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 researchers’ and practitioners’ 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 column−1) 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 28 days before sowing wheat plants. After 40 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 für Bildung, Wissenschaft, Forschung und Technologie http://dx.doi.org/10.13039/501100010571Humboldt-Universität 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 $d$-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 $\tilde{\Omega}(d^2 \log n)$ that is known for exact reconstruction of $d$-sparse vectors of length $n$ via non-adaptive measurements, by a multiplicative factor $\tilde{\Omega}(d)$. Specifically, we give simple randomized constructions of non-adaptive measurement schemes, with $m=O(d \log n)$ measurements, that allow efficient reconstruction of $d$-sparse vectors up to $O(d)$ false positives even in the presence of $\delta m$ false positives and $O(m/d)$ false negatives within the measurement outcomes, for any constant $\delta < 1$. 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 $m = O(d^{1+o(1)} \log n)$ measurements. We also obtain explicit constructions that allow fast reconstruction in time \poly(m), which would be sublinear in $n$ 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