1,187 research outputs found
An Adaptable Energy-Efficient Medium Access Control Protocol for Wireless Sensor Networks
Wireless networks have become ubiquitous recently and therefore their usefulness has also become more extensive. Wireless sensor networks (WSN) detect environmental information with sensors in remote settings. One problem facing WSNs is the inability to resupply power to these energy-constrained devices due to their remoteness. Therefore to extend a WSN\u27s effectiveness, the lifetime of the network must be increased by making them as energy efficient as possible. An energy efficient medium access control (MAC) can boost a WSN\u27s lifetime. This research creates a MAC protocol called Adaptive sensor Medium Access Control (AMAC) which is based on Sensor Medium Access Control (SMAC) which saves energy by periodically sleeping and not receiving. AMAC adapts to traffic conditions by incorporating multiple duty cycles. Under a high traffic load, AMAC has a short duty cycle and wakes up often. Under a low traffic load, AMAC has a longer duty cycle and wakes up infrequently. The AMAC protocol is simulated in OPNET Modeler using various topologies. AMAC uses 15% less power and 22% less energy per byte than SMAC but doubles the latency. AMAC is promising and further research can decrease its latency and increase its energy efficiency
Measurement of Radio-Frequency Radiation Pressure
We perform measurements of the radiation pressure of a radio-frequency (RF)
electromagnetic field which may lead to a new SI-traceable power calibration.
There are several groups around the world investigating methods to perform more
direct SI traceable measurement of RF power (where RF is defined to range from
100s of MHz to THz). A measurement of radiation pressure offers the possibility
for a power measure traceable to the kilogram and to Planck's constant through
the redefined SI. Towards this goal, we demonstrate the ability to measure the
radiation pressure/force carried in a field at 15~GHz.Comment: 2 pages 4 figure
Method Emergence in Practice - Influences and Consequences
This paper explores the relationship between what influences and shapes the unique and locally situated method-in-action and how it consequently emerges. Based on a synthesis of prominent Information Systems (Development) literature, an analytical framework is developed. The framework is organised into three perspectives: 1) the structuralist, 2) the individualist and 3) the interactive process perspective. Each perspective supplies a set of key concepts for conceptual understanding and empirical exploration. The analytical framework is used to structure and analyse a two-year longitudinal case study of method emergence in a web-based ISD project. The paper concludes with a summary of the research and its implications. We propose that this research and future theoretical and empirical contributions that address the relationship between the whats and hows of method emergence will support and improve ISD researchersâ and practitionersâ ability to pay attention to and act in accordance with the myriad characteristics, actors and events that shape the method-inaction in practice. Such contributions we argue will build up a vigilance and capacity for problem spotting as well as problem solving
Root and shoot growth of spring wheat (Triticum aestivum L.) are differently affected by increasing subsoil biopore density when grown under different subsoil moisture
A column experiment with five different pore densities (0, 1, 2, 3, and 4 pores columnâ1) and two varying moisture regimes (comparatively dry and comparatively moist regime) in the subsoil part of the columns was established. In each pore, Lumbricus terrestris was introduced for 28Â days before sowing wheat plants. After 40Â days of plant growth, watering was stopped to induce progressive topsoil drying. Parameters describing the shoot hydration, mineral uptake, and aboveground biomass were quantified. Root biomass and root length densities (RLD) were measured separately for six soil layers. Under dry subsoil conditions, plants grown under increasing biopore density showed an increase of the RLD and an improved shoot hydration but the aboveground biomass was unaffected. Since RLD but not root biomass was enhanced, it is assumed that roots were able to explore a larger volume of soil with the same amount of root biomass. Thereby, subsoil water likely was used more efficiently leading to an improved hydration. Under moist subsoil conditions, plants grown with increasing biopore density revealed enhanced shoot biomasses and nutrient uptake while the belowground biomass was unaffected. The improved nutrient uptake can be ascribed to, first, the higher subsoil water availability favoring mass flow driven nutrient uptake, and second, to direct and indirect effects of earthworms on the availability of soil nutrients. It is concluded that high biopore abundancies have the potential to improve not only the belowground but also the aboveground biomass. This, however, largely depends on subsoil moisture.Bundesministerium fĂŒr Bildung, Wissenschaft, Forschung und Technologie
http://dx.doi.org/10.13039/501100010571Humboldt-UniversitÀt zu Berlin (1034)Peer Reviewe
Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Effectively modeling long spatiotemporal sequences is challenging due to the
need to model complex spatial correlations and long-range temporal dependencies
simultaneously. ConvLSTMs attempt to address this by updating tensor-valued
states with recurrent neural networks, but their sequential computation makes
them slow to train. In contrast, Transformers can process an entire
spatiotemporal sequence, compressed into tokens, in parallel. However, the cost
of attention scales quadratically in length, limiting their scalability to
longer sequences. Here, we address the challenges of prior methods and
introduce convolutional state space models (ConvSSM) that combine the tensor
modeling ideas of ConvLSTM with the long sequence modeling approaches of state
space methods such as S4 and S5. First, we demonstrate how parallel scans can
be applied to convolutional recurrences to achieve subquadratic parallelization
and fast autoregressive generation. We then establish an equivalence between
the dynamics of ConvSSMs and SSMs, which motivates parameterization and
initialization strategies for modeling long-range dependencies. The result is
ConvS5, an efficient ConvSSM variant for long-range spatiotemporal modeling.
ConvS5 significantly outperforms Transformers and ConvLSTM on a long horizon
Moving-MNIST experiment while training 3X faster than ConvLSTM and generating
samples 400X faster than Transformers. In addition, ConvS5 matches or exceeds
the performance of state-of-the-art methods on challenging DMLab, Minecraft and
Habitat prediction benchmarks and enables new directions for modeling long
spatiotemporal sequences
Lifestyle interventions for patients with non-alcoholic steato-hepatitis-Design, rationale and protocol of the study "target group-specific optimisation of lifestyle interventions for behavior change in non-alcoholic steato-hepatitis (OPTI-NASH)"
BACKGROUND: Non-alcoholic steato-hepatitis (NASH) is the inflammatory, progressive form of non-alcoholic fatty liver disease (NAFLD). A delayed diagnose interval is typical for the majority of the patients because of the asymptomatic natural course. However, serious sequelae may develop such as cirrhosis or hepatocellular carcinoma. NASH is also associated with an increased risk of metabolic diseases. Obesity developed due to a lack of exercise or a disadvantageous diet often leads to NAFLD or NASH, thereby interventions including enhanced physical activity and calorie reduction form the actual gold standard of treatment. To date, patients rarely use these. The project aims to model lifestyle interventions based on the preferences of the NASH patients. METHODS: Based on a systematic review and focus group discussions, two discrete choice experiments (DCE) will be designed, one on aspects influencing successful uptake of lifestyle interventions and one to analyses parameters contributing to long-term participation. An online survey will be used to elicit patient's preferences on program design and on motivational aspects in a cross-sectional design. The recruitment will take place in nine certified specialist practices and hospital outpatient clinics aiming to reach a sample size of n = 500 which is also required for the DCE design. DISCUSSION: The results will provide an overview of the NASH patient's preferences regarding the successful uptake and long-term implementation of lifestyle interventions. Recommendations for optimized lifestyle change programs will be derived and an intervention manual will be developed to facilitate target group-specific inclusion in programs in practice.</p
Noise-Resilient Group Testing: Limitations and Constructions
We study combinatorial group testing schemes for learning -sparse Boolean
vectors using highly unreliable disjunctive measurements. We consider an
adversarial noise model that only limits the number of false observations, and
show that any noise-resilient scheme in this model can only approximately
reconstruct the sparse vector. On the positive side, we take this barrier to
our advantage and show that approximate reconstruction (within a satisfactory
degree of approximation) allows us to break the information theoretic lower
bound of that is known for exact reconstruction of
-sparse vectors of length via non-adaptive measurements, by a
multiplicative factor .
Specifically, we give simple randomized constructions of non-adaptive
measurement schemes, with measurements, that allow efficient
reconstruction of -sparse vectors up to false positives even in the
presence of false positives and false negatives within the
measurement outcomes, for any constant . We show that, information
theoretically, none of these parameters can be substantially improved without
dramatically affecting the others. Furthermore, we obtain several explicit
constructions, in particular one matching the randomized trade-off but using measurements. We also obtain explicit constructions
that allow fast reconstruction in time \poly(m), which would be sublinear in
for sufficiently sparse vectors. The main tool used in our construction is
the list-decoding view of randomness condensers and extractors.Comment: Full version. A preliminary summary of this work appears (under the
same title) in proceedings of the 17th International Symposium on
Fundamentals of Computation Theory (FCT 2009
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