System Identification With Sparse Coprime Sensing

Given a continuous time LTI system with impulse response h_c(t), it is shown that the uniformly spaced samples h_c(nT) can be identified for any chosen spacing by using an impulse train input with an arbitrarily small rate 1/NT and sampling the system output with an arbitrarily small rate 1/MT, provided M and N are coprime. This idea, referred to here as the sparse coprime sensing method for system identification, is closely related to well known results in multirate signal processing. It is shown that the problem can be related to the identification of a decimation filter from input-output measurements. It is also shown that the problem is equivalent to the identification of a discrete time N x M LTI system from a knowledge of the full rate input and output vector sequences

Provider payments and patient charges as policy tools for cost-containment: How successful are they in high-income countries?

In this paper, we focus on those policy instruments with monetary incentives that are used to contain public health expenditure in high-income countries. First, a schematic view of the main cost-containment methods and the variables in the health system they intend to influence is presented. Two types of instruments to control the level and growth of public health expenditure are considered: (i) provider payment methods that influence the price and quantity of health care, and (ii) cost-containment measures that influence the behaviour of patients. Belonging to the first type of instruments, we have: fee-for-service, per diem payment, case payment, capitation, salaries and budgets. The second type of instruments consists of patient charges and reference price systems for pharmaceuticals. Secondly, we provide an overview of experience in high-income countries that use or have used these particular instruments. Finally, the paper assesses the overall potential of these instruments in cost-containment policies

MIMO radar with broadband waveforms: Smearing filter banks and 2D virtual arrays

In this paper MIMO radars with broadband waveforms are considered. A time domain viewpoint is taken, which allows frequency invariant beamforming with a filter bank called the smearing filter bank. Motivated by recent work on two dimensional arrays to obtain frequency invariant one dimensional beams, the generation of two dimensional virtual arrays from one dimensional ULAs is also considered. It is also argued that when the smearing filter bank is appropriately used, frequency invariant 2D beams can be generated

Thinned coprime arrays for DOA estimation

Sparse arrays can generate a larger aperture than traditional uniform linear arrays (ULA) and offer enhanced degrees-of-freedom (DOFs) which can be exploited in both beamforming and direction-of-arrival (DOA) estimation. One class of sparse arrays is the coprime array, composed of two uniform linear subarrays which yield an effective difference co-array with higher number of DOFs. In this work, we present a new coprime array structure termed thinned coprime array (TCA), which exploits the redundancy in the structure of the existing coprime array and achieves the same virtual aperture and DOFs as the conventional coprime array with much fewer number of sensors. An analysis of the DOFs provided by the new structure in comparison with other sparse arrays is provided and simulation results for DOA estimation using the compressive sensing based method are provided

Frequency invariant MVDR beamforming without filters and implementation using MIMO radar

Frequency invariant beamforming with sensor arrays is generally achieved using filters in the form of tapped delay-lines following each sensor. However it has been recently shown that with the help of the rectangular smart antenna array, it is possible to generate frequency invariant beampattern without using filters. In this paper, this frequency invariant beamforming technique is utilized to perform MVDR beamforming in the beamspace by designing frequency invariant beams spanning the desired range of azimuthal angles and optimally combining them. However, the performance of the frequency invariant beamformer depends on the number of sensors which could be large for a rectangular array of size M × N. Making use of the virtual array concept used in MIMO radar, a novel method of producing the same frequency invariant beam, using only M transmitting and N receiving antennas, is proposed and a design example is provided to demonstrate the idea

Effect of climate variables on yield of major food-crops in Nepal -A time-series analysis-

Climate change influences crop yield vis-à-vis crop production to a greater extent in countries like Nepal where agriculture depends largely on natural circumstances. Plausible scenarios of climate change like higher temperatures and changes in precipitation will directly affect crop yields. Therefore, this study assesses the effect of observed climate variables on yield of major food-crops in Nepal, namely rice, wheat, maize, millet, barley and potato based on regression model for historical (1978-2008) climatic data and yield data for the food-crops. The yield growth rate of all the food-crops is positive. However, the growth rate for all crops, except potato and wheat, is below population growth rate during the period. Climate variables like temperature and precipitation are the important determinants of crop yields. Trend of precipitation is neither increasing nor decreasing significantly during this period. However, temperature is increasing by 0.7 0C during the period. Climate variables show some influences on the yield of these major food-crops in Nepal. Increase in summer rain and maximum temperature has contributed positively to rice yield. Also, increase in summer rain and minimum temperature has positive impact on potato yield. However, increase in summer rain and maximum temperature adversely affected the yield of maize and millet. Increase in wheat and barley yield is contributed by current trend of winter rain and temperature. Consideration of spatial variation in similar type of study in Nepal that will be helpful in identifying the region more vulnerable to climate change in terms of crop yield is highly recommended.Climate variables; temperature; rainfall; food-crops; Nepal

Sampling Requirements for Stable Autoregressive Estimation

We consider the problem of estimating the parameters of a linear univariate autoregressive model with sub-Gaussian innovations from a limited sequence of consecutive observations. Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime. In particular, we show that for a fixed sparsity level, stable recovery of AR parameters is possible when the number of samples scale sub-linearly with the AR order. Our results improve over existing sampling complexity requirements in AR estimation using the LASSO, when the sparsity level scales faster than the square root of the model order. We further derive sufficient conditions on the sparsity level that guarantee the minimax optimality of the $\ell_1$-regularized least squares estimate. Applying these techniques to simulated data as well as real-world datasets from crude oil prices and traffic speed data confirm our predicted theoretical performance gains in terms of estimation accuracy and model selection

Effects of Content and Audience Awareness Goals for Revision on EFL Learners' Writing Performance

The process of revision and its essential role in the writing process is universally known. However, students have continuous problem in writing and especially in revising. Practitioners believe that the main problem is lack of instructional attention on revision strategies focused on content and audience awareness in classrooms, and students’ not setting any clear goals to follow while writing and revising although they are both described as goal‐directed process in cognitive model of composition writing. The purpose of this study was to figure out the effects of revising goals focused on content and audience awareness on aspects of essay writing (i.e., Task Response (TR), Cohesion and Coherence (CC), Lexical Resource (LR), and Grammar Range and Accuracy (GRA)), and overall writing performance of EFL learners. Based on their writing performance in the pretest, all 26 students were systematically assigned to two different goal conditions: a General Goal (GG, that was to improve the essay in general); and a Content plus Audience Awareness Goal (C*AG, that was to improve the essay focusing on content and organization of ideas, and communication with the intended audience). After six treatment sessions (within six weeks), they were given a writing test as the posttest. Final drafts of essays in both pretest and posttest were scored for aspects of essay writing and for overall writing performance. As a result of the treatment, students in C*AG condition managed to write essays with significantly higher quality compared to students in GG condition (t = 2.137, p = .043). Moreover, those in C*AG condition improved their essays significantly in all aspects of essay writing (t = 6.503, 5.894, 6.936, 5.744, and p = .000 for TR, CC, LR, and GRA respectively) while students’ essays in GG condition scored higher in terms of TR (t = 2.930, p = .013) and GRA (t = 2.713, p = .019) only. Based on the results, it is recommended for English writing teachers to consider revision strategies that focus on content and audience awareness in teaching writing and revising since such strategies can lead students to better performance in all aspects of essay writing