Optimal MSE solution for a decision feedback equalizer

Due to the inherent feedback in a decision feedback equalizer (DFE) the minimum mean square error (MMSE) or Wiener solution is not known exactly. The main difficulty in such analysis is due to the propagation of the decision errors, which occur because of the feedback. Thus in literature, these errors are neglected while designing and/or analyzing the DFEs. Then a closed form expression is obtained for Wiener solution and we refer this as ideal DFE (IDFE). DFE has also been designed using an iterative and computationally efficient alternative called least mean square (LMS) algorithm. However, again due to the feedback involved, the analysis of an LMS-DFE is not known so far. In this paper we theoretically analyze a DFE taking into account the decision errors. We study its performance at steady state. We then study an LMS-DFE and show the proximity of LMS-DFE attractors to that of the optimal DFE Wiener filter (obtained after considering the decision errors) at high signal to noise ratios (SNR). Further, via simulations we demonstrate that, even at moderate SNRs, an LMS-DFE is close to the MSE optimal DFE. Finally, we compare the LMS DFE attractors with IDFE via simulations. We show that an LMS equalizer outperforms the IDFE. In fact, the performance improvement is very significant even at high SNRs (up to 33%), where an IDFE is believed to be closer to the optimal one. Towards the end, we briefly discuss the tracking properties of the LMS-DFE.

Antenna Selection in Interference-Constrained Underlay Cognitive Radios: SEP-Optimal Rule and Performance Benchmarking

In the underlay mode of cognitive radio, secondary users are allowed to transmit when the primary is transmitting, but under tight interference constraints that protect the primary. However, these constraints limit the secondary system performance. Antenna selection (AS)-based multiple antenna techniques, which exploit spatial diversity with less hardware, help improve secondary system performance. We develop a novel and optimal transmit AS rule that minimizes the symbol error probability (SEP) of an average interference-constrained multiple-input-single-output secondary system that operates in the underlay mode. We show that the optimal rule is a non-linear function of the power gain of the channel from the secondary transmit antenna to the primary receiver and from the secondary transmit antenna to the secondary receive antenna. We also propose a simpler, tractable variant of the optimal rule that performs as well as the optimal rule. We then analyze its SEP with L transmit antennas, and extensively benchmark it with several heuristic selection rules proposed in the literature. We also enhance these rules in order to provide a fair comparison, and derive new expressions for their SEPs. The results bring out new inter-relationships between the various rules, and show that the optimal rule can significantly reduce the SEP.

A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography

Purpose: Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography. Methods: The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data. Results: The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique. Conclusions: The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time. (C) 2013 American Association of Physicists in Medicine. http://dx.doi.org/10.1118/1.4792459]

Rice LHS1/OsMADS1 Controls Floret Meristem Specification by Coordinated Regulation of Transcription Factors and Hormone Signaling Pathways

SEPALLATA (SEP) MADS box transcription factors mediate floral development in association with other regulators. Mutants in five rice (Oryza sativa) SEP genes suggest both redundant and unique functions in panicle branching and floret development. LEAFY HULL STERILE1/OsMADS1, from a grass-specific subgroup of LOFSEP genes, is required for specifying a single floret on the spikelet meristem and for floret organ development, but its downstream mechanisms are unknown. Here, key pathways and directly modulated targets of OsMADS1 were deduced from expression analysis after its knockdown and induction in developing florets and by studying its chromatin occupancy at downstream genes. The negative regulation of OsMADS34, another LOFSEP gene, and activation of OsMADS55, a SHORT VEGETATIVE PHASE-like floret meristem identity gene, show its role in facilitating the spikelet-to-floret meristem transition. Direct regulation of other transcription factor genes like OsHB4 (a class III homeodomain Leu zipper member), OsBLH1 (a BEL1-like homeodomain member), OsKANADI2, OsKANADI4, and OsETTIN2 show its role in meristem maintenance, determinacy, and lateral organ development. We found that the OsMADS1 targets OsETTIN1 and OsETTIN2 redundantly ensure carpel differentiation. The multiple effects of OsMADS1 in promoting auxin transport, signaling, and auxin-dependent expression and its direct repression of three cytokinin A-type response regulators show its role in balancing meristem growth, lateral organ differentiation, and determinacy. Overall, we show that OsMADS1 integrates transcriptional and signaling pathways to promote rice floret specification and development.

Regulation of lipid biosynthesis, sliding motility, and biofilm formation by a membrane-anchored nucleoid-associated protein of mycobacterium tuberculosis

Bacteria use a number of small basic proteins for organization and compaction of their genomes. By their interaction with DNA, these nucleoid-associated proteins (NAPs) also influence gene expression. Rv3852, a NAP of Mycobacterium tuberculosis, is conserved among the pathogenic and slow-growing species of mycobacteria. Here, we show that the protein predominantly localizes in the cell membrane and that the carboxy-terminal region with the propensity to form a transmembrane helix is necessary for its membrane localization. The protein is involved in genome organization, and its ectopic expression in Mycobacterium smegmatis resulted in altered nucleoid morphology, defects in biofilm formation, sliding motility, and change in apolar lipid profile. We demonstrate its crucial role in regulating the expression of KasA, KasB, and GroEL1 proteins, which are in turn involved in controlling the surface phenotypes in mycobacteria.

Optimal parameter selection for bilateral filters using Poisson unbiased risk estimate

Bilateral filters perform edge-preserving smoothing and are widely used for image denoising. The denoising performance is sensitive to the choice of the bilateral filter parameters. We propose an optimal parameter selection for bilateral filtering of images corrupted with Poisson noise. We employ the Poisson's Unbiased Risk Estimate (PURE), which is an unbiased estimate of the Mean Squared Error (MSE). It does not require a priori knowledge of the ground truth and is useful in practical scenarios where there is no access to the original image. Experimental results show that quality of denoising obtained with PURE-optimal bilateral filters is almost indistinguishable with that of the Oracle-MSE-optimal bilateral filters.

Regulation of Chemokines, CCL3 and CCL4, by interferon gamma and Nitric oxide synthase 2 in mouse macrophages and during Salmonella enterica Serovar Typhimurium infection

Background. Interferon gamma (IFN-gamma) increases the expression of multiple genes and responses; however, the mechanisms by which IFN-gamma downmodulates cellular responses is not well understood. In this study, the repression of CCL3 and CCL4 by IFN-gamma and nitric oxide synthase 2 (NOS2) in macrophages and upon Salmonella typhimurium infection of mice was investigated. Methods. Small molecule regulators and adherent peritoneal exudates cells (A-PECs) from Nos2(-/-)mice were used to identify the contribution of signaling molecules during IFN-gamma-mediated in vitro regulation of CCL3, CCL4, and CXCL10. In addition, infection of bone marrow-derived macrophages (BMDMs) and mice (C57BL/6, Ifn-gamma(-/), and Nos2(-/-)) with S. typhimurium were used to gain an understanding of the in vivo regulation of these chemokines. Results. IFN-gamma repressed CCL3 and CCL4 in a signal transducer and activator of transcription 1 (STAT1)-NOS2-p38 mitogen activated protein kinase (p38MAPK)-activating transcription factor 3 (ATF3) dependent pathway in A-PECs. Also, during intracellular replication of S. typhimurium in BMDMs, IFN-gamma and NOS2 repressed CCL3 and CCL4 production. The physiological roles of these observations were revealed during oral infection of mice with S. typhimurium, wherein endogenous IFN-gamma and NOS2 enhanced serum amounts of tumor necrosis factor alpha and CXCL10 but repressed CCL3 and CCL4. Conclusions. This study sheds novel mechanistic insight on the regulation of CCL3 and CCL4 in mouse macrophages and during S. typhimurium oral infection.

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.

Mycobacterium tuberculosis RsdA provides a conformational rationale for selective regulation of sigma-factor activity by proteolysis

The relative levels of different sigma factors dictate the expression profile of a bacterium. Extracytoplasmic function sigma factors synchronize the transcriptional profile with environmental conditions. The cellular concentration of free extracytoplasmic function sigma factors is regulated by the localization of this protein in a sigma/anti-sigma complex. Anti-sigma factors are multi-domain proteins with a receptor to sense environmental stimuli and a conserved anti-sigma domain (ASD) that binds a sigma factor. Here we describe the structure of Mycobacterium tuberculosis anti-sigma(D) (RsdA) in complex with the -35 promoter binding domain of sigma(D) (sigma(D)(4)). We note distinct conformational features that enable the release of sigma(D) by the selective proteolysis of the ASD in RsdA. The structural and biochemical features of the sigma(D)/RsdA complex provide a basis to reconcile diverse regulatory mechanisms that govern sigma/anti-sigma interactions despite high overall structural similarity. Multiple regulatory mechanisms embedded in an ASD scaffold thus provide an elegant route to rapidly re-engineer the expression profile of a bacterium in response to an environmental stimulus.

SEP-optimal adaptive gain and transmit power amplify-and-forward relaying

Amplify-and-forward (AF) relay based cooperation has been investigated in the literature given its simplicity and practicality. Two models for AF, namely, fixed gain and fixed power relaying, have been extensively studied. In fixed gain relaying, the relay gain is fixed but its transmit power varies as a function of the source-relay (SR) channel gain. In fixed power relaying, the relay's instantaneous transmit power is fixed, but its gain varies. We propose a general AF cooperation model in which an average transmit power constrained relay jointly adapts its gain and transmit power as a function of the channel gains. We derive the optimal AF gain policy that minimizes the fading- averaged symbol error probability (SEP) of MPSK and present insightful and tractable lower and upper bounds for it. We then analyze the SEP of the optimal policy. Our results show that the optimal scheme is up to 39.7% and 47.5% more energy-efficient than fixed power relaying and fixed gain relaying, respectively. Further, the weaker the direct source-destination link, the greater are the energy-efficiency gains.

Power-optimal scheduling for a green base station with delay constraints

We consider a power optimization problem with average delay constraint on the downlink of a Green Base-station. A Green Base-station is powered by both renewable energy such as solar or wind energy as well as conventional sources like diesel generators or the power grid. We try to minimize the energy drawn from conventional energy sources and utilize the harvested energy to the maximum extent. Each user also has an average delay constraint for its data. The optimal action consists of scheduling the users and allocating the optimal transmission rate for the chosen user. In this paper, we formulate the problem as a Markov Decision Problem and show the existence of a stationary average-cost optimal policy. We also derive some structural results for the optimal policy.

Optimal design of timer-based, distributed selection with unknown number of nodes

The timer-based selection scheme is a popular, simple, and distributed scheme that is used to select the best node from a set of available nodes. In it, each node sets a timer as a function of a local preference number called a metric, and transmits a packet when its timer expires. The scheme ensures that the timer of the best node, which has the highest metric, expires first. However, it fails to select the best node if another node transmits a packet within Delta s of the transmission by the best node. We derive the optimal timer mapping that maximizes the average success probability for the practical scenario in which the number of nodes in the system is unknown but only its probability distribution is known. We show that it has a special discrete structure, and present a recursive characterization to determine it. We benchmark its performance with ad hoc approaches proposed in the literature, and show that it delivers significant gains. New insights about the optimality of some ad hoc approaches are also developed.

Regulation of protumorigenic pathways by Insulin like growth factor binding protein2 and its association along with beta-catenin in breast cancer lymph node metastasis

Background: Insulin like growth factor binding proteins modulate the mitogenic and pro survival effects of IGF. Elevated expression of IGFBP2 is associated with progression of tumors that include prostate, ovarian, glioma among others. Though implicated in the progression of breast cancer, the molecular mechanisms involved in IGFBP2 actions are not well defined. This study investigates the molecular targets and biological pathways targeted by IGFBP2 in breast cancer. Methods: Transcriptome analysis of breast tumor cells (BT474) with stable knockdown of IGFBP2 and breast tumors having differential expression of IGFBP2 by immunohistochemistry was performed using microarray. Differential gene expression was established using R-Bioconductor package. For validation, gene expression was determined by qPCR. Inhibitors of IGF1R and integrin pathway were utilized to study the mechanism of regulation of beta-catenin. Immunohistochemical and immunocytochemical staining was performed on breast tumors and experimental cells, respectively for beta-catenin and IGFBP2 expression. Results: Knockdown of IGFBP2 resulted in differential expression of 2067 up regulated and 2002 down regulated genes in breast cancer cells. Down regulated genes principally belong to cell cycle, DNA replication, repair, p53 signaling, oxidative phosphorylation, Wnt signaling. Whole genome expression analysis of breast tumors with or without IGFBP2 expression indicated changes in genes belonging to Focal adhesion, Map kinase and Wnt signaling pathways. Interestingly, IGFBP2 knockdown clones showed reduced expression of beta-catenin compared to control cells which was restored upon IGFBP2 re-expression. The regulation of beta-catenin by IGFBP2 was found to be IGF1R and integrin pathway dependent. Furthermore, IGFBP2 and beta-catenin are co-ordinately overexpressed in breast tumors and correlate with lymph node metastasis. Conclusion: This study highlights regulation of beta-catenin by IGFBP2 in breast cancer cells and most importantly, combined expression of IGFBP2 and beta-catenin is associated with lymph node metastasis of breast tumors.

Training for Antenna Selection in Time-Varying Channels: Optimal Selection, Energy Allocation, and Energy Efficiency Evaluation

Single receive antenna selection (AS) is a popular method for obtaining diversity benefits without the additional costs of multiple radio receiver chains. Since only one antenna receives at any time, the transmitter sends a pilot multiple times to enable the receiver to estimate the channel gains of its N antennas to the transmitter and select an antenna. In time-varying channels, the channel estimates of different antennas are outdated to different extents. We analyze the symbol error probability (SEP) in time-varying channels of the N-pilot and (N+1)-pilot AS training schemes. In the former, the transmitter sends one pilot for each receive antenna. In the latter, the transmitter sends one additional pilot that helps sample the channel fading process of the selected antenna twice. We present several new results about the SEP, optimal energy allocation across pilots and data, and optimal selection rule in time-varying channels for the two schemes. We show that due to the unique nature of AS, the (N+1)-pilot scheme, despite its longer training duration, is much more energy-efficient than the conventional N-pilot scheme. An extension to a practical scenario where all data symbols of a packet are received by the same antenna is also investigated.