High Linearity SAR ADC for Smart Sensor Applications

This paper presents capacitive array optimization technique to improve the Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) of Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) for smart sensor application. Monte Carlo simulation results show that capacitive array optimization technique proposed can make the SFDR, SNDR and (Signal-to-Noise Ratio) SNR more concentrated, which means the differences between maximum value and minimum value of SFDR, SNDR and SNR are much smaller than the conventional calibration techniques, more stable performance enhancement can be achieved, and the averaged SFDR is improved from 72.9 dB to 91.1 dB by using the capacitive array optimization method, 18.2 dB improvement of SFDR is obtained with only little expense of digital logic circuits, which makes it good choice for high resolution and high linearity smart sensing systems

Implementation aspects and offline digital signal processing of a smart pebble for river bed sediment transport monitoring

Conceptualization aspects of a smart sediment particle (smart pebble) for monitoring of sediment transport in riverbeds have been documented previously [1]. However, this mixed signal approach was done only at a conceptual level and lacks complete implementation aspects such as limited PCB real estate, a miniaturized power source and adequately addressing the offset errors. A fully digital version with multiple strap-down MEMs, signal conditioning blocks, an 8-bit processor and a memory subsystem was designed and assembled within a less than 4 cm diameter sphere to allow data capture for up to 15 minutes. This compact subsystem allows exporting of output data, stored within the memory from nine sampled MEM sensors, into an offline-processing environment for further processing to generate essential motion information. Complex mathematical algorithms for axis conversion, etc, are housed within the offline-processing environment reducing the burden on the smart pebble. The total electronic subsystem embedded within the pebble together with the external processing algorithms tackle cumulative errors, gravitational compensation requirements and offset errors, while being powered by a specially designed power stage, based on a single alkaline cell

Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

Testing and calibration of smart pebble for river bed sediment transport monitoring

The Smart Pebble (smart particle), SP, has been developed for the past two years to monitor sediment transport in riverbeds. The implementation is based on use of small size and low cost acceleration and angular motion sensors. In this stage, the project is focused on calibrating and testing the final version of the SP as well as its packaging in a 4-cm diameter spherical package. The calibration was done in two stages; individual sensor calibration and complete system calibration. The complete SP unit was tested under linear motions generated by a shake table, and 2D rotational motions using two manually controlled servomotors. Offline digital signal conditioning was done in MATLAB. The preliminary results show that the system has relatively large amplitude error due to low sampling frequency. Experiments conducted by sampling a 1-Hz sinusoidal signal at different rates show that to keep the amplitude error of the system under 5% the sampling rate has to be at least 10 times the maximum bandwidth of the signals acquired from sensors

Communication-efficient Distributed Multi-resource Allocation

In several smart city applications, multiple resources must be allocated among competing agents that are coupled through such shared resources and are constrained --- either through limitations of communication infrastructure or privacy considerations. We propose a distributed algorithm to solve such distributed multi-resource allocation problems with no direct inter-agent communication. We do so by extending a recently introduced additive-increase multiplicative-decrease (AIMD) algorithm, which only uses very little communication between the system and agents. Namely, a control unit broadcasts a one-bit signal to agents whenever one of the allocated resources exceeds capacity. Agents then respond to this signal in a probabilistic manner. In the proposed algorithm, each agent makes decision of its resource demand locally and an agent is unaware of the resource allocation of other agents. In empirical results, we observe that the average allocations converge over time to optimal allocations.Comment: To appear in IEEE International Smart Cities Conference (ISC2 2018), Kansas City, USA, September, 2018. arXiv admin note: substantial text overlap with arXiv:1711.0197

CXCR4-targeted and MMP-responsive iron oxide nanoparticles for enhanced magnetic resonance imaging

MRI offers high spatial resolution with excellent tissue penetration but it has limited sensitivity and the commonly administered contrast agents lack specificity. In this study, two sets of iron oxide nanoparticles (IONPs) were synthesized that were designed to selectively undergo copper-free click conjugation upon sensing of matrix metalloproteinase (MMP) enzymes, thereby leading to a self-assembled superparamagnetic nanocluster network with T2 signal enhancement properties. For this purpose, IONPs with bioorthogonal azide and alkyne surfaces masked by polyethylene glycol (PEG) layers tethered to CXCR4-targeted peptide ligands were synthesized and characterized. The IONPs were tested in vitro and T2 signal enhancements of around 160 % were measured when the IONPs were incubated with cells expressing MMP2/9 and CXCR4. Simultaneous systemic administration of the bioorthogonal IONPs in tumor-bearing mice demonstrated the signal-enhancing ability of these ‘smart’ self-assembling nanomaterials