A behavioral model for the non-linear on-resistance in sample-and-hold analog switches

Abstract — This paper presents a behavioral model of the non-linear on-resistance in S&H analog switches. The model is suitable for analysis and design of low-voltage sampled data systems. Simulated results using the ATMEL 0.24µm CMOS process are shown to validate the model. The Advanced-Compact-Mosfet model (ACM), a symmetric drainto-source model, valid in the whole inversion level regime of MOS transistors, is used as reference.

Tactile sensing chips with POSFET array and integrated interface electronics

This work presents the advanced version of novel POSFET (Piezoelectric Oxide Semiconductor Field Effect Transistor) devices based tactile sensing chip. The new version of the tactile sensing chip presented here comprises of a 4 x 4 array of POSFET touch sensing devices and integrated interface electronics (i.e. multiplexers, high compliance current sinks and voltage output buffers). The chip also includes four temperature diodes for the measurement of contact temperature. Various components on the chip have been characterized systematically and the overall operation of the tactile sensing system has been evaluated. With new design the POSFET devices have improved performance (i.e. linear response in the dynamic contact forces range of 0.01–3N and sensitivity (without amplification) of 102.4 mV/N), which is more than twice the performance of their previous implementations. The integrated interface electronics result in reduced interconnections which otherwise would be needed to connect the POSFET array with off-chip interface electronic circuitry. This research paves the way for CMOS (Complementary Metal Oxide Semiconductor) implementation of full on-chip tactile sensing systems based on POSFETs

Tactile Sensing for Robotic Applications

This chapter provides an overview of tactile sensing in robotics. This chapter is an attempt to answer three basic questions: \u2022 What is meant by Tactile Sensing? \u2022 Why Tactile Sensing is important? \u2022 How Tactile Sensing is achieved? The chapter is organized to sequentially provide the answers to above basic questions. Tactile sensing has often been considered as force sensing, which is not wholly true. In order to clarify such misconceptions about tactile sensing, it is defined in section 2. Why tactile section is important for robotics and what parameters are needed to be measured by tactile sensors to successfully perform various tasks, are discussed in section 3. An overview of `How tactile sensing has been achieved\u2019 is given in section 4, where a number of technologies and transduction methods, that have been used to improve the tactile sensing capability of robotic devices, are discussed. Lack of any tactile analog to Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Devices (CCD) optical arrays has often been cited as one of the reasons for the slow development of tactile sensing vis-\ue0-vis other sense modalities like vision sensing. Our own contribution \u2013 development of tactile sensing arrays using piezoelectric polymers and involving silicon micromachining - is an attempt in the direction of achieving tactile analog of CMOS optical arrays. The first phase implementation of these tactile sensing arrays is discussed in section 5. Section 6 concludes the chapter with a brief discussion on the present status of tactile sensing and the challenges that remain to be solved

A Comparative Study of Various Probability Density Estimation Methods for Data Analysis

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Experimental assessment of the interface electronic system for PVDF-based piezoelectric tactile sensors

Tactile sensors are widely employed to enable the sense of touch for applications such as robotics and prosthetics. In addition to the selection of an appropriate sensing material, the performance of the tactile sensing system is conditioned by its interface electronic system. On the other hand, due to the need to embed the tactile sensing system into a prosthetic device, strict requirements such as small size and low power consumption are imposed on the system design. This paper presents the experimental assessment and characterization of an interface electronic system for piezoelectric tactile sensors for prosthetic applications. The interface electronic is proposed as part of a wearable system intended to be integrated into an upper limb prosthetic device. The system is based on a low power arm-microcontroller and a DDC232 device. Electrical and electromechanical setups have been implemented to assess the response of the interface electronic with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system

Full-hand electrotactile feedback using electronic skin and matrix electrodes for high-bandwidth human–machine interfacing

Tactile feedback is relevant in a broad range of human–machine interaction systems (e.g. teleoperation, virtual reality and prosthetics). The available tactile feedback interfaces comprise few sensing and stimulation units, which limits the amount of information conveyed to the user. The present study describes a novel technology that relies on distributed sensing and stimulation to convey comprehensive tactile feedback to the user of a robotic end effector. The system comprises six flexible sensing arrays (57 sensors) integrated on the fingers and palm of a robotic hand, embedded electronics (64 recording channels), a multichannel stimulator and seven flexible electrodes (64 stimulation pads) placed on the volar side of the subject’s hand. The system was tested in seven subjects asked to recognize contact positions and identify contact sliding on the electronic skin, using distributed anode configuration (DAC) and single dedicated anode configuration. The experiments demonstrated that DAC resulted in substantially better performance. Using DAC, the system successfully translated the contact patterns into electrotactile profiles that the subjects could recognize with satisfactory accuracy (i.e. median{IQR} of 88.6{11}% for static and 93.3{5}% for dynamic patterns). The proposed system is an important step towards the development of a high-density human–machine interfacing between the user and a robotic han

X-ray variability with WFXT: AGNs, transients and more

The Wide Field X-ray Telescope (WFXT) is a proposed mission with a high survey speed, due to the combination of large field of view (FOV) and effective area, i.e. grasp, and sharp PSF across the whole FOV. These characteristics make it suitable to detect a large number of variable and transient X-ray sources during its operating lifetime. Here we present estimates of the WFXT capabilities in the time domain, allowing to study the variability of thousand of AGNs with significant detail, as well as to constrain the rates and properties of hundreds of distant, faint and/or rare objects such as X-ray Flashes/faint GRBs, Tidal Disruption Events, ULXs, Type-I bursts etc. The planned WFXT extragalactic surveys will thus allow to trace variable and transient X-ray populations over large cosmological volumes.Comment: Proceedings of "The Wide Field X-ray Telescope Workshop", held in Bologna, Italy, Nov. 25-26 2009 (arXiv:1010.5889). To appear in Memorie della Societ\`a Astronomica Italiana 2010 - Minor corrections to text

Embedded Electronic System Based on Dedicated Hardware DSPs for Electronic Skin Implementation

The effort to develop an electronic skin is highly motivated by many application domains namely robotics, biomedical instrumentations, and replacement prosthetic devices. Several e-skin systems have been proposed recently and have demonstrated the need of an embedded electronic system for tactile data processing either to mimic the human skin or to respond to the application demands. Processing tactile data requires efficient methods to extract meaningful information from raw sensors data. In this framework, our goal is the development of a dedicated embedded electronic system for electronic skin. The embedded electronic system has to acquire the tactile data, process and extract structured information. Machine Learning (ML) represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensors data. This paper presents an embedded electronic system based on dedicated hardware implementation for electronic skin systems. It provides a Tensorial kernel function implementation for machine learning based on Tensorial kernel approach. Results assess the time latency and the hardware complexity for real time functionality. The implementation results highlight the high amount of power consumption needed for the input touch modalities classification task. Conclusions and future perspectives are also presented