SQL Performance Tuning is a handbook of practical solutions for busy database professionals charged with managing an organization s critically important data. Covering today s most popular and widely installed database environments, this book is an indispensable resource for managing and tuning SQL across multiple platforms.
This report presents a tutorial of fundamental array processing and beamforming theory relevant to microphone array speech processing. A microphone array consists of multiple microphones placed at different spatial locations. Built upon a knowledge of sound propagation principles, the multiple inputs can be manipulated to enhance or attenuate signals emanating from particular directions. In this way, microphone arrays provide a means of enhancing a desired signal in the presence of corrupting noise sources. Moreover, this enhancement is based purely on knowledge of the source location, and so microphone array techniques are applicable to a wide variety of noise types. Microphone arrays have great potential in practical applications of speech processing, due to their ability to provide both noise robustness and hands-free signal acquisition.
msp430The LDC1312 and LDC1314 are 2- and 4-channel,
1? Easy-to-use – minimal configuration required
12-bit inductance to digital converters (LDCs) for
? Measure up to 4 sensors with one IC
inductive sensing solutions. With multiple channels ? Multiple channels support environmental and and support for remote sensing, the LDC1312 and aging compensation LDC1314 enable the performance and reliability benefits of inductive sensing to be realized at minimal? Multi-channel remote sensing provides lowest cost and power. The products are easy to use, onlysystem cost requiring that the sensor frequency be within 1 kHz ? Pin-compatible medium and high-resolution and 10 MHz to begin sensing. The wide 1 kHz to 10 options MHz sensor frequency range also enables use of very small PCB coils, further reducing sensing– LDC1312/4: 2/4-ch 12-bit LDC solution cost and size.– LDC1612/4: 2/4-ch 28
V1.16 Win32 July 2012
- Ported to Win32 C++
- Allow multiple instances of libnids to coexist in the same process
- Incorporate unofficial patch to track established TCP connections
- Migration of calls to secure versions (i.e. strcpy to strcpy_s)
- Compiles under Visual Studio 2010 with no warnings at W4
- Linux support well and truly broken, Linux specific code removed
We consider the problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization problem and provide efficient convex
relaxations for this nonconvex optimization problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two Cramer-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
Smart Grids provide many benefits for society. Reliability, observability across the
energy distribution system and the exchange of information between devices are just
some of the features that make Smart Grids so attractive. One of the main products of
a Smart Grid is to data. The amount of data available nowadays increases fast and carries
several kinds of information. Smart metres allow engineers to perform multiple
measurements and analyse such data. For example, information about consumption,
power quality and digital protection, among others, can be extracted. However, the main
challenge in extracting information from data arises from the data quality. In fact, many
sectors of the society can benefit from such data. Hence, this information needs to be
properly stored and readily available. In this chapter, we will address the main concepts
involving Technology Information, Data Mining, Big Data and clustering for deploying
information on Smart Grids.
Smart Grids provide many benefits for society. Reliability, observability across the
energy distribution system and the exchange of information between devices are just
some of the features that make Smart Grids so attractive. One of the main products of
a Smart Grid is to data. The amount of data available nowadays increases fast and carries
several kinds of information. Smart metres allow engineers to perform multiple
measurements and analyse such data. For example, information about consumption,
power quality and digital protection, among others, can be extracted. However, the main
challenge in extracting information from data arises from the data quality. In fact, many
sectors of the society can benefit from such data. Hence, this information needs to be
properly stored and readily available. In this chapter, we will address the main concepts
involving Technology Information, Data Mining, Big Data and clustering for deploying
information on Smart Grids.
