Effects of periphyton grown on bamboo substrates on growth and production of Indian major carp, rohu (Label rohita Ham.)

The effects of periphyton, grown on bamboo substrates, on growth and production of Indian major carp, rohu, Labeo rohita (Hamilton), were studied in 10 ponds during July to October '95 at the Bangladesh Agricultural University, Mymensingh. Five ponds were provided with bamboo substrates (treatment I) and the rests without bamboo substrates (treatment II). It was revealed that there had been no discernible difference in the water quality parameters between treatments. A large number of plankton (30 genera) showed periphytic nature and colonized on the bamboo substrates. The growth and production of fish was significantly (p<0.05) higher in the ponds with bamboo substrates as compared to the ponds without substrates. The net production of rohu in treatment I was about 1.7 times higher than that of treatment II. Fish production was as much as 1899 kg/ha over a culture period of 4 months in the periphyton-based production system.

A single all-optical processor for multiple spectral amplitude code label recognition using four wave mixing

We propose a novel label processor which can recognize multiple spectral-amplitude-code labels using four-wave-mixing sidebands and selective optical filtering. Ten code-labels x 10 Gbps variable-length packets are transmitted over a 200 km single-hop switched network.

Joint modelling of voicing label and continuous F0 for HMM based speech synthesis

Fundamental frequency, or F0 is critical for high quality speech synthesis in HMM based speech synthesis. Traditionally, F0 values are considered to depend on a binary voicing decision such that they are continuous in voiced regions and undefined in unvoiced regions. Multi-space distribution HMM (MSDHMM) has been used for modelling the discontinuous F0. Recently, a continuous F0 modelling framework has been proposed and shown to be effective, where continuous F0 observations are assumed to always exist and voicing labels are explicitly modelled by an independent stream. In this paper, a refined continuous F0 modelling approach is proposed. Here, F0 values are assumed to be dependent on voicing labels and both are jointly modelled in a single stream. Due to the enforced dependency, the new method can effectively reduce the voicing classification error. Subjective listening tests also demonstrate that the new approach can yield significant improvements on the naturalness of the synthesised speech. A dynamic random unvoiced F0 generation method is also investigated. Experiments show that it has significant effect on the quality of synthesised speech. © 2011 IEEE.

Carbon nanostructure-based field-effect transistors for label-free chemical/biological sensors.

Over the past decade, electrical detection of chemical and biological species using novel nanostructure-based devices has attracted significant attention for chemical, genomics, biomedical diagnostics, and drug discovery applications. The use of nanostructured devices in chemical/biological sensors in place of conventional sensing technologies has advantages of high sensitivity, low decreased energy consumption and potentially highly miniaturized integration. Owing to their particular structure, excellent electrical properties and high chemical stability, carbon nanotube and graphene based electrical devices have been widely developed for high performance label-free chemical/biological sensors. Here, we review the latest developments of carbon nanostructure-based transistor sensors in ultrasensitive detection of chemical/biological entities, such as poisonous gases, nucleic acids, proteins and cells.

Multitask learning without label correspondences

We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available. This is in contrast with existing methods which either assume that the label sets shared by different tasks are the same or that there exists a label mapping oracle. Our method directly maximizes the mutual information among the labels, and we show that the resulting objective function can be efficiently optimized using existing algorithms. Our proposed approach has a direct application for data integration with different label spaces, such as integrating Yahoo! and DMOZ web directories.

Estimating labels from label proportions

Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, also with known label proportions. This problem appears in areas like e-commerce, spam filtering and improper content detection. We present consistent estimators which can reconstruct the correct labels with high probability in a uniform convergence sense. Experiments show that our method works well in practice. Copyright 2008 by the author(s)/owner(s).

Label-free detection of human prostate-specific antigen (hPSA) using film bulk acoustic resonators (FBARs)

Label-free detection of cancer biomarkers using low cost biosensors has promising applications in clinical diagnostics. In this work, ZnO-based thin film bulk acoustic wave resonators (FBARs) with resonant frequency of ∼1.5 GHz and mass sensitivity of 0.015 mg/m2 (1.5 ng/cm2) have been fabricated for their deployment as biosensors. Mouse monoclonal antibody, anti-human prostate-specific antigen (Anti-hPSA) has been used to bind human prostate-specific antigen (hPSA), a model cancer used in this study. Ellipsometry was used to characterize and optimise the antibody adsorption and antigen binding on gold surface. It was found that the best amount of antibody at the gold surface for effective antigen binding is around 1 mg/m2, above or below which resulted in the reduced antigen binding due to either the limited binding sites (below 1 mg/m2) or increased steric effect (above 1 mg/m2). The FBAR data were in good agreement with the data obtained from ellipsometry. Antigen binding experiments using FBAR sensors demonstrated that FBARs have the capability to precisely detect antigen binding, thereby making FBARs an attractive low cost alternative to existing cancer diagnostic sensors. © 2013 Elsevier B.V.

Fast low-cost FIR filter processed ECDM labels for optical label switching

A code-label recognition time of less than 500ps is demonstrated using low-cost FIRfilters. The electronically-processed label provides a control signal from an auto-correlated label. Error-free electronic code-label switching of an optical 10Gb/s signal is demonstrated. © 2010 Optical Society of America.

A novel chemiluminescent immunoassay for microcystin (MC) detection based on gold nanoparticles label and its application to MC analysis in aquatic environmental samples

A novel chemiluminescent immunoassay method based on gold nanoparticles was developed for the detection of microcystins (MCs). The immunoassay included three main steps: indirect competitive immunoreaction, oxidative dissolution of gold nanoparticles, and indirect determination for MCs with Au3+-catalysed luminol chemiluminesent system. The method has a wide working range (0.05-10 mu g L-1, r(2) = 0.9914), the limit of detection was determined to be 0.024 mu g L-1, which is much lower than the World Health Organization's proposed guidelines (1 mu g L-1) for drinking-water. The proposed method was applied to MC analysis in natural water and fish tissue samples, and most results in the proposed method were in agreement with the conventional indirect competitive enzyme-linked immunosorbent assay method, which indicated that the new chemiluminescent immunoassay was sensitive, reliable, and suitable for MC analysis in natural water and fish tissue samples.

高级微机ISA总线上的多功能接口板

介绍了一个应用于 Pentium机型上 ISA总线的接口电路 ,这个接口电路具有高速度、高可靠性的特点 ,是为光谱测量与能级寿命测量装置的控制及数据获取系统专门设计研制的。

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.