xl2tpd is an implementation of the Layer 2 Tunnelling Protocol (RFC 2661).
L2TP allows you to tunnel PPP over UDP. Some ISPs use L2TP to tunnel user
sessions from dial-in servers (modem banks, ADSL DSLAMs) to back-end PPP
servers. Another important application is Virtual Private Networks where
the IPsec protocol is used to secure the L2TP connection (L2TP/IPsec,
RFC 3193). The L2TP/IPsec protocol is mainly used by Windows and
Mac OS X clients. On Linux, xl2tpd can be used in combination with IPsec
implementations such as Openswan.
This taglib contains tags used to create struts input forms, as well as other tags generally useful in the creation of HTML-based user interfaces.
Many of the tags in this tag library will throw a JspException at runtime when they are utilized incorrectly (such as when you specify an invalid combination of tag attributes). JSP allows you to declare an "error page" in the <％@ page ％> directive. If you wish to process the actual exception that caused the problem, it is passed to the error page as a request attribute under key org.apache.struts.action.EXCEPTION.
contains documents relating to improvement of adaptive beamforming using mixed norm algorithm, combination of lms with smi algorithm, sample code for implementation of lms in matlab
PseudoQ is an open source java application for creating, playing and solving SuDoku puzzles of various types. It features both a Swing GUI and command-line operation. The automatic solving of puzzles uses "smart" techniques rather than a brute force search of every possible combination.
for entropy
H = entropy(S)
this command will evaluate the entropy of S, S should be row matrix
H = entropy([X Y Z])
this command will find the joint entropy for the 3 variables
H = entropy([X,Y],[Z,W])
this will find H(X,Y/Z,W).. you can use it for any combination of joint entropies
Please validate this function before using it
The 4.0 kbit/s speech codec described in this paper is based on a
Frequency Domain Interpolative (FDI) coding technique, which
belongs to the class of prototype waveform Interpolation (PWI)
coding techniques. The codec also has an integrated voice
activity detector (VAD) and a noise reduction capability. The
input signal is subjected to LPC analysis and the prediction
residual is separated into a slowly evolving waveform (SEW) and
a rapidly evolving waveform (REW) components. The SEW
magnitude component is quantized using a hierarchical
predictive vector quantization approach. The REW magnitude is
quantized using a gain and a sub-band based shape. SEW and
REW phases are derived at the decoder using a phase model,
based on a transmitted measure of voice periodicity. The spectral
(LSP) parameters are quantized using a combination of scalar
and vector quantizers. The 4.0 kbits/s coder has an algorithmic
delay of 60 ms and an estimated floating point complexity of
21.5 MIPS. The performance of this coder has been evaluated
using in-house MOS tests under various conditions such as
background noise. channel errors, self-tandem. and DTX mode
of operation, and has been shown to be statistically equivalent to
ITU-T (3.729 8 kbps codec across all conditions tested.
The AZ1117 is a series of low dropout three-terminal regulators with a dropout of 1.15V at 1A output current.
The AZ1117 series provides current limiting and thermal shutdown. Its circuit includes a trimmed bandgap reference to assure output voltage accuracy to be within 1% for 1.5V, 1.8V, 2.5V, 2.85V, 3.3V, 5.0V and adjustable versions or 2% for 1.2V version. Current limit is trimmed to ensure specified output current and controlled short-circuit current. On-chip thermal shutdown provides protection against any combination of overload and ambient temperature that would create excessive junction temperature.
The AZ1117 has an adjustable version, that can provide the output voltage from 1.25V to 12V with only 2 external resistors.
The recent developments in full duplex (FD) commu-
nication promise doubling the capacity of cellular networks using
self interference cancellation (SIC) techniques. FD small cells
with device-to-device (D2D) communication links could achieve
the expected capacity of the future cellular networks (5G). In
this work, we consider joint scheduling and dynamic power
algorithm (DPA) for a single cell FD small cell network with
D2D links (D2DLs). We formulate the optimal user selection and
power control as a non-linear programming (NLP) optimization
problem to get the optimal user scheduling and transmission
power in a given TTI. Our numerical results show that using
DPA gives better overall throughput performance than full power
transmission algorithm (FPA). Also, simultaneous transmissions
(combination of uplink (UL), downlink (DL), and D2D occur
80% of the time thereby increasing the spectral efficiency and
network capacity
When we started thinking about writing the first edition of this book a few years ago, we had been
working together for more than five years on the borderline between propagation and signal processing.
Therefore, it is not surprising that this book deals with propagation models and design tools for MIMO
wireless communications. Yet, this book should constitute more than a simple combination of these
two domains. It hopefully conveys our integrated understanding of MIMO, which results from endless
controversial discussions on various multi-antenna related issues, as well as various interactions with
numerous colleagues. Obviously, this area of technology is so large that it is beyond our aim to cover all
aspects in details. Rather, our goal is to provide researchers, R&D engineers and graduate students with
a comprehensive coverage of radio propagation models and space–time signal processing techniques
for multi-antenna, multi-user and multi-cell networks.
This paper presents a Hidden Markov Model (HMM)-based speech
enhancement method, aiming at reducing non-stationary noise from speech
signals. The system is based on the assumption that the speech and the noise
are additive and uncorrelated. Cepstral features are used to extract statistical
information from both the speech and the noise. A-priori statistical
information is collected from long training sequences into ergodic hidden
Markov models. Given the ergodic models for the speech and the noise, a
compensated speech-noise model is created by means of parallel model
combination, using a log-normal approximation. During the compensation, the
mean of every mixture in the speech and noise model is stored. The stored
means are then used in the enhancement process to create the most likely
speech and noise power spectral distributions using the forward algorithm
combined with mixture probability. The distributions are used to generate a
Wiener filter for every observation. The paper includes a performance
evaluation of the speech enhancer for stationary as well as non-stationary
noise environment.
