MODLEACH: A Variant of LEACH for WSNs

Wireless sensor networks are appearing as an emerging need for mankind. Though, Such networks are still in research phase however, they have high potential to be applied in almost every field of life. Lots of research is done and a lot more is awaiting to be standardized. In this work, cluster based routing in wireless sensor networks is studied precisely. Further, we modify one of the most prominent wireless sensor network's routing protocol "LEACH" as modified LEACH (MODLEACH) by introducing \emph{efficient cluster head replacement scheme} and \emph{dual transmitting power levels}. Our modified LEACH, in comparison with LEACH out performs it using metrics of cluster head formation, through put and network life. Afterwards, hard and soft thresholds are implemented on modified LEACH (MODLEACH) that boast the performance even more. Finally a brief performance analysis of LEACH, Modified LEACH (MODLEACH), MODLEACH with hard threshold (MODLEACHHT) and MODLEACH with soft threshold (MODLEACHST) is undertaken considering metrics of throughput, network life and cluster head replacements.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

A content-aware quantisation mechanism for transform domain distributed video coding

The discrete cosine transform (DCT) is widely applied in modern codecs to remove spatial redundancies, with the resulting DCT coefficients being quantised to achieve compression as well as bit-rate control. In distributed video coding (DVC) architectures like DISCOVER, DCT coefficient quantisation is traditionally performed using predetermined quantisation matrices (QM), which means the compression is heavily dependent on the sequence being coded. This makes bit-rate control challenging, with the situation exacerbated in the coding of high resolution sequences due to QM scarcity and the non-uniform bit-rate gaps between them. This paper introduces a novel content-aware quantisation (CAQ) mechanism to overcome the limitations of existing quantisation methods in transform domain DVC. CAQ creates a frame-specific QM to reduce quantisation errors by analysing the distribution of DCT coefficients. In contrast to the predetermined QM that is applicable to only 4x4 block sizes, CAQ produces QM for larger block sizes to enhance compression at higher resolutions. This provides superior bit-rate control and better output quality by seeking to fully exploit the available bandwidth, which is especially beneficial in bandwidth constrained scenarios. In addition, CAQ generates superior perceptual results by innovatively applying different weightings to the DCT coefficients to reflect the human visual system. Experimental results corroborate that CAQ both quantitatively and qualitatively provides enhanced output quality in bandwidth limited scenarios, by consistently utilising over 90% of available bandwidth

Magnetohydrodynamic Viscous Flow Over a Shrinking Sheet With Second Order Slip Flow Model

In this paper, we investigate the magnetohydrodynamic viscous flow with second order slip flow model over a permeable shrinking surface. We have obtained the closed form of exact solution of Navier-Stokes equations by using similarity variable technique. The effects of slip, suction and magnetic parameter have been investigated in detail. The results show that there are two solution branches, namely lower and upper solution branch. The behavior of velocity and shear stress profiles for different values of slip, suction and magnetic parameters has been discussed through graphs.Comment: 13 Pages, 8 Figures. Accepted for Publication in Heat Transfer Researc

Agricultural Growth in China and Sub-Saharan African Countries

Agriculture remains a dominant sector in the economies of most African and several Asian countries. However, the poor performance of agriculture in Africa stands in sharp contrast to the robust agricultural growth in many Asian countries.2 In this regard, the experience of China is perhaps as impressive as it is relevant to many countries in Sub-Saharan Africa. A general observation is that the productivity of land and labour has to rise through intensive agriculture, given the limited area of arable land (in China and Africa) and the high rates of growth of population (as in Africa). In many African countries, labour productivity has fallen and land productivity has not increased significantly. In China, productivities of both land and labour have increased significantly since at least the early 1980s. Agricultural output can increase in three ways: (i) get more from the same quantities of inputs through better utilisation of the existing capacity; (ii) use increased quantities of inputs; and (iii) use new techniques to raise the productivity of each input or raise the total product curve. All of these may require changes in tenurial arrangements, levels of investment in infrastructure and support services, and policies that affect the prices of outputs and inputs. A close examination of factors underlying the contrasting experiences in China and African countries reveals important differences in the institutional and policy environments affecting the individual behaviour with regard to the adoption and use of new (profitable) technologies to raise the land and labour productivities.

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning

In this paper we introduce the idea of improving the performance of parametric temporal-difference (TD) learning algorithms by selectively emphasizing or de-emphasizing their updates on different time steps. In particular, we show that varying the emphasis of linear TD($\lambda$)'s updates in a particular way causes its expected update to become stable under off-policy training. The only prior model-free TD methods to achieve this with per-step computation linear in the number of function approximation parameters are the gradient-TD family of methods including TDC, GTD($\lambda$), and GQ($\lambda$). Compared to these methods, our _emphatic TD($\lambda$)_ is simpler and easier to use; it has only one learned parameter vector and one step-size parameter. Our treatment includes general state-dependent discounting and bootstrapping functions, and a way of specifying varying degrees of interest in accurately valuing different states.Comment: 29 pages This is a significant revision based on the first set of reviews. The most important change was to signal early that the main result is about stability, not convergenc