Low Mass Calibration Target for MM-Wave Remote Sensing Instruments

The reliability of millimeter and sub-millimeter wave radiometer measurements is dependent on the accuracy of the loads they employ as calibration targets. In the recent past on-board calibration loads have been developed for a variety of satellite remote sensing instruments. Unfortunately some of these have suffered from calibration inaccuracies which had poor thermal performance of the calibration target as the root cause. Stringent performance parameters of the calibration target such as low reflectivity, high temperature uniformity, low mass and low power consumption combined with low volumetric requirements remain a challenge for the space instrument developer. In this paper we present a novel multi-layer absorber concept for a calibration load which offers an excellent compromise between very good radiometric performance and temperature uniformity and the mass and volumetric constraints required by space-borne calibration targets.

Compact low-power cortical recording architecture for compressive multichannel data acquisition

This paper introduces an area- and power-efficient approach for compressive recording of cortical signals used in an implantable system prior to transmission. Recent research on compressive sensing has shown promising results for sub-Nyquist sampling of sparse biological signals. Still, any large-scale implementation of this technique faces critical issues caused by the increased hardware intensity. The cost of implementing compressive sensing in a multichannel system in terms of area usage can be significantly higher than a conventional data acquisition system without compression. To tackle this issue, a new multichannel compressive sensing scheme which exploits the spatial sparsity of the signals recorded from the electrodes of the sensor array is proposed. The analysis shows that using this method, the power efficiency is preserved to a great extent while the area overhead is significantly reduced resulting in an improved power-area product. The proposed circuit architecture is implemented in a UMC 0.18 [Formula: see text]m CMOS technology. Extensive performance analysis and design optimization has been done resulting in a low-noise, compact and power-efficient implementation. The results of simulations and subsequent reconstructions show the possibility of recovering fourfold compressed intracranial EEG signals with an SNR as high as 21.8 dB, while consuming 10.5 [Formula: see text]W of power within an effective area of 250 [Formula: see text]m × 250 [Formula: see text]m per channel.

MaxMAC: a Maximally Traffic-Adaptive MAC Protocol for Wireless Sensor Networks

Energy efficiency is a major concern in the design of Wireless Sensor Networks (WSNs) and their communication protocols. As the radio transceiver typically accounts for a major portion of a WSN node’s power consumption, researchers have proposed Energy-Efficient Medium Access (E2-MAC) protocols that switch the radio transceiver off for a major part of the time. Such protocols typically trade off energy-efficiency versus classical quality of service parameters (throughput, latency, reliability). Today’s E2-MAC protocols are able to deliver little amounts of data with a low energy footprint, but introduce severe restrictions with respect to throughput and latency. Regrettably, they yet fail to adapt to varying traffic load at run-time. This paper presents MaxMAC, an E2-MAC protocol that targets at achieving maximal adaptivity with respect to throughput and latency. By adaptively tuning essential parameters at run-time, the protocol reaches the throughput and latency of energy-unconstrained CSMA in high-traffic phases, while still exhibiting a high energy-efficiency in periods of sparse traffic. The paper compares the protocol against a selection of today’s E2-MAC protocols and evaluates its advantages and drawbacks.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

Real-World Evaluation of Sensor Context-aware Adaptive Duty-cycled Opportunistic Routing

Energy is of primary concern in wireless sensor networks (WSNs). Low power transmission makes the wireless links unreliable, which leads to frequent topology changes. Resulting packet retransmissions aggravate the energy consumption. Beaconless routing approaches, such as opportunistic routing (OR) choose packet forwarders after data transmissions, and are promising to support dynamic features of WSNs. This paper proposes SCAD - Sensor Context-aware Adaptive Duty-cycled beaconless OR for WSNs. SCAD is a cross-layer routing solution and it brings the concept of beaconless OR into WSNs. SCAD selects packet forwarders based on multiple types of network contexts. To achieve a balance between performance and energy efficiency, SCAD adapts duty-cycles of sensors based on real-time traffic loads and energy drain rates. We implemented SCAD in TinyOS running on top of Tmote Sky sensor motes. Real-world evaluations show that SCAD outperforms other protocols in terms of both throughput and network lifetime.

The Face of Power

Objective: In humans and other animals, open, expansive postures (compared to contracted postures) are evolutionary developed expressions of power and have been shown to cause neuroendocrine and behavioral changes (Carney, Cuddy, & Yap, 2010). In the present study we aimed to investigate whether power postures have a bearing on the participant’s facial appearance and whether others are able to distinguish faces after “high power posing” from faces after “low power posing”. Methods: 16 models were photographed 4-5 minutes after having adopted high and low power postures. Two different high power and two different low power postures were held for 2 minutes each. Power-posing sessions were performed on two consecutive days. High and low power photographs of each model were paired and an independent sample of 100 participants were asked to pick the more dominant and the more likeable face of each pair. Results: Photographs that were taken after adopting high power postures were chosen significantly more often as being more dominant looking. There was no preference when asked to choose the more likeable photograph (chance level). A further independent sample rated each photograph for head tilt, making it unlikely that dominance ratings were caused merely by the posture of the head. Consistently, facial width-to-height ratio did not differ between faces after high and low power posing. Conclusions: Postures associated with high power affect facial appearance, leading to a more dominant looking face. This finding may have implications for everyday life, for instance when a dominant appearance is needed.