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Register a new userTowards Energy Positive Sensing using Kinetic Energy Harvesters
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Added by
Sara Khalifa
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information rich sensing signals while the energy from the harvester is used to power a load. We systematically analyse multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.
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Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is to employ energy harvesters that convert the environmental energy into electrical energy. Kinetic Energy Harvesting (KEH) converts the ambient motion/vibration energy into electrical energy to power the IoT sensor nodes. However, most previous works employ KEH without dynamically tracking the optimal operating point of the transducer for maximum power output. In this paper, we systematically analyse the relation between the operating point of the transducer and the corresponding energy yield. To this end, we explore the voltage-current characteristics of the KEH transducer to find its Maximum Power Point (MPP). We show how this operating point can be approximated in a practical energy harvesting circuit. We design two hardware circuit prototypes to evaluate the performance of the proposed mechanism and analyse the harvested energy using a precise load shaker under a wide set of controlled conditions typically found in human-centric applications. We analyse the dynamic current-voltage characteristics and specify the relation between the MPP sampling rate and harvesting efficiency which outlines the need for dynamic MPP tracking. The results show that the proposed energy harvesting mechanism outperforms the conventional method in terms of generated power and offers at least one order of magnitude higher power than the latter.
This paper presents a wireless neural recording system featuring energy-efficient data compression and encryption. An ultra-high efficiency is achieved by leveraging compressed sensing (CS) for simultaneous data compression and encryption. CS enables sub-Nyquist sampling of neural signals by taking advantage of its intrinsic sparsity. It simultaneously encrypts the data with the sampling matrix being the cryptographic key. To share the key over an insecure wireless channel, we implement an elliptic-curve cryptography (ECC) based key exchanging protocol. The CS operation is executed in a custom-designed IC fabricated in 180nm CMOS technology. Mixed-signal circuits are designed to optimize the power efficiency of the matrix-vector multiplication (MVM) of the CS operation. The ECC algorithm is implemented in a low-power Cortex-M0 microcontroller (MCU). To be protected from timing and power analysis attacks, the implementation avoids possible data-dependent branches and also employs a randomized ECC initialization. At a compression ratio of 8x, the average correlated coefficient between the reconstructed signals and the uncompressed signals is 0.973, while the ciphertext-only attacks (CoA) achieve no better than 0.054 over 200,000 attacks. The prototype achieves a 35x power saving compared with conventional implementation in low-power MCUs. This work demonstrates a promising solution for future chronic neural recording systems with requirements in high energy efficiency and security.
The measurement of parameters that describe kinetic processes is important in the study of molecular interactions. It enables a deeper understanding of the physical mechanisms underlying how different biological entities interact with each other, such as viruses with cells, vaccines with antibodies, or new drugs with specific diseases. In this work, we study theoretically the use of quantum sensing techniques for measuring the kinetic parameters of molecular interactions. The sensor we consider is a plasmonic resonance sensor -- a label-free photonic sensor that is one of the most widely used in research and industry. The first type of interaction we study is the antigen BSA interacting with antibody IgG1, which provides a large sensor response. The second type is the enzyme carbonic anhydrase interacting with the tumor growth inhibitor benzenesulfonamide, which produces a small sensor response. For both types of interaction we consider the use of two-mode Fock states, squeezed vacuum states and squeezed displaced states. We find that these quantum states offer an enhancement in the measurement precision of kinetic parameters when compared to that obtained with classical light. The results may help in the design of more precise quantum-based sensors for studying kinetics in the life sciences.
Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing with SE problem are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e.g., Compressively Sensed Measurements). This study proposes a novel approach for learning such a mapping from a training set. To accomplish this objective, the Convolutional Support Estimator Networks (CSENs), each with a compact configuration, are designed. The proposed CSEN can be a crucial tool for the following scenarios: (i) Real-time and low-cost support estimation can be applied in any mobile and low-power edge device for anomaly localization, simultaneous face recognition, etc. (ii) CSENs output can directly be used as prior information which improves the performance of sparse signal recovery algorithms. The results over the benchmark datasets show that state-of-the-art performance levels can be achieved by the proposed approach with a significantly reduced computational complexity.
Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting. We propose a method, which we name kinetic compressive sensing (KCS), based on a hierarchical Bayesian model and on a novel reconstruction algorithm, that encodes sparsity of kinetic parameters. Parametric maps are reconstructed by maximizing the joint probability, with an Iterated Conditional Modes (ICM) approach, alternating the optimization of activity time series (OS-MAP-OSL), and kinetic parameters (MAP-LM). We evaluated the proposed algorithm on a simulated dynamic phantom: a bias/variance study confirmed how direct estimates can improve the quality of parametric maps over a post-reconstruction fitting, and showed how the novel sparsity prior can further reduce their variance, without affecting bias. Real FDG PET human brain data (Siemens mMR, 40min) images were also processed. Results enforced how the proposed KCS-regularized direct method can produce spatially coherent images and parametric maps, with lower spatial noise and better tissue contrast. A GPU-based open source implementation of the algorithm is provided.
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