Chromium accumulation and distribution in crop plants

Crop plants accumulated chromium from nutrient solutions labelled with either ⁵¹CrIII or ⁵¹CrVI and retained approximately 98 % of the elements in the roots. Of the 9 crop species examined, cauliflower plants accumulated the most chromium in both shoots and roots and mung bean seedlings the least. Species differences of approximately 10-fold in the tops and 5-fold in the roots were recorded. The roots of each species supplied with ⁵¹CrIII contained more chromium than those supplied with ⁵¹CrVI but the reverse was found in the shoots. There was a greater proportion of soluble chromium in plants supplied with ⁵¹CrVI than in those supplied with ⁵¹CrIII, the major soluble compound being tentatively identified as trioxalato-CrIII. Little chromium was associated with cell organdies or soluble proteins.     

Soft Reconstruction of Speech in the Presence of Noise and Packet Loss

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth efficient way to combat the noisy channel degradations. In this paper, we consider soft reconstruction of speech spectrum, in GSM adaptive multirate and IS-641 vocoders, transmitted over a channel disturbed with noise and/or packet loss. Several schemes are presented which exploit different levels of intraframe and interframe residual redundancy for improved source decoding at the receiver. A packetization strategy is proposed which is matched to the presented error concealment units. For decoders that exploit the residual redundancy, extensive complexity has been a serious concern, especially as the quantizer bitrate increases . In this paper, a novel method is presented to construct reduced complexity algorithms. The proposed methodology is based on the classification of the signal domain and efficient approximation of the residual redundancy or the a priori transition probabilities. The presented schemes provide high quality error concealment solutions for code excited linear prediction (CELP) coders     

Accounting for randomness in measurement and sampling in studying cancer cell population dynamics

Knowing the expected temporal evolution of the proportion of different cell types in sample tissues gives an indication about the progression of the disease and its possible response to drugs. Such systems have been modelled using Markov processes. We here consider an experimentally realistic scenario in which transition probabilities are estimated from noisy cell population size measurements. Using aggregated data of FACS measurements, we develop MMSE and ML estimators and formulate two problems to find the minimum number of required samples and measurements to guarantee the accuracy of predicted population sizes. Our numerical results show that the convergence mechanism of transition probabilities and steady states differ widely from the real values if one uses the standard deterministic approach for noisy measurements. This provides support for our argument that for the analysis of FACS data one should consider the observed state as a random variable. The second problem we address is about the consequences of estimating the probability of a cell being in a particular state from measurements of small population of cells. We show how the uncertainty arising from small sample sizes can be captured by a distribution for the state probability.Inspec keywords: cancer, tumours, cellular biophysics, biomedical measurement, Gaussian distribution, maximum likelihood estimation, mean square error methods, hidden Markov models, fluorescence, random processes, convergence of numerical methodsOther keywords: cancer cell population dynamics, malignant tumours, tissue samples, normal tissue cells, disease, drugs, Markov process, cell population size measurement, hidden Markov model, noisy measurement, state transition probability, fluorescence‐activated cell sorting measurement, minimum mean square error estimator, maximum likelihood estimator, transition probability matrix, noise distributions, Gaussian distributions, MMSE, convergence mechanism, standard deterministic approach, stochastic phenomena, random variable     

Lifetime maximized data gathering in wireless sensor networks using limited-order distributed source coding

This paper addresses the problem of efficient data gathering in wireless sensor networks with a complexity constrained data gathering node. Due to the complexity constraint, the data gathering node employs an asymmetric DSC that (de)compresses the data of a given node exploiting its dependency with a limited number of other nodes. This is characterized in a DSC rate allocation structure that is referred to as limited-order DSC. Within this structure, we investigate the problem of rate allocation for the nodes to maximize the network lifetime. To this end, an algorithm is proposed that is proven optimal with polynomial complexity in terms of number of network nodes. Numerical results demonstrate that the algorithm, even with limited complexity, allows for exploiting most of the achievable compression gain.     

Reconstruction of predictively encoded signals over noisy channels using a sequence MMSE decoder

In this paper, we consider the problem of decoding predictively encoded signal over a noisy channel when there is residual redundancy (captured by a /spl gamma/-order Markov model) in the sequence of transmitted data. Our objective is to minimize the mean-squared error (MSE) in the reconstruction of the original signal (input to the predictive source coder). The problem is formulated and solved through minimum mean-squared error (MMSE) decoding of a sequence of samples over a memoryless noisy channel. The related previous works include several maximum a posteriori (MAP) and MMSE-based decoders. The MAP-based approaches are suboptimal when the performance criterion is the MSE. On the other hand, the previously known MMSE-based approaches are suboptimal, since they are designed to efficiently reconstruct the data samples received (the prediction residues) rather than the original signal. The proposed scheme is set up by modeling the source-coder-produced symbols and their redundancy with a trellis structure. Methods are presented to optimize the solutions in terms of complexity. Numerical results and comparisons are provided, which demonstrate the effectiveness of the proposed techniques.     

Channel Feedback Quantization for High Data Rate MIMO Systems

In this work, we study a multiple-input multiple-output (MIMO) wireless system where the channel state information is partially available at the transmitter through a feedback link. Based on singular value decomposition, the MIMO channel is split into independent sub-channels. Effective feedback of the required spatial channel information entails efficient quantization/encoding of a unitary matrix. We propose two schemes for quantizing unitary matrices via Givens rotations and examine the performance for a scenario where the rates allocated to the sub-channels are selected according to their corresponding gains. Numerical results show that the proposed schemes offer a significant performance improvement as compared to that of MIMO systems without feedback, with a negligible increase in the complexity     

Efficient source decoding over memoryless noisy channels using higher order Markov models

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth-efficient way to combat noisy channel degradations. This redundancy can be employed to either assist the channel decoder for improved performance or design better source decoders. In this work, a family of solutions for the asymptotically optimum minimum mean-squared error (MMSE) reconstruction of a source over memoryless noisy channels is presented when the redundancy in the source encoder output stream is exploited in the form of a /spl gamma/-order Markov model (/spl gamma//spl ges/1) and a delay of /spl delta/,/spl delta/>0, is allowed in the decoding process. It is demonstrated that the proposed solutions provide a wealth of tradeoffs between computational complexity and the memory requirements. A simplified MMSE decoder which is optimized to minimize the computational complexity is also presented. Considering the same problem setup, several other maximum a posteriori probability (MAP) symbol and sequence decoders are presented as well. Numerical results are presented which demonstrate the efficiency of the proposed algorithms.     

A flexible rate slepian-wolf code construction

A flexible rate Slepian-Wolf (SW) code is constructed, which is vital for wireless sensor network applications. The proposed solution is based on an efficient and practical algorithm to compute the syndrome of the rate-compatible convolutional codes (RCPC). Using this algorithm, there is no need to compute the syndrome of punctured version of the mother code for each puncturing matrix, which is complex. Instead, the syndrome of the punctured code is the punctured version of the syndrome of the mother code using the same pattern of puncturing. The algorithm is general for all convolutional codes in Zq. The strategy is also generalized for parallel and serial concatenated convolutional codes. For the cases, where the dependencies among sources are modeled as a virtual discrete channel, a simplified decoding scheme is suggested. This method is generalized to achieve all points on the SW boundary using a simple code design technique. Simulation results demonstrate the performance and effectiveness of the proposed methods.     

Single and double frame coding of speech LPC parameters using a lattice-based quantization scheme

A lattice-based scheme for the single-frame and the double-frame quantization of the speech line spectral frequency parameters is proposed. The lattice structure provides a low-complexity vector quantization framework, which is implemented using a trellis structure. In the single-frame scheme, the intraframe dependencies are exploited using a linear predictor. In the double-frame scheme, the parameters of two consecutive frames are jointly quantized and hence the interframe dependencies are also exploited. A switched scheme is also considered in which, lattice-based double-frame and single-frame quantization is performed for each two frame and the one which results in a lower distortion is chosen. Comparisons to the Split-VQ, the Multi-Stage VQ, the Trellis Coded Quantization, the interframe Block-Based Trellis Quantizer, and the interframe scheme used in IS-641 EFRC and the GSM AMR codec are provided. These results demonstrate the effectiveness of the proposed lattice-based quantization schemes, while maintaining a very low complexity. Finally, the issue of the robustness to channel errors is investigated.     

Structural and mechanical properties of ZrN films prepared by ion beam sputtering with varying N2/Ar ratio and substrate temperature

Zirconium nitride (ZrN) films were deposited by ion beam sputtering technique on stainless steel 304 substrates using a mix of (Ar+N₂) gas. In this paper, the effects of N₂/(N₂+Ar) flow ratio (F(N₂)) and substrate temperature on the microstructure and microscopic properties of the deposited films were investigated. The phase and the morphology were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) respectively; moreover, the composition depth profile of ZrN was obtained using secondary ion mass spectroscopy (SIMS). In a wide range of F(N₂) (10-54%), the intensity of (1 1 1) peak increased which was the preferred orientation, while for F(N₂) more than 54% the ZrN peak intensity was decreased and the amorphous structure was formed at 95%. The XRD patterns presented a texture change due to the processing temperature, which was varied within the range 200-550 °C. At 400 °C, the (1 1 1) crystalline plane intensity was higher than the other ones, leading to the presence of a preference for this orientation. Good planarity of the deposited films was confirmed by SEM, it did not reveal any undulations, fractures, or cracking. The Vickers micro-hardness tester with a load of 25 g was used to measure the hardness of the films. The results showed that the structural and mechanical properties were strongly influenced by nitrogen ratio and substrate temperature.