A novel frequency-offset monitoring technique for direct-detection DPSK systems

We propose and demonstrate a novel technique to monitor the frequency offset between the optical source and delay interferometer (DI) for direct-detection differential phase-shift-keying (DPSK) systems. In this scheme, a phase-modulated tone is applied to DPSK signals at the transmitter and then detected after being converted into an amplitude-modulated tone at the DI to be used for the monitoring signal. Our experimental demonstration shows that the monitoring range and sensitivity of the proposed scheme are measured to be /spl plusmn/2 GHz and /spl sim/10 MHz, respectively, which we believe are good enough to be used either to generate alarm signals for the frequency offset monitoring or to control the feedback loop of the DI.     

Comments on “Quadratic stabilization of continuous timesystems with state-delay and norm-bounded time-varyinguncertainties”

In the above mentioned paper by Mahmoud-Muthairi (ibid. vol.39 (1994)) a sufficient condition for memoryless stabilization of a class of uncertain linear systems with a variable-state delay and norm-bounded time-varying uncertainties is derived in terms of an algebraic Riccati equation. This Riccati equation depends on several free matrix variables, and a subsequent result in the paper, Theorem 2, states that failure or success of the stabilization algorithm is independent of the selection of these matrix variables. In this paper, we give a counterexample to this Theorem 2 as well as providing a fix     

Parallel finding all initial palindromes and periods of a string on reconfigurable meshes

Given a string of lengthn, this short paper first presents anO(1)-time parallel algorithm for finding all initial palindromes and periods of the string on ann×n reconfigurable mesh (RM). Then, under the same cost (= time × the number of processors =O(n ²)), we provide a partitionable strategy when the RM doesn’t offer sufficient processors; this overcomes the hardware limitation and is very suitable for VLSI implementation. Prof. Chung was supported in part by the National Science Council of R. O. C. under contracts NSC87-2213-E011-001 and NSC87-2213-E011-003.     

Application of multi-layer perceptron neural networks to vision problems

This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.     

A game theoretic fault detection filter

The fault detection process is approximated with a disturbance attenuation problem. The solution to this problem, for both linear time-varying and time-invariant systems, leads to a game theoretic filter which bounds the transmission of all exogenous signals except the fault to be detected. In the limit, when the disturbance attenuation bound is brought to zero, a complete transmission block is achieved by embedding the nuisance inputs into an unobservable, invariant subspace. Since this is the same invariant subspace structure seen in some types of detection filters, we can claim that the asymptotic game filter is itself a detection filter. One can also make use of this subspace structure to reduce the order of the limiting game theoretic filter by factoring this invariant subspace out of the state space. The resulting lower dimensional filter will then be sensitive only to the failure to be detected. A pair of examples given at the end of the paper demonstrate the effectiveness of the filter for time-invariant and time-varying problems in both full-order and reduced-order forms     

An evolutionary approach to pattern-based time series segmentation

Time series data, due to their numerical and continuous nature, are difficult to process, analyze, and mine. However, these tasks become easier when the data can be transformed into meaningful symbols. Most recent works on time series only address how to identify a given pattern from a time series and do not consider the problem of identifying a suitable set of time points for segmenting the time series in accordance with a given set of pattern templates (e.g., a set of technical patterns for stock analysis). However, the use of fixed-length segmentation is an oversimplified approach to this problem; hence, a dynamic approach (with high controllability) is preferable so that the time series can be segmented flexibly and effectively according to the needs of the users and the applications. In view of the fact that this segmentation problem is an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary time series segmentation algorithm. This approach allows a sizeable set of pattern templates to be generated for mining or query. In addition, defining similarity between time series (or time series segments) is of fundamental importance in fitness computation. By identifying the perceptually important points directly from the time domain, time series segments and templates of different lengths can be compared and intuitive pattern matching can be carried out in an effective and efficient manner. Encouraging experimental results are reported from tests that segment both artificial time series generated from the combinations of pattern templates and the time series of selected Hong Kong stocks.     

Decreased Expression of Plakophilin-2 and αT-Catenin in Arrhythmogenic Right Ventricular Cardiomyopathy: Potential Markers for Diagnosis

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a hereditary disease of the heart muscle. Clinical challenges remain, however, in identifying patients with ARVC in the early or concealed stages with subtle clinical manifestations. Therefore, we wanted to identify potential targets by immunohistochemical (IHC) analysis in comparison with controls. Pathogenic mutations were identified in 11 of 37 autopsied patients with ARVC. As observed from IHC analysis of the RV, expression of αT-catenin and plakophilin-2 is significantly decreased in autopsied patients with ARVC as compared to controls, and the decreased expression is consistent in patients with and without pathogenic mutations. Furthermore, ARVC specimens demonstrated a reduced localization of αT-catenin, desmocollin-2, desmoglein-2, desmoplakin, and plakophilin-2 on intercalated discs. These findings have been validated by comparing RV specimens obtained via endomyocardial biopsy between patients with ARVC and those without. The pathogenic mutation was present in 3 of 5 clinical patients with ARVC. In HL-1 myocytes, siRNA was used to knockdown CTNNA3, and western blotting analysis demonstrated that the decline in αT-catenin expression was accompanied by a significant decline in the expression of plakophilin-2. The aforementioned effect was directed towards protein degradation rather than mRNA stability. Plakophilin-2 expression decreases concurrently with the decline in CTNNA3 expression. Therefore, the expression of αT-catenin and plakophilin-2 could be potential surrogates for the diagnosis of ARVC.     

MicroRNAs as Potential Biomarkers in the Differential Diagnosis of Lipomatous Tumors and Their Mimics

Adipocytic tumors are the most common subtype of soft tissue tumors. In current clinical practice, distinguishing benign lipomas from well-differentiated liposarcomas (WDLPS), as well as dedifferentiated liposarcomas (DDLPS) from their morphologic mimics, remains a significant diagnostic challenge. This is especially so when examining small biopsy samples and without the aid of additional ancillary tests. Recognizing the important role that microRNAs (miRNAs) play in tumorigenesis and their potential utility in tumor classification, we analyzed routine clinical tissue samples of benign and malignant lipomatous tumors, as well as other sarcoma mimics, to identify distinguishing miRNA-based signatures that can aid in the differential diagnosis of these entities. We discovered a 6-miRNA signature that separated lipomas from WDLPS with high confidence (AUC of 0.963), as well as a separate 6-miRNA signature that distinguished DDLPS from their more aggressive histologic mimics (AUC of 0.740). Functional enrichment analysis unveiled possible mechanistic involvement of these predictive miRNAs in adipocytic cancer-related biological processes and pathways such as PI3K/AKT/mTOR and MAPK signaling, further supporting the relevance of these miRNAs as biomarkers for adipocytic tumors. Our results demonstrate that miRNA expression profiling may potentially be used as an adjunctive tool for the diagnosis of benign and malignant adipocytic tumors. Further validation studies are warranted.     

The pH effect for rare earth metals Nd, Pr and Y in aqueous solution dissolved Mg, Zn and Al

The pH variations of Mg, Zn and Al solutions to which had been added the rare earth metals Nd, Pr and Y were observed in 3.5 wt.% NaCl with respect to high energy mechanical ball milling effects. Mg was directly dissolved and exhibited a pH value of 10.5. On the other hand, Zn and Al needed to be saturated for a certain amount of time. The addition of rare earth metals played a role in increasing the pH with low reduction potentials. Additionally, mechanical ball milling provided high energy to Mg + x wt.% Zn + 0.5 wt.% Nd mixture by fracturing fragmentation of metals, which led to an increase in the pH when the mixture was immersed in 3.5 wt.% NaCl. The addition of Zn to Mg + 0.5 wt.% Nd caused a higher pH than when Mg + 0.5 wt.% Nd alone was added.