A low-power, low-cost soil-moisture sensor using dual-probe heat-pulse technique

This paper presents the development and testing of an integrated low-power and low-cost dual-probe heat-pulse (DPHP) soil-moisture sensor in view of the electrical power consumed and affordability in developing countries. A DPHP sensor has two probes: a heater and a temperature sensor probe spaced 3 mm apart from the heater probe. Supply voltage of 3.3V is given to the heater-coil having resistance of 33 Omega power consumption of 330 mW, which is among the lowest in this category of sensors. The heater probe is 40 mm long with 2 mm diameter and hence is stiff enough to be inserted into the soil. The parametric finite element simulation study was performed to ensure that the maximum temperature rise is between 1 degrees C and 5 degrees C for wet and dry soils, respectively. The discrepancy between the simulation and experiment is less than 3.2%. The sensor was validated with white clay and tested with red soil samples to detect volumetric water-content ranging from 0% to 30%. The sensor element is integrated with low-power electronics for amplifying the output from thermocouple sensor and TelosB mote for wireless communication. A 3.7V lithium ion battery with capacity of 1150 mAh is used to power the system. The battery is charged by a 6V and 300 mA solar cell array. Readings were taken in 30 min intervals. The life-time of DPHP sensor node is around 3.6 days. The sensor, encased in 30 mm x 20 mm x 10 mm sized box, and integrated with electronics was tested independently in two separate laboratories for validating as well as investigating the dependence of the measurement of soil-moisture on the density of the soil. The difference in the readings while repeating the experiments was found out to be less than 0.01%. Furthermore, the effect of ambient temperature on the measurement of soil-moisture is studied experimentally and computationally. (C) 2015 Elsevier B.V. All rights reserved.

Dual Drug Conjugate Loaded Nanoparticles for the Treatment of Cancer

Two antineoplastic agents, Imatinib (IM) and 5-Fluorouracil (FU) were conjugated by hydrolysable linkers through an amide bond and entrapped in polymeric Human Serum Albumin (HSA) nanoparticles. The presence of dual drugs in a common carrier has the advantage of reaching the site of action simultaneously and acting at different phases of the cell cycle to arrest the growth of cancer cells before they develop chemoresistance. The study has demonstrated an enhanced anticancer activity of the conjugate, and conjugate loaded stealth HSA nanoparticles (NPs) in comparison to the free drug in A-549 human lung carcinoma cell line and Zebra fish embryos (Danio rerio). Hydrolysability of the conjugate has also been demonstrated with complete hydrolysis being observed after 12 h. In vivo pharmacodynamics study in terms of tumor volume and pharmacokinetics in mice for conjugate (IM-SC-FU) and conjugate loaded nanoparticles showed significant anti-cancer activity. The other parameters evaluated were particle size (86nm), Poly Dispersive Index (PDI) (0.209), zeta potential (-49mV), drug entrapment efficiency (96.73%) and drug loading efficiency (89%). Being in stealth mode gives the potential for the NPs to evade Reticulo-Endothelial system (RES), achieve passive targeting by Enhanced Permeation Retention (EPR) effect with controlled release of the therapeutic agent. As the conjugate cleaves into individual drugs in the tumor environment, this promises better suppression of cancer chemoresistance by delivering dual drugs with different modes of action at the same site, thereby synergistically inhibiting the growth of cancerous tissue.

Development of the 2003 CU-HTK conversational telephone speech transcription system

This paper describes the development of the 2003 CU-HTK large vocabulary speech recognition system for Conversational Telephone Speech (CTS). The system was designed based on a multi-pass, multi-branch structure where the output of all branches is combined using system combination. A number of advanced modelling techniques such as Speaker Adaptive Training, Heteroscedastic Linear Discriminant Analysis, Minimum Phone Error estimation and specially constructed Single Pronunciation dictionaries were employed. The effectiveness of each of these techniques and their potential contribution to the result of system combination was evaluated in the framework of a state-of-the-art LVCSR system with sophisticated adaptation. The final 2003 CU-HTK CTS system constructed from some of these models is described and its performance on the DARPA/NIST 2003 Rich Transcription (RT-03) evaluation test set is discussed.

Unsupervised training for mandarin broadcast news and conversation transcription

A significant cost in obtaining acoustic training data is the generation of accurate transcriptions. For some sources close-caption data is available. This allows the use of lightly-supervised training techniques. However, for some sources and languages close-caption is not available. In these cases unsupervised training techniques must be used. This paper examines the use of unsupervised techniques for discriminative training. In unsupervised training automatic transcriptions from a recognition system are used for training. As these transcriptions may be errorful data selection may be useful. Two forms of selection are described, one to remove non-target language shows, the other to remove segments with low confidence. Experiments were carried out on a Mandarin transcriptions task. Two types of test data were considered, Broadcast News (BN) and Broadcast Conversations (BC). Results show that the gains from unsupervised discriminative training are highly dependent on the accuracy of the automatic transcriptions. © 2007 IEEE.