Power and Discrete Rate Adaptation for Energy Harvesting Wireless Nodes

Energy Harvesting (EH) nodes, which harvest energy from the environment in order to communicate over a wireless link, promise perpetual operation of a wireless network with battery-powered nodes. In this paper, we address the throughput optimization problem for a rate-adaptive EH node that chooses its rate from a set of discrete rates and adjusts its power depending on its channel gain and battery state. First, we show that the optimal throughput of an EH node is upper bounded by the throughput achievable by a node that is subject only to an average power constraint. We then propose a simple transmission scheme for an EH node that achieves an average throughput close to the upper bound. The scheme's parameters can be made to account for energy overheads such as battery non-idealities and the energy required for sensing and processing. The effect of these overheads on the average throughput is also analytically characterized.

An embedded grid adaptation strategy for unstructured data based finite volume computations

A grid adaptation strategy for unstructured data based codes, employing a combination of hexahedral and prismatic elements, generalizable to tetrahedral and pyramidal elements has been developed.

Enhancing Speedup in Network Processing Applications by Exploiting Instruction Reuse with Flow Aggregation

Instruction reuse is a microarchitectural technique that improves the execution time of a program by removing redundant computations at run-time. Although this is the job of an optimizing compiler, they do not succeed many a time due to limited knowledge of run-time data. In this paper we examine instruction reuse of integer ALU and load instructions in network processing applications. Specifically, this paper attempts to answer the following questions: (1) How much of instruction reuse is inherent in network processing applications?, (2) Can reuse be improved by reducing interference in the reuse buffer?, (3) What characteristics of network applications can be exploited to improve reuse?, and (4) What is the effect of reuse on resource contention and memory accesses? We propose an aggregation scheme that combines the high-level concept of network traffic i.e. "flows" with a low level microarchitectural feature of programs i.e. repetition of instructions and data along with an architecture that exploits temporal locality in incoming packet data to improve reuse. We find that for the benchmarks considered, 1% to 50% of instructions are reused while the speedup achieved varies between 1% and 24%. As a side effect, instruction reuse reduces memory traffic and can therefore be considered as a scheme for low power.

New Insights into Optimal Discrete Rate Adaptation for Average Power Constrained Single and Multi-Node Systems

The throughput-optimal discrete-rate adaptation policy, when nodes are subject to constraints on the average power and bit error rate, is governed by a power control parameter, for which a closed-form characterization has remained an open problem. The parameter is essential in determining the rate adaptation thresholds and the transmit rate and power at any time, and ensuring adherence to the power constraint. We derive novel insightful bounds and approximations that characterize the power control parameter and the throughput in closed-form. The results are comprehensive as they apply to the general class of Nakagami-m (m >= 1) fading channels, which includes Rayleigh fading, uncoded and coded modulation, and single and multi-node systems with selection. The results are appealing as they are provably tight in the asymptotic large average power regime, and are designed and verified to be accurate even for smaller average powers.

Does acoustic adaptation drive vertical stratification? A test in a tropical cricket assemblage

In species-rich assemblages, differential utilization of vertical space can be driven by resource availability. For animals that communicate acoustically over long distances under habitat-induced constraints, access to an effective transmission channel is a valuable resource. The acoustic adaptation hypothesis suggests that habitat acoustics imposes a selective pressure that drives the evolution of both signal structure and choice of calling sites by signalers. This predicts that species-specific signals transmit best in native habitats. In this study, we have tested the hypothesis that vertical stratification of calling heights of acoustically communicating species is driven by acoustic adaptation. This was tested in an assemblage of 12 coexisting species of crickets and katydids in a tropical wet evergreen forest. We carried out transmission experiments using natural calls at different heights from the forest floor to the canopy. We measured signal degradation using 3 different measures: total attenuation, signal-to-noise ratio (SNR), and envelope distortion. Different sets of species supported the hypothesis depending on which attribute of signal degradation was examined. The hypothesis was upheld by 5 species for attenuation and by 3 species each for SNR and envelope distortion. Only 1 species of 12 provided support for the hypothesis by all 3 measures of signal degradation. The results thus provided no overall support for acoustic adaptation as a driver of vertical stratification of coexisting cricket and katydid species.

Stabilization of stochastic approximation by step size adaptation

A scheme for stabilizing stochastic approximation iterates by adaptively scaling the step sizes is proposed and analyzed. This scheme leads to the same limiting differential equation as the original scheme and therefore has the same limiting behavior, while avoiding the difficulties associated with projection schemes. The proof technique requires only that the limiting o.d.e. descend a certain Lyapunov function outside an arbitrarily large bounded set. (C) 2012 Elsevier B.V. All rights reserved.

Adaptation of small capacity natural gas engine for producer gas operation

This article addresses the adaptation of a low-power natural gas engine for using producer gas as a fuel. The 5.9 L natural gas engine with a compression ratio of 10.5:1, rated at 55 kW shaft power, delivered 30 kW using producer gas as fuel in the naturally aspirated mode. Optimal ignition timing for peak power was found to be 20 degrees before top dead centre. Air-to-fuel ratio (A/F) was found to be 1.2 +/- 0.1 over a range of loads. Critical evaluation of the energy flows in the engine resulted in identifying losses and optimizing the engine cooling. The specific fuel consumption was found to be 1.2 +/- 0.1 kg of biomass per kilowatt hour. A reduction of 40 per cent in brake mean effective pressure was observed compared with natural gas operation. Governor response to load variations has been studied with respect to frequency recovery time. The study also attempts to adopt a turbocharger for higher power output. Preliminary results suggest a possibility of about 30 per cent increase in the output.

Low complexity adaptation for SISO channel shortening equalizers

Since the days of Digital Subscriber Links (DSL), time domain equalizers (TEQ's) have been used to combat time dispersive channels in Multicarrier Systems. In this paper, we propose computationally inexpensive techniques to recompute TEQ weights in the presence of changes in the channel, especially over fast fading channels. The techniques use no extra information except the perturbation to the channel itself, and provide excellent approximations to the new TEQ weights. Adaptation methods for two existing Channel shortening algorithms are proposed and their performance over randomly varying, randomly perturbed channels is studied. The proposed adaptation techniques are shown to perform admirably well for small changes in channels for OFDM systems. (C) 2012 Elsevier GmbH. All rights reserved.

Algal biofuel production and mitigation potential in India

Energy and energy services are the backbone of growth and development in India and is increasingly dependent upon the use of fossil based fuels that lead to greenhouse gases (GHG) emissions and related concerns. Algal biofuels are being evolved as carbon (C)-neutral alternative biofuels. Algae are photosynthetic microorganisms that convert sunlight, water and carbon dioxide (CO2) to various sugars and lipids Tri-Acyl-Glycols (TAG) and show promise as an alternative, renewable and green fuel source for India. Compared to land based oilseed crops algae have potentially higher yields (5-12 g/m(2)/d) and can use locations and water resources not suited for agriculture. Within India, there is little additional land area for algal cultivation and therefore needs to be carried out in places that are already used for agriculture, e.g. flooded paddy lands (20 Mha) with village level technologies and on saline wastelands (3 Mha). Cultivating algae under such conditions requires novel multi-tier, multi-cyclic approaches of sharing land area without causing threats to food and water security as well as demand for additional fertilizer resources by adopting multi-tier cropping (algae-paddy) in decentralized open pond systems. A large part of the algal biofuel production is possible in flooded paddy crop land before the crop reaches dense canopies, in wastewaters (40 billion litres per day), in salt affected lands and in nutrient/diversity impoverished shallow coastline fishery. Mitigation will be achieved through avoidance of GHG, C-capture options and substitution of fossil fuels. Estimates made in this paper suggest that nearly half of the current transportation petro-fuels could be produced at such locations without disruption of food security, water security or overall sustainability. This shift can also provide significant mitigation avenues. The major adaptation needs are related to socio-technical acceptance for reuse of various wastelands, wastewaters and waste-derived energy and by-products through policy and attitude change efforts.

Catalytic activity of Peptidase N is required for adaptation of Escherichia coli to nutritional downshift and high temperature stress

Peptidase N (PepN), the sole M1 family member in Escherichia coli, displays broad substrate specificity and modulates stress responses: it lowers resistance to sodium salicylate (NaSal)-induced stress but is required during nutritional downshift and high temperature (NDHT) stress. The expression of PepN does not significantly change during different growth phases in LB or NaSal-induced stress; however, PepN amounts are lower during NDHT stress. To gain mechanistic insights on the roles of catalytic activity of PepN in modulating these two stress responses, alanine mutants of PepN replacing E264 (GAMEN motif) and E298 (HEXXH motif) were generated. There are no major structural changes between purified wild type (WT) and mutant proteins, which are catalytically inactive. Importantly, growth profiles of Delta pepN upon expression of WT or mutant proteins demonstrated the importance of catalytic activity during NDHT but not NaSal-induced stress. Further fluorescamine reactivity studies demonstrated that the catalytic activity of PepN is required to generate higher intracellular amounts of free N-terminal amino acids; consequently, the lower growth of Delta pepN during NDHT stress increases with high amounts of casamino acids. Together, this study sheds insights on the expression and functional roles of the catalytic activity of PepN during adaptation to NDHT stress. (C) 2012 Elsevier GmbH. All rights reserved.

Reuse, recycle to de-bloat software

Most Java programmers would agree that Java is a language that promotes a philosophy of “create and go forth”. By design, temporary objects are meant to be created on the heap, possibly used and then abandoned to be collected by the garbage collector. Excessive generation of temporary objects is termed “object churn” and is a form of software bloat that often leads to performance and memory problems. To mitigate this problem, many compiler optimizations aim at identifying objects that may be allocated on the stack. However, most such optimizations miss large opportunities for memory reuse when dealing with objects inside loops or when dealing with container objects. In this paper, we describe a novel algorithm that detects bloat caused by the creation of temporary container and String objects within a loop. Our analysis determines which objects created within a loop can be reused. Then we describe a source-to-source transformation that efficiently reuses such objects. Empirical evaluation indicates that our solution can reduce upto 40% of temporary object allocations in large programs, resulting in a performance improvement that can be as high as a 20% reduction in the run time, specifically when a program has a high churn rate or when the program is memory intensive and needs to run the GC often.

Low complexity adaptation for channel shortening equalizers

Since the days of Digital Subscriber Links(DSL), Time Domain Equalizers(TEQ's) have been used to combat time dispersive channels in Multicarrier Systems. In this paper, we propose computationally inexpensive techniques to recompute TEQ weights in the presence of changes in the channel, especially over fast fading channels. The techniques use no extra information except the perturbation itself, and provide excellent approximations to the new TEQ weights. The proposed adaptation techniques are shown to perform admirably well for small changes in channels for OFDM systems.

An adaptation framework for head-pose classification in dynamic multi-view scenarios

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.

Joint evaluation of channel feedback schemes, rate adaptation, and scheduling in OFDMA downlinks with feedback delays

Orthogonal frequency-division multiple access (OFDMA) systems divide the available bandwidth into orthogonal subchannels and exploit multiuser diversity and frequency selectivity to achieve high spectral efficiencies. However, they require a significant amount of channel state feedback for scheduling and rate adaptation and are sensitive to feedback delays. We develop a comprehensive analysis for OFDMA system throughput in the presence of feedback delays as a function of the feedback scheme, frequency-domain scheduler, and rate adaptation rule. Also derived are expressions for the outage probability, which captures the inability of a subchannel to successfully carry data due to the feedback scheme or feedback delays. Our model encompasses the popular best-n and threshold-based feedback schemes and the greedy, proportional fair, and round-robin schedulers that cover a wide range of throughput versus fairness tradeoff. It helps quantify the different robustness of the schedulers to feedback overhead and delays. Even at low vehicular speeds, it shows that small feedback delays markedly degrade the throughput and increase the outage probability. Further, given the feedback delay, the throughput degradation depends primarily on the feedback overhead and not on the feedback scheme itself. We also show how to optimize the rate adaptation thresholds as a function of feedback delay.

Interplay Between Optimal Selection Scheme, Selection Criterion, and Discrete Rate Adaptation in Opportunistic Wireless Systems

An opportunistic, rate-adaptive system exploits multi-user diversity by selecting the best node, which has the highest channel power gain, and adapting the data rate to selected node's channel gain. Since channel knowledge is local to a node, we propose using a distributed, low-feedback timer backoff scheme to select the best node. It uses a mapping that maps the channel gain, or, in general, a real-valued metric, to a timer value. The mapping is such that timers of nodes with higher metrics expire earlier. Our goal is to maximize the system throughput when rate adaptation is discrete, as is the case in practice. To improve throughput, we use a pragmatic selection policy, in which even a node other than the best node can be selected. We derive several novel, insightful results about the optimal mapping and develop an algorithm to compute it. These results bring out the inter-relationship between the discrete rate adaptation rule, optimal mapping, and selection policy. We also extensively benchmark the performance of the optimal mapping with several timer and opportunistic multiple access schemes considered in the literature, and demonstrate that the developed scheme is effective in many regimes of interest.