evolution) systems. We propose a training method for antenna selection that is especially suitable for OFDMA. By means of simulation, we evaluate the SNR-gain that can be achieved with our design. We find that the performance depends on the bandwidth assigned to each user, the scheduling method (round-robin or frequency-domain scheduling), and the Doppler spread. Furthermore, the signal-to-noise ratio of the training sequence plays a critical role. Typical SNR gains are around 2 dB, with larger values obtainable in certain circumstances.

Speech segmentation using extrema based signal track length measure

We introduce a novel temporal feature of a signal, namely extrema-based signal track length (ESTL) for the problem of speech segmentation. We show that ESTL measure is sensitive to both amplitude and frequency of the signal. The short-time ESTL (ST_ESTL) shows a promising way to capture the significant segments of speech signal, where the segments correspond to acoustic units of speech having distinct temporal waveforms. We compare ESTL based segmentation with ML and STM methods and find that it is as good as spectral feature based segmentation, but with lesser computational complexity.