Astronomical Data Analysis and Sparsity: from Wavelets to Compressed Sensing

Wavelets have been used extensively for several years now in astronomy for many purposes, ranging from data filtering and deconvolution, to star and galaxy detection or cosmic ray removal. More recent sparse representations such ridgelets or curvelets have also been proposed for the detection of anisotropic features such cosmic strings in the cosmic microwave background. We review in this paper a range of methods based on sparsity that have been proposed for astronomical data analysis. We also discuss what is the impact of Compressed Sensing, the new sampling theory, in astronomy for collecting the data, transferring them to the earth or reconstructing an image from incomplete measurements.Comment: Submitted. Full paper will figures available at http://jstarck.free.fr/IEEE09_SparseAstro.pd

H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has an overwhelming pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient

Feasibility of wireless horse monitoring using a kinetic energy harvester model

To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric energy harvester at the leg (or in the hoof) of the horse, allowing perpetual monitoring devices. This paper reports the average power that can be delivered to the node by energy harvesting for four different natural gaits of the horse: stand, walking, trot and canter, based on an existing model for a velocity-damped resonant generator (VDRG). To this end, 33 accelerometer datasets were collected over 4.5 h from six horses during different activities. Based on these measurements, a vibrational energy harvester model was calculated that can provide up to 64.04 mu W during the energetic canter gait, taking an energy conversion rate of 60% into account. Most energy is provided during canter in the forward direction of the horse. The downwards direction is less suitable for power harvesting. Additionally, different wireless technologies are considered to realize perpetual wireless data sensing. During horse training sessions, BLE allows continues data transmissions (one packet every 0.04 s during canter), whereas IEEE 802.15.4 and UWB technologies are better suited for continuous horse monitoring during less energetic states due to their lower sleep current

Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

Realistic Compression of Kinetic Sensor Data

We introduce a realistic analysis for a framework for storing and processing kinetic data observed by sensor networks. The massive data sets generated by these networks motivates a significant need for compression. We are interested in the kinetic data generated by a finite set of objects moving through space. Our previously introduced framework and accompanying compression algorithm assumed a given set of sensors, each of which continuously observes these moving objects in its surrounding region. The model relies purely on sensor observations; it allows points to move freely and requires no advance notification of motion plans. Here, we extend the initial theoretical analysis of this framework and compression scheme to a more realistic setting. We extend the current understanding of empirical entropy to introduce definitions for joint empirical entropy, conditional empirical entropy, and empirical independence. We also introduce a notion of limited independence between the outputs of the sensors in the system. We show that, even with this notion of limited independence and in both the statistical and empirical settings, the previously introduced compression algorithm achieves an encoding size on the order of the optimal

Rapid prototyping of carbon-based chemiresistive gas sensors on paper

Chemically functionalized carbon nanotubes (CNTs) are promising materials for sensing of gases and volatile organic compounds. However, the poor solubility of carbon nanotubes hinders their chemical functionalization and the subsequent integration of these materials into devices. This manuscript describes a solvent-free procedure for rapid prototyping of selective chemiresistors from CNTs and graphite on the surface of paper. This procedure enables fabrication of functional gas sensors from commercially available starting materials in less than 15 min. The first step of this procedure involves the generation of solid composites of CNTs or graphite with small molecule selectors—designed to interact with specific classes of gaseous analytes—by solvent-free mechanical mixing in a ball mill and subsequent compression. The second step involves deposition of chemiresistive sensors by mechanical abrasion of these solid composites onto the surface of paper. Parallel fabrication of multiple chemiresistors from diverse composites rapidly generates cross-reactive arrays capable of sensing and differentiating gases and volatile organic compounds at part-per-million and part-per-thousand concentrations.Massachusetts Institute of Technology. Institute for Soldier NanotechnologiesNational Cancer Institute (U.S.) (Ruth L. Kirschstein National Research Service Award F32CA157197

A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems

A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution. Document type: Articl

Energy scavenging system for indoor wireless sensor network

As wireless communication evolves wireless sensors have begun to be integrated into society more and more. As these sensors are used to a greater extent newer and better ways to keep them working optimally have begun to surface. One such method aims to further the sensors energy independence on humans. This technique is known as energy scavenging. The logic behind energy scavenging is to allow the device to have its own reliable source of energy that does not require upkeep, has a long life expectancy, and does not completely rely on an internal power source. The aim of this thesis is to research techniques for indoor energy scavenging for sensor that is used to monitor patients in a hospital. There are numerous techniques to achieve energy scavenging in wireless sensor networks. Multiple scavenging methods are known such as vibration energy, thermoelectric energy, and photovoltaic energy. All of these methods were analyzed and compared to see which would be optimal for the situation the sensor would be put in. Other techniques come into play to help the efficiency of the sensor network. These methods were also examined to help the energy scavenging to be more feasible