654 research outputs found

### Modeling and Estimation for Real-Time Microarrays

Microarrays are used for collecting information about a large number of different genomic particles simultaneously. Conventional fluorescent-based microarrays acquire data after the hybridization phase. During this phase, the target analytes (e.g., DNA fragments) bind to the capturing probes on the array and, by the end of it, supposedly reach a steady state. Therefore, conventional microarrays attempt to detect and quantify the targets with a single data point taken in the steady state. On the other hand, a novel technique, the so-called real-time microarray, capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to conventional microarrays. In this paper, we study the signal processing aspects of the real-time microarray system design. In particular, we develop a probabilistic model for real-time microarrays and describe a procedure for the estimation of target amounts therein. Moreover, leveraging on system identification ideas, we propose a novel technique for the elimination of cross hybridization. These are important steps toward developing optimal detection algorithms for real-time microarrays, and to understanding their fundamental limitations

### On the existence of codes with constant bounded PMEPR for multicarrier signals

It has been shown that with probability one the peak to mean envelope power ratio (PMEPR) of any random codeword chosen from a symmetric QAM/PSK constellation is log n where n is the number of subcarriers [1]. In this paper, the existence of
codes with nonzero rate and PMEPR bounded by a constant is established

### New Null Space Results and Recovery Thresholds for Matrix Rank Minimization

Nuclear norm minimization (NNM) has recently gained significant attention for
its use in rank minimization problems. Similar to compressed sensing, using
null space characterizations, recovery thresholds for NNM have been studied in
\cite{arxiv,Recht_Xu_Hassibi}. However simulations show that the thresholds are
far from optimal, especially in the low rank region. In this paper we apply the
recent analysis of Stojnic for compressed sensing \cite{mihailo} to the null
space conditions of NNM. The resulting thresholds are significantly better and
in particular our weak threshold appears to match with simulation results.
Further our curves suggest for any rank growing linearly with matrix size $n$
we need only three times of oversampling (the model complexity) for weak
recovery. Similar to \cite{arxiv} we analyze the conditions for weak, sectional
and strong thresholds. Additionally a separate analysis is given for special
case of positive semidefinite matrices. We conclude by discussing simulation
results and future research directions.Comment: 28 pages, 2 figure

### Existence of codes with constant PMEPR and related design

Recently, several coding methods have been proposed to reduce the high peak-to-mean envelope ratio (PMEPR) of multicarrier signals. It has also been shown that with probability one, the PMEPR of any random codeword chosen from a symmetric quadrature amplitude modulation/phase shift keying (QAM/PSK) constellation is logn for large n, where n is the number of subcarriers. Therefore, the question is how much reduction beyond logn can one asymptotically achieve with coding, and what is the price in terms of the rate loss? In this paper, by optimally choosing the sign of each subcarrier, we prove the existence of q-ary codes of constant PMEPR for sufficiently large n and with a rate loss of at most log/sub q/2. We also obtain a Varsharmov-Gilbert-type upper bound on the rate of a code, given its minimum Hamming distance with constant PMEPR, for large n. Since ours is an existence result, we also study the problem of designing signs for PMEPR reduction. Motivated by a derandomization algorithm suggested by Spencer, we propose a deterministic and efficient algorithm to design signs such that the PMEPR of the resulting codeword is less than clogn for any n, where c is a constant independent of n. For symmetric q-ary constellations, this algorithm constructs a code with rate 1-log/sub q/2 and with PMEPR of clogn with simple encoding and decoding. Simulation results for our algorithm are presented

### Maximum-Likelihood Sequence Detection of Multiple Antenna Systems over Dispersive Channels via Sphere Decoding

Multiple antenna systems are capable of providing high data rate transmissions over wireless channels. When the channels are dispersive, the signal at each receive antenna is a combination of both the current and past symbols sent from all transmit antennas corrupted by noise. The optimal receiver is a maximum-likelihood sequence detector and is often considered to be practically infeasible due to high computational complexity (exponential in number of antennas and channel memory). Therefore, in practice, one often settles for a less complex suboptimal receiver structure, typically with an equalizer meant to suppress both the intersymbol and interuser interference, followed by the decoder. We propose a sphere decoding for the sequence detection in multiple antenna communication systems over dispersive channels. The sphere decoding provides the maximum-likelihood estimate with computational complexity comparable to the standard space-time decision-feedback equalizing (DFE) algorithms. The performance and complexity of the sphere decoding are compared with the DFE algorithm by means of simulations

### Manifold Optimization Over the Set of Doubly Stochastic Matrices: A Second-Order Geometry

Convex optimization is a well-established research area with applications in
almost all fields. Over the decades, multiple approaches have been proposed to
solve convex programs. The development of interior-point methods allowed
solving a more general set of convex programs known as semi-definite programs
and second-order cone programs. However, it has been established that these
methods are excessively slow for high dimensions, i.e., they suffer from the
curse of dimensionality. On the other hand, optimization algorithms on manifold
have shown great ability in finding solutions to nonconvex problems in
reasonable time. This paper is interested in solving a subset of convex
optimization using a different approach. The main idea behind Riemannian
optimization is to view the constrained optimization problem as an
unconstrained one over a restricted search space. The paper introduces three
manifolds to solve convex programs under particular box constraints. The
manifolds, called the doubly stochastic, symmetric and the definite multinomial
manifolds, generalize the simplex also known as the multinomial manifold. The
proposed manifolds and algorithms are well-adapted to solving convex programs
in which the variable of interest is a multidimensional probability
distribution function. Theoretical analysis and simulation results testify the
efficiency of the proposed method over state of the art methods. In particular,
they reveal that the proposed framework outperforms conventional generic and
specialized solvers, especially in high dimensions

### On the Entropy Region of Discrete and Continuous Random Variables and Network Information Theory

We show that a large class of network information theory problems can be cast as convex optimization over the convex space of entropy vectors. A vector in 2^(n) - 1 dimensional space is called entropic if each of its entries can be regarded as the joint entropy of a particular subset of n random variables (note that any set of size n has 2^(n) - 1 nonempty subsets.) While an explicit characterization of the space of entropy vectors is well-known for n = 2, 3 random variables, it is unknown for n > 3 (which is why most network information theory problems are open.) We will construct inner bounds to the space of entropic vectors using tools such as quasi-uniform distributions, lattices, and Cayley's hyperdeterminant

### Diversity-Multiplexing Gain Trade-off of a MIMO System with Relays

We find the diversity-multiplexing gain trade-off of a multiple-antenna (MIMO) system with M transmit antennas, N receive antennas, R relay nodes, and with independent Rayleigh fading, in which the relays apply a distributed space-time code. In this two-stage scheme the trade-off is shown to coincide with that of a MIMO system with R transmit and min{M, N} receive antennas

### Group Frames with Few Distinct Inner Products and Low Coherence

Frame theory has been a popular subject in the design of structured signals
and codes in recent years, with applications ranging from the design of
measurement matrices in compressive sensing, to spherical codes for data
compression and data transmission, to spacetime codes for MIMO communications,
and to measurement operators in quantum sensing. High-performance codes usually
arise from designing frames whose elements have mutually low coherence.
Building off the original "group frame" design of Slepian which has since been
elaborated in the works of Vale and Waldron, we present several new frame
constructions based on cyclic and generalized dihedral groups. Slepian's
original construction was based on the premise that group structure allows one
to reduce the number of distinct inner pairwise inner products in a frame with
$n$ elements from $\frac{n(n-1)}{2}$ to $n-1$. All of our constructions further
utilize the group structure to produce tight frames with even fewer distinct
inner product values between the frame elements. When $n$ is prime, for
example, we use cyclic groups to construct $m$-dimensional frame vectors with
at most $\frac{n-1}{m}$ distinct inner products. We use this behavior to bound
the coherence of our frames via arguments based on the frame potential, and
derive even tighter bounds from combinatorial and algebraic arguments using the
group structure alone. In certain cases, we recover well-known Welch bound
achieving frames. In cases where the Welch bound has not been achieved, and is
not known to be achievable, we obtain frames with close to Welch bound
performance

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