Showing papers on "Data acquisition published in 2009"

The IceCube data acquisition system: Signal capture, digitization, and timestamping

Abstract: IceCube is a km-scale neutrino observatory under construction at the South Pole with sensors both in the deep ice (InIce) and on the surface (IceTop). The sensors, called Digital Optical Modules (DOMs). detect, digitize and timestamp the signals from optical Cherenkov-radiation photons. The DOM Main Board (MB) data acquisition subsystem is connected to the central DAQ in the IceCube Laboratory (ICL) by a single twisted copper wire-pair and transmits packetized data on demand. Time calibration is maintained throughout the array by regular transmission to the DOMs of precisely timed analog signals, synchronized to a central GPS-disciplined clock. The design goals and consequent features, functional capabilities, and initial performance of the DOM MB, and the operation of a combined array of DOMs as a system, are described here. Experience with the first InIce strings and the IceTop stations indicates that the system design and performance goals have been achieved. (c) 2009 Elsevier B.V. All rights reserved.

A Compressive Sensing Data Acquisition and Imaging Method for Stepped Frequency GPRs

Abstract: A novel data acquisition and imaging method is presented for stepped-frequency continuous-wave ground penetrating radars (SFCW GPRs). It is shown that if the target space is sparse, i.e., a small number of point like targets, it is enough to make measurements at only a small number of random frequencies to construct an image of the target space by solving a convex optimization problem which enforces sparsity through lscr 1 minimization. This measurement strategy greatly reduces the data acquisition time at the expense of higher computational costs. Imaging results for both simulated and experimental GPR data exhibit less clutter than the standard migration methods and are robust to noise and random spatial sampling. The images also have increased resolution where closely spaced targets that cannot be resolved by the standard migration methods can be resolved by the proposed method.

Algorithms for State Restoration and Trace-Signal Selection for Data Acquisition in Silicon Debug

Abstract: To locate and correct design errors that escape pre-silicon verification, silicon debug has become a necessary step in the implementation flow of digital integrated circuits. Embedded logic analysis, which employs on-chip storage units to acquire data in real time from the internal signals of the circuit-under-debug, has emerged as a powerful technique for improving observability during in-system debug. However, as the amount of data that can be acquired is limited by the on-chip storage capacity, the decision on which signals to sample is essential when it is not known a priori where the bugs will occur. In this paper, we present accelerated algorithms for restoring circuit state elements from the traces collected during a debug session, by exploiting bitwise parallelism. We also introduce new metrics that guide the automated selection of trace signals, which can enhance the real-time observability during in-system debug.

Trajectory analysis for magnetic particle imaging

Abstract: Recently a new imaging technique called magnetic particle imaging was proposed. The method uses the nonlinear response of magnetic nanoparticles when a time varying magnetic field is applied. Spatial encoding is achieved by moving a field-free point through an object of interest while the field strength in the vicinity of the point is high. A resolution in the submillimeter range is provided even for fast data acquisition sequences. In this paper, a simulation study is performed on different trajectories moving the field-free point through the field of view. The purpose is to provide mandatory information for the design of a magnetic particle imaging scanner. Trajectories are compared with respect to density, speed and image quality when applied in data acquisition. Since simulation of the involved physics is a time demanding task, moreover, an efficient implementation is presented utilizing caching techniques.

Monitoring in Industrial Systems Using Wireless Sensor Network With Dynamic Power Management

Abstract: The advances in wireless communication, microelectronics, digital electronics, and highly integrated electronics and the increasing need for more efficient controlled electric systems make the development of monitoring and supervisory control tools the object of study of many researchers. This paper proposes a digital system for energy usage evaluation, condition monitoring, diagnosis, and supervisory control for electric systems applying wireless sensor networks (WSNs) with dynamic power management (DPM). The system is based on two hardware topologies responsible for signal acquisition, processing, and transmission: intelligent sensor modules (ISMs) and remote data acquisition units (RDAUs). The gateway function of the wired network is carried out by remote servers (RSs) based on the Soekris architecture, which is responsible for receiving the data collected and transmitting it to the supervisory controller (SC). To extend the WSN lifetime, sensor nodes implement a DPM protocol. The basic characteristics of the presented system are the following: 1) easy implementation; 2) low-cost implementation; 3) easy implementation of redundant routines (security); 4) portability/versatility; and 5) extended network lifetime.

Development and Implementation of a Real-Time See-Through-Wall Radar System Based on FPGA

Abstract: This paper presents the development of a low-cost real-time ultrawideband (UWB) see-through-wall (STW) imaging radar system. The designs of the microwave front end, the UWB data acquisition, and the system integration are discussed in detail. As for the most challenging task, the UWB data acquisition, we introduce a custom low-cost module based on commercial field-programmable gate array (FPGA) boards and low-speed analog-to-digital converters. The introduced module does not require a custom implementation of high-speed wideband mixed-signal circuitry but only depends on the FPGA firmware design, which favors a rapid system prototyping. The data acquisition module accomplishes a 100-ps equivalent-time sampling resolution at 100-Msamples/s real-time rate, while the developed STW system provides a 2-D real-time view of motion with a 1.5-ms speed behind walls. The system allows for an easy reconfiguration to support multiple operating frequency ranges, pulse sampling resolutions, and array deployments, thus providing a tremendous experimental flexibility. Our studies indicate that utilizing available technologies and off-the-shelf components could produce a practical stand-alone STW system at reasonable design effort and cost, which will lead to a better understanding of the challenging problems associated with STW technology.

Learning Systems for Electric Consumption of Buildings

Abstract: Individual appliances’ electricity consumption is automatically disaggregated from a single custom metering system on the main feed to an occupied residential building. A data acquisition system samples voltage and current at 100 kHz, then calculates real and reactive power, harmonics, and other features at 20Hz. A probabilistic eventdetector using the generalized likelihood ratio (GLR) matches human-labeled events to the time-series of features. Machine-learning classification was most successful with the 1-nearest-neighbor algorithm, correctly identifying 90% of the laboratorygenerated training events and 79% of validation examples. The challenge of obtaining adequate training data for the real-world home leads to the development of the Wire Spy, a wirelessly-networked event detector with an inductive sensor which clamps to the cable of an appliance.

PermaDAQ: A scientific instrument for precision sensing and data recovery in environmental extremes

Abstract: The PermaSense project has set the ambitious goal of gathering real-time environmental data for high-mountain permafrost in unattended operation over multiple years. This paper discusses the specialized sensing and data recovery architecture tailored to meet the precision, reliability and durability requirements of scientists utilizing the data for model validation. We present a custom sensor interface board including specialized sensors and redundancy features for end-to-end data validation. Aspects of high-quality data acquisition, design for reliability by strict separation of operating phases and analysis of energy efficiency are discussed. The system integration using the Dozer protocol scheme achieves a best-in-class average power consumption of 148µA considerably exceeding the lifetime requirement.

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

Abstract: In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs, but can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets.

Suitability of a PXI platform for an electrical impedance tomography system

Abstract: There are many different electrical impedance tomography (EIT) systems which are either non-commercial (in-house products) or commercial products. However, these systems are usually designed for specific applications and therefore the functionality of the systems might be limited. Nowadays there are commercially available many low-cost, efficient and accurate multifunctional components for data acquisition and signal processing. Therefore, it should be possible to construct an EIT system which is mainly built from commercially available components. The main goal of this work was to study the performance of a PXI-based EIT system. In this work, a PXI-based EIT system with 16 independent current injection channels and 80 independent measurement channels was constructed and tested. The results indicate that an EIT system can be constructed using a PXI platform which decreases the construction time of the system. Moreover, the system is efficient, accurate, modular, and it is not limited to any predetermined measurement protocols.

A method of synchronizing data acquisition and a portable electronic device configured to perform the same

Abstract: A method of synchronizing force data with touch data and a portable electronic device configured to perform the same are provided. In accordance with one embodiment, there is provided a method of synchronizing data acquisition in a portable electronic device, the method comprising: detecting touch events on a touch-sensitive display and storing touch data for each touch event with a time stamp for the touch event; detecting force events on the touch-sensitive display and storing force data for each force event with a time stamp for the force event; matching touch data for a particular touch event with force data for a force event corresponding to the particular touch event; and outputting the matched touch data and force data for input processing operations.

A Fully Implantable 96-channel Neural Data Acquisition System

Abstract: A fully implantable neural data acquisition system is a key component of a clinically viable brain-machine interface. This type of system must communicate with the outside world and obtain power without the use of wires that cross through the skin. We present a 96-channel fully implantable neural data acquisition system. This system performs spike detection and extraction within the body and wirelessly transmits data to an external unit. Power is supplied wirelessly through the use of inductively coupled coils. The system was implanted acutely in sheep and successfully recorded, processed and transmitted neural data. Bidirectional communication between the implanted system and an external unit was successful over a range of 2 m. The system is also shown to integrate well into a brain-machine interface. This demonstration of a high channel-count fully implanted neural data acquisition system is a critical step in the development of a clinically viable brain-machine interface.

Image quality, tissue heating, and frame rate trade-offs in acoustic radiation force impulse imaging

Abstract: The real-time application of acoustic radiation force impulse (ARFI) imaging requires both short acquisition times for a single ARFI image and repeated acquisition of these frames. Due to the high energy of pulses required to generate appreciable radiation force, however, repeated acquisitions could result in substantial transducer face and tissue heating. We describe and evaluate several novel beam sequencing schemes which, along with parallel-receive acquisition, are designed to reduce acquisition time and heating. These techniques reduce the total number of radiation force impulses needed to generate an image and minimize the time between successive impulses. We present qualitative and quantitative analyses of the trade-offs in image quality resulting from the acquisition schemes. Results indicate that these techniques yield a significant improvement in frame rate with only moderate decreases in image quality. Tissue and transducer face heating resulting from these schemes is assessed through finite element method modeling and thermocouple measurements. Results indicate that heating issues can be mitigated by employing ARFI acquisition sequences that utilize the highest track-to-excitation ratio possible.

Crack measurement: Development, testing and applications of an automatic image-based algorithm

Abstract: The paper presents an Image-based Method for Crack Analysis (IMCA) which is capable of processing a sequence of digital imagery to perform a twofold task: (i) the extraction of crack borders and the evaluation of its width across the longitudinal profile; (ii) the measurement of crack deformations (width, sliding and rotation). Here both problems are solved in 2-D, but an extension to 3-D is also addressed. The equipment needed to apply the method is made up of a digital camera (or a still video-camera in case a high frequency in data acquisition is necessary), an orientation frame which establishes the object reference system, a pair of signalized supports to be placed in a permanent way on both sides of the crack to compute deformations; however, permanent targets are mandatory only for case (ii). The measurement process is carried out in a fully automatic way, a fact also that makes this technique highly operational for unskilled people in engineering surveying or photogrammetry. The accuracy of the proposed method, evaluated in experimental tests adopting different consumer digital cameras, is about ± 5–20 μm, like the accuracy of most deformometers, but with the advantage of automation and of augmented achievable information; moreover, the image sequence can be archived and off-line measurements could be performed at any time.

Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions

Abstract: SAR stereo image analysis for 3D information extraction is mostly carried out based on imagery taken under same-side or opposite-side viewing conditions. For urban scenes in practice stereo is up to now usually restricted to the first configuration, because increasing image dissimilarity connected with rising illumination direction differences leads to a lack of suitable features for matching, especially in the case of low or medium resolution data. However, due to two developments SAR stereo from arbitrary viewing conditions becomes an interesting option for urban information extraction. The first one is the availability of airborne sensor systems, which are capable of more flexible data acquisition in comparison to satellite sensors. This flexibility enables multi-aspect analysis of objects in built-up areas for various kinds of purpose, such as building recognition, road network extraction, or traffic monitoring. The second development is the significant improvement of the geometric resolution providing a high level of detail especially of roof features, which can be observed from a wide span of viewpoints. In this paper, high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their height from the different layover effects in views taken from orthogonal aspect angles. High level object matching is proposed that relies on symbolic data, representing suitable features of urban objects. Here, a knowledge-based approach is applied, which is realized by a production system that codes a set of suitable principles of perceptual grouping in its production rules. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows of point targets, rectangular structures or symmetries. The stereo analysis is then accomplished by means of productions that combine and match these 2D image objects and infer their height by 3D clustering. The approach is tested using real SAR data of an urban scene.

The IceCube data acquisition system: Signal capture, digitization,and timestamping

Abstract: IceCube is a km-scale neutrino observatory under construction at the South Pole with sensors both in the deep ice (InIce) and on the surface (IceTop). The sensors, called Digital Optical Modules (DOMs), detect, digitize and timestamp the signals from optical Cherenkov-radiation photons. The DOM Main Board (MB) data acquisition subsystem is connected to the central DAQ in the IceCube Laboratory (ICL) by a single twisted copper wire-pair and transmits packetized data on demand. Time calibration is maintained throughout the array by regular transmission to the DOMs of precisely timed analog signals, synchronized to a central GPS-disciplined clock. The design goals and consequent features, functional capabilities, and initial performance of the DOM MB, and the operation of a combined array of DOMs as a system, are described here. Experience with the first InIce strings and the IceTop stations indicates that the system design and performance goals have been achieved.

Application of wireless sensor networks in environmental monitoring

Abstract: The wireless sensor networks (WSN) is one of the most significant technologies in the 21st century In recent years, achievements in micro-sensor technology and low-power electronics make WSN become realities in applications In this paper, we propose a system for WSN based on environmental monitoring The system can monitor several environmental parameters such as underground water level, barometric pressure, ambient temperature, atmospheric humidity, wind direction, wind speed and rainfall and provide various convenient services for end users who can manage the data via a website from long-distance or applications in console terminal This paper introduces the architecture of WSN system, the hardware of node, data acquisition, data processing with gateway and data visualization Through the use of WSN in the environmental monitoring, it's possible to change the traditionally environmental monitoring methods for people

Highway Bridge Assessment Using an Adaptive Real-Time Wireless Sensor Network

Abstract: A real-time wireless sensor network platform capable of maintaining lossless data transmission over several minutes of continuous, high-rate sampling is presented in this paper. The platform was designed specifically to provide the capability to enable expeditious system identification, as well as load rating of highway bridges without compromising the typical data acquisition parameters employed in comparable cable-based tests. Consequently, the hardware signal conditioning interface permits data collection from a variety of sensors typical to structural health monitoring, including accelerometers, strain transducers, and temperature sensors. The embedded software features a proprietary network transmission protocol capable of lossless, real-time delivery of up to 40 measurement channels at an effective sampling rate of 128 samples per second per channel. Documented in this paper is a field study on an end-of-service highway bridge in which ambient vibration monitoring was performed using 60 accelerometers interfaced with 30 wireless sensor nodes operating within one of two simultaneously operating star topology networks. In addition, an experimental load rating of the entire structure was performed through large-scale strain measurement facilitated by the same wireless sensor network platform.

A multi-threshold sampling method for TOF PET signal processing

Abstract: As an approach to realizing all-digital data acquisition for positron emission tomography (PET), we have previously proposed and studied a multi-threshold sampling method to generate samples of a PET event waveform with respect to a few user-defined amplitudes. In this sampling scheme, one can extract both the energy and timing information for an event. In this paper, we report our prototype implementation of this sampling method and the performance results obtained with this prototype. The prototype consists of two multi-threshold discriminator boards and a time-to-digital converter (TDC) board. Each of the multi-threshold discriminator boards takes one input and provides up to 8 threshold levels, which can be defined by users, for sampling the input signal. The TDC board employs the CERN HPTDC chip that determines the digitized times of the leading and falling edges of the discriminator output pulses. We connect our prototype electronics to the outputs of two Hamamatsu R9800 photomultiplier tubes (PMTs) that are individually coupled to a 6.25×6.25×25mm(3) LSO crystal. By analyzing waveform samples generated by using four thresholds, we obtain a coincidence timing resolution of about 340 ps and an ∼18% energy resolution at 511 keV. We are also able to estimate the decay-time constant from the resulting samples and obtain a mean value of 44ns with an ∼9 ns FWHM. In comparison, using digitized waveforms obtained at a 20 GSps sampling rate for the same LSO/PMT modules we obtain ∼300 ps coincidence timing resolution, ∼14% energy resolution at 511 keV, and ∼5 ns FWHM for the estimated decay-time constant. Details of the results on the timing and energy resolutions by using the multi-threshold method indicate that it is a promising approach for implementing digital PET data acquisition.

Method and system for biometric recognition

Abstract: High quality, high contrast images of an iris and the face of a person are acquired in rapid succession in either sequence by a single sensor and one or more illuminators, preferably within less than one second of each other, by changing the data acquisition settings or illumination settings between each acquisition.

Compressive sensor array system and method

Abstract: A compressive sensor array (CSA) system and method uses compressive sampling techniques to acquire sensor data from an array of sensors without independently sampling each of the sensor signals. In general, the CSA system and method uses the compressive sampling techniques to combine the analog sensor signals from the array of sensors into a composite sensor signal and to sample the composite sensor signal at a sub-Nyquist sampling rate. At least one embodiment of the CSA system and method allows a single analog-to-digital converter (ADC) and single RF demodulation chain to be used for an arbitrary number of sensors, thereby providing scalability and eliminating redundant data acquisition hardware. By reducing the number of samples, the CSA system and method also facilitates the processing, storage and transmission of the sensor data.

Positioning, detection and communication system and method

Abstract: A positioning, communication, and detection system designed to provide a three dimensional location of an object, navigation tools, and bidirectional surface-to-subsurface communications, and methods of using the system. The system can include one or multiple transmitters comprising electromagnetic beacons, software defined radio receivers with an associated processing unit and data acquisition system, and magnetic antennas. The system may use theoretical calculations, scale model testing, signal processing, and sensor data.

Systems and methods for seismic data acquisition employing asynchronous, decoupled data sampling and transmission

Abstract: Systems and methods for asynchronously acquiring seismic data are described, one system comprising one or more seismic sources, a plurality of sensor modules each comprising a seismic sensor, an A/D converter for generating digitized seismic data, a digital signal processor (DSP), and a sensor module clock; a seismic data recording station; and a seismic data transmission sub-system comprising a high precision clock, the sub-system allowing transmission of at least some of the digitized seismic data to the recording station, wherein each sensor module is configured to periodically receive from the sub-system an amount of the drift of its clock relative to the high precision clock This abstract is provided to comply with rules requiring an abstract to ascertain the subject matter of the disclosure It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims 37 CFR 172(b)

A method for predicting power usage effectiveness and data center infrastructure efficiency within a real-time monitoring system

Abstract: A system for making real-time predictions about power usage efficiency (PUE) and/or data center infrastructure efficiency (DCiE) of an electrical system comprises a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system; an analytics server communicatively connected to the data acquisition component and comprising a virtual system modeling engine configured to generate predicted data output for the electrical system using a virtual system model of the electrical system, an analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, and a PUE/DCiE simulation engine configured to use the virtual system model updated based in the real-time data to forecast the PUE/DCiE.