On joint maximum-likelihood estimation of PCR efficiency and initial amount of target

We consider the problem of estimating unknown parameters of the real-time polymerase chain reaction (RTPCR) from noisy observations. The joint ML estimator of the RT-PCR efficiency and the initial number of DNA target molecules is derived. The mean-square error performance of the estimator is studied via simulations. The simulation results indicate that the proposed estimator significantly outperforms a competing technique

Signal Processing Aspects of Real-Time DNA Microarrays

Data acquisition in conventional fluorescent-based microarrays takes place after the completion of a hybridization phase. During the hybridization phase, target analytes bind to their corresponding capturing probes on the array. The conventional microarrays attempt to detect presence and quantify amounts of the targets by collecting a single data point, supposedly taken after the hybridization process has reached its steady-state. Recently, so-called real-time microarrays capable of acquiring not only the steady-state data but the entire kinetics of hybridization have been proposed in [1]. The richness of the information obtained by the real-time microarrays promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to the conventional microarrays. In the current paper, we study the signal processing aspects of the real-time microarray data acquisition

Divide-and-conquer: Approaching the capacity of the two-pair bidirectional Gaussian relay network

The capacity region of multi-pair bidirectional relay networks, in which a relay node facilitates the communication between multiple pairs of users, is studied. This problem is first examined in the context of the linear shift deterministic channel model. The capacity region of this network when the relay is operating at either full-duplex mode or half-duplex mode for arbitrary number of pairs is characterized. It is shown that the cut-set upper-bound is tight and the capacity region is achieved by a so called divide-and-conquer relaying strategy. The insights gained from the deterministic network are then used for the Gaussian bidirectional relay network. The strategy in the deterministic channel translates to a specific superposition of lattice codes and random Gaussian codes at the source nodes and successive interference cancelation at the receiving nodes for the Gaussian network. The achievable rate of this scheme with two pairs is analyzed and it is shown that for all channel gains it achieves to within 3 bits/sec/Hz per user of the cut-set upper-bound. Hence, the capacity region of the two-pair bidirectional Gaussian relay network to within 3 bits/sec/Hz per user is characterized.Comment: IEEE Trans. on Information Theory, accepte

Estimation over Communication Networks: Performance Bounds and Achievability Results

This paper considers the problem of estimation over communication networks. Suppose a sensor is taking measurements of a dynamic process. However the process needs to be estimated at a remote location connected to the sensor through a network of communication links that drop packets stochastically. We provide a framework for computing the optimal performance in the sense of expected error covariance. Using this framework we characterize the dependency of the performance on the topology of the network and the packet dropping process. For independent and memoryless packet dropping processes we find the steady-state error for some classes of networks and obtain lower and upper bounds for the performance of a general network. Finally we find a necessary and sufficient condition for the stability of the estimate error covariance for general networks with spatially correlated and Markov type dropping process. This interesting condition has a max-cut interpretation

Nucleic Acid Detection Using Bioluminescence Regenerative Cycle and Statistical Signal Processing

An important emerging research area is the study and development of signal processing techniques for rapid real- time nucleic acid detection (1). In this paper, we focus on the newly developed bioluminescence regenerative cycle (BRC) technique, and apply statistical signal processing to the data identification problem. This extended summary provides a description of the BRC platform and experiments, the statistical model employed for analysis, and some preliminary experimental results

Pelleting Turkey Diets

The pelleting process for feed production consists of compressing feed into pellets suitable for the particular animal involved. Feed is forced through small holes in a round die, utilizing steam. Some feedstuffs are more conducive to producing good pellets than others, e.q. wheat, probably because of its gluten, usually improves pellet quality, whereas, oats is difficult to pellet. The addition of fat may allow for increased quality and yield up to a point, but beyond 4—5% fat causes the pellets to be quite unstable and they break apart easily in handling

Improved sparse recovery thresholds with two-step reweighted ℓ_1 minimization

It is well known that ℓ_1 minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsity, as a function of the ratio between the system dimensions, so that with high probability almost all sparse signals can be recovered from iid Gaussian measurements, have been computed and are referred to as “weak thresholds” [4]. In this paper, we introduce a reweighted ℓ_1 recovery algorithm composed of two steps: a standard ℓ_1 minimization step to identify a set of entries where the signal is likely to reside, and a weighted ℓ_1 minimization step where entries outside this set are penalized. For signals where the non-sparse component has iid Gaussian entries, we prove a “strict” improvement in the weak recovery threshold. Simulations suggest that the improvement can be quite impressive—over 20% in the example we consider