Abstract: This paper investigates the performance of a speech
recognizer in an interactive voice response system for various coded
speech signals, coded by using a vector quantization technique namely
Multi Switched Split Vector Quantization Technique. The process of
recognizing the coded output can be used in Voice banking application.
The recognition technique used for the recognition of the coded speech
signals is the Hidden Markov Model technique. The spectral distortion
performance, computational complexity, and memory requirements of
Multi Switched Split Vector Quantization Technique and the
performance of the speech recognizer at various bit rates have been
computed. From results it is found that the speech recognizer is
showing better performance at 24 bits/frame and it is found that the
percentage of recognition is being varied from 100% to 93.33% for
various bit rates.
Abstract: This paper describes an efficient hardware implementation of a new technique for interfacing the data exchange between the microprocessor-based systems and the external devices. This technique, based on the use of software/hardware system and a reduced physical address, enlarges the interfacing capacity of the microprocessor-based systems, uses the Direct Memory Access (DMA) to increases the frequency of the new bus, and improves the speed of data exchange. While using this architecture in microprocessor-based system or in computer, the input of the hardware part of our system will be connected to the bus system, and the output, which is a new bus, will be connected to an external device. The new bus is composed of a data bus, a control bus and an address bus. A Xilinx Integrated Software Environment (ISE) 7.1i has been used for the programmable logic implementation.
Abstract: In this paper we propose a method for vision systems
to consistently represent functional dependencies between different
visual routines along with relational short- and long-term knowledge
about the world. Here the visual routines are bound to visual properties
of objects stored in the memory of the system. Furthermore,
the functional dependencies between the visual routines are seen
as a graph also belonging to the object-s structure. This graph is
parsed in the course of acquiring a visual property of an object to
automatically resolve the dependencies of the bound visual routines.
Using this representation, the system is able to dynamically rearrange
the processing order while keeping its functionality. Additionally, the
system is able to estimate the overall computational costs of a certain
action. We will also show that the system can efficiently use that
structure to incorporate already acquired knowledge and thus reduce
the computational demand.
Abstract: The study of human hand morphology reveals that developing an artificial hand with the capabilities of human hand is an extremely challenging task. This paper presents the development of a robotic prosthetic hand focusing on the improvement of a tendon driven mechanism towards a biomimetic prosthetic hand. The design of this prosthesis hand is geared towards achieving high level of dexterity and anthropomorphism by means of a new hybrid mechanism that integrates a miniature motor driven actuation mechanism, a Shape Memory Alloy actuated mechanism and a passive mechanical linkage. The synergy of these actuators enables the flexion-extension movement at each of the finger joints within a limited size, shape and weight constraints. Tactile sensors are integrated on the finger tips and the finger phalanges area. This prosthesis hand is developed with an exact size ratio that mimics a biological hand. Its behavior resembles the human counterpart in terms of working envelope, speed and torque, and thus resembles both the key physical features and the grasping functionality of an adult hand.
Abstract: Octree compression techniques have been used
for several years for compressing large three dimensional data
sets into homogeneous regions. This compression technique
is ideally suited to datasets which have similar values in
clusters. Oil engineers represent reservoirs as a three dimensional
grid where hydrocarbons occur naturally in clusters. This
research looks at the efficiency of storing these grids using
octree compression techniques where grid cells are broken
into active and inactive regions. Initial experiments yielded
high compression ratios as only active leaf nodes and their
ancestor, header nodes are stored as a bitstream to file on
disk. Savings in computational time and memory were possible
at decompression, as only active leaf nodes are sent to the
graphics card eliminating the need of reconstructing the original
matrix. This results in a more compact vertex table, which can
be loaded into the graphics card quicker and generating shorter
refresh delay times.
Abstract: Most of the image watermarking methods, using the properties of the human visual system (HVS), have been proposed in literature. The component of the visual threshold is usually related to either the spatial contrast sensitivity function (CSF) or the visual masking. Especially on the contrast masking, most methods have not mention to the effect near to the edge region. Since the HVS is sensitive what happens on the edge area. This paper proposes ultrasound image watermarking using the visual threshold corresponding to the HVS in which the coefficients in a DCT-block have been classified based on the texture, edge, and plain area. This classification method enables not only useful for imperceptibility when the watermark is insert into an image but also achievable a robustness of watermark detection. A comparison of the proposed method with other methods has been carried out which shown that the proposed method robusts to blockwise memoryless manipulations, and also robust against noise addition.
Abstract: In this paper we present a soft timing phase estimation (STPE) method for wireless mobile receivers operating in low signal to noise ratios (SNRs). Discrete Polyphase Matched (DPM) filters, a Log-maximum a posterior probability (MAP) and/or a Soft-output Viterbi algorithm (SOVA) are combined to derive a new timing recovery (TR) scheme. We apply this scheme to wireless cellular communication system model that comprises of a raised cosine filter (RCF), a bit-interleaved turbo-coded multi-level modulation (BITMM) scheme and the channel is assumed to be memory-less. Furthermore, no clock signals are transmitted to the receiver contrary to the classical data aided (DA) models. This new model ensures that both the bandwidth and power of the communication system is conserved. However, the computational complexity of ideal turbo synchronization is increased by 50%. Several simulation tests on bit error rate (BER) and block error rate (BLER) versus low SNR reveal that the proposed iterative soft timing recovery (ISTR) scheme outperforms the conventional schemes.
Abstract: Seemingly simple probabilities in the m-player game bingo have never been calculated. These probabilities include expected game length and the expected number of winners on a given turn. The difficulty in probabilistic analysis lies in the subtle interdependence among the m-many bingo game cards in play. In this paper, the game i got it!, a bingo variant, is considered. This variation provides enough weakening of the inter-player dependence to allow probabilistic analysis not possible for traditional bingo. The probability of winning in exactly k turns is calculated for a one-player game. Given a game of m-many players, the expected game length and tie probability are calculated. With these calculations, the game-s interesting payout scheme is considered.
Abstract: Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
Abstract: The purpose of this research is to develop a security model for voice eavesdropping protection over digital networks. The proposed model provides an encryption scheme and a personal secret key exchange between communicating parties, a so-called voice data transformation system, resulting in a real-privacy conversation. The operation of this system comprises two main steps as follows: The first one is the personal secret key exchange for using the keys in the data encryption process during conversation. The key owner could freely make his/her choice in key selection, so it is recommended that one should exchange a different key for a different conversational party, and record the key for each case into the memory provided in the client device. The next step is to set and record another personal option of encryption, either taking all frames or just partial frames, so-called the figure of 1:M. Using different personal secret keys and different sets of 1:M to different parties without the intervention of the service operator, would result in posing quite a big problem for any eavesdroppers who attempt to discover the key used during the conversation, especially in a short period of time. Thus, it is quite safe and effective to protect the case of voice eavesdropping. The results of the implementation indicate that the system can perform its function accurately as designed. In this regard, the proposed system is suitable for effective use in voice eavesdropping protection over digital networks, without any requirements to change presently existing network systems, mobile phone network and VoIP, for instance.
Abstract: In this paper the authors propose and verify an approach to control heat flow in machine tool components. Thermal deformations are a main aspect that affects the accuracy of machining. Due to goals of energy efficiency, thermal basic loads should be reduced. This leads to inhomogeneous and time variant temperature profiles. To counteract these negative consequences, material with high melting enthalpy is used as a method for thermal stabilization. The increased thermal capacity slows down the transient thermal behavior. To account for the delayed thermal equilibrium, a control mechanism for thermal flow is introduced. By varying a gap in a heat flow path the thermal resistance of an assembly can be controlled. This mechanism is evaluated in two experimental setups. First to validate the ability to control the thermal resistance and second to prove the possibility of a self-sufficient option based on the selfsensing abilities of thermal shape memory alloys.
Abstract: This paper presents a tested research concept that
implements a complex evolutionary algorithm, genetic algorithm
(GA), in a multi-microcontroller environment. Parallel Distributed
Genetic Algorithm (PDGA) is employed in adaptive beam forming
technique to reduce power usage of adaptive antenna at WCDMA
base station. Adaptive antenna has dynamic beam that requires more
advanced beam forming algorithm such as genetic algorithm which
requires heavy computation and memory space. Microcontrollers are
low resource platforms that are normally not associated with GAs,
which are typically resource intensive. The aim of this project was to
design a cooperative multiprocessor system by expanding the role of
small scale PIC microcontrollers to optimize WCDMA base station
transmitter power. Implementation results have shown that PDGA
multi-microcontroller system returned optimal transmitted power
compared to conventional GA.
Abstract: Even it has been recognized that Shape Memory
Alloys (SMA) have a significant potential for deployment actuators,
the number of applications of SMA-based actuators to the present
day is still quite small, due to the need of deep understanding of the
thermo-mechanical behavior of SMA, causing an important need for
a mathematical model able to describe all thermo-mechanical
properties of SMA by relatively simple final set of constitutive
equations. SMAs offer attractive potentials such as: reversible strains
of several percent, generation of high recovery stresses and high
power / weight ratios. The paper tries to provide an overview of the
shape memory functions and a presentation of the designed and
developed temperature control system used for a gripper actuated by
two pairs of differential SMA active springs. An experimental setup
was established, using electrical energy for actuator-s springs heating
process. As for holding the temperature of the SMA springs at certain
level for a long time was developed a control system in order to
avoid the active elements overheating.
Abstract: This paper is concerned with the delay-distributiondependent
stability criteria for bidirectional associative memory
(BAM) neural networks with time-varying delays. Based on the
Lyapunov-Krasovskii functional and stochastic analysis approach,
a delay-probability-distribution-dependent sufficient condition is derived
to achieve the globally asymptotically mean square stable of
the considered BAM neural networks. The criteria are formulated in
terms of a set of linear matrix inequalities (LMIs), which can be
checked efficiently by use of some standard numerical packages. Finally,
a numerical example and its simulation is given to demonstrate
the usefulness and effectiveness of the proposed results.
Abstract: Gradual patterns have been studied for many years as
they contain precious information. They have been integrated in
many expert systems and rule-based systems, for instance to reason
on knowledge such as “the greater the number of turns, the greater
the number of car crashes”. In many cases, this knowledge has been
considered as a rule “the greater the number of turns → the greater
the number of car crashes” Historically, works have thus been
focused on the representation of such rules, studying how implication
could be defined, especially fuzzy implication. These rules were
defined by experts who were in charge to describe the systems they
were working on in order to turn them to operate automatically. More
recently, approaches have been proposed in order to mine databases
for automatically discovering such knowledge. Several approaches
have been studied, the main scientific topics being: how to determine
what is an relevant gradual pattern, and how to discover them as
efficiently as possible (in terms of both memory and CPU usage).
However, in some cases, end-users are not interested in raw level
knowledge, and are rather interested in trends. Moreover, it may be
the case that no relevant pattern can be discovered at a low level of
granularity (e.g. city), whereas some can be discovered at a higher
level (e.g. county). In this paper, we thus extend gradual pattern
approaches in order to consider multiple level gradual patterns. For
this purpose, we consider two aggregation policies, namely
horizontal and vertical.
Abstract: We study a new technique for optimal data compression
subject to conditions of causality and different types of memory. The
technique is based on the assumption that some information about
compressed data can be obtained from a solution of the associated
problem without constraints of causality and memory. This allows
us to consider two separate problem related to compression and decompression
subject to those constraints. Their solutions are given
and the analysis of the associated errors is provided.
Abstract: An analysis is made of the flow of an incompressible viscoelastic fluid (of small memory) over a porous plate subject to suction or blowing. It is found that velocity at a point increases with increase in the elasticity in the fluid. It is also shown that wall shear stress depends only on suction and is also independent of the material of fluids. No steady solution for velocity distribution exists when there is blowing at the plate. Temperature distribution in the boundary layer is determined and it is found that temperature at a point decreases with increase in the elasticity in the fluid.
Abstract: This paper describes how the correct endian mode of
the TMS320C6713 DSK board can be identified. It also explains how
the TMS320C6713 DSK board can be used in the little endian and in
the big endian modes for assembly language programming in
particular and for signal processing in general. Similarly, it discusses
how crucially important it is for a user of the TMS320C6713 DSK
board to identify the mode of operation and then use it correctly
during the development stages of the assembly language
programming; otherwise, it will cause unnecessary confusion and
erroneous results as far as storing data into the memory and loading
data from the memory is concerned. Furthermore, it highlights and
strongly recommends to the users of the TMS320C6713 DSK board
to be aware of the availability and importance of various display
options in the Code Composer Studio (CCS) for correctly
interpreting and displaying the desired data in the memory. The
information presented in this paper will be of great importance and
interest to those practitioners and developers who wants to use the
TMS320C6713 DSK board for assembly language programming as
well as input-output signal processing manipulations. Finally,
examples that clearly illustrate the concept are presented.
Abstract: This paper introduces an adiabatic register file based
on two-phase CPAL (Complementary Pass-Transistor Adiabatic
Logic circuits) with power-gating scheme, which can operate on a
single-phase power clock. A 32×32 single-phase adiabatic register file
with power-gating scheme has been implemented with TSMC 0.18μm
CMOS technology. All the circuits except for the storage cells employ
two-phase CPAL circuits, and the storage cell is based on the
conventional memory one. The two-phase non-overlap power-clock
generator with power-gating scheme is used to supply the proposed
adiabatic register file. Full-custom layouts are drawn. The energy and
functional simulations have been performed using the net-list
extracted from their layouts. Compared with the traditional static
CMOS register file, HSPICE simulations show that the proposed
adiabatic register file can work very well, and it attains about 73%
energy savings at 100 MHz.