In this paper, we address the speaker independent
recognition of Chinese number speeches 0~9 based on HMM.
Our former results of inside and outside testing achieved
92.5% and 76.79% respectively. To improve further the
performance, two important features of speech; MFCC and
cluster number of vector quantification, are unified together
and evaluated on various values. The best performance
achieve 96.2% and 83.1% on MFCC Number = 20 and VQ
clustering number = 64.
This document summarizes a research paper on advanced speaker recognition using hidden Markov models. It begins with an abstract that outlines using discrete wavelet transforms to reduce noise in speech signals before extracting mel frequency cepstral coefficients features and applying vector quantization and hidden Markov models for recognition. The document then provides more detail on the speaker recognition system and methods used, including denoising with discrete wavelet transforms, feature extraction with MFCCs, vector quantization to normalize feature size, training hidden Markov models, and using the Viterbi algorithm during testing to determine the recognized speaker. The research achieved 98% recognition accuracy on a database of 50 speakers in a normal noise environment.
Realization and design of a pilot assist decision making system based on spee...csandit
A system based on speech recognition is proposed fo
r pilot assist decision-making. It is based
on a HIL aircraft simulation platform and uses the
microcontroller SPCE061A as the central
processor to achieve better reliability and higher
cost-effect performance. Technologies of
LPCC (linear predictive cepstral coding) and DTW (D
ynamic Time Warping) are applied for
isolated-word speech recognition to gain a smaller
amount of calculation and a better real-time
performance. Besides, we adopt the PWM (Pulse Width
Modulation) regulation technology to
effectively regulate each control surface by speech
, and thus to assist the pilot to make decisions.
By trial and error, it is proved that we have a sat
isfactory accuracy rate of speech recognition
and control effect. More importantly, our paper pro
vides a creative idea for intelligent human-
computer interaction and applications of speech rec
ognition in the field of aviation control. Our
system is also very easy to be extended and applied
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Speaker recognition systems aim to automatically identify or verify a speaker's identity based on characteristics of their voice. There are two main types: speaker identification determines which registered speaker is speaking, while speaker verification accepts or rejects a speaker's claimed identity. All systems contain modules for feature extraction and feature matching. Feature extraction represents the voice signal with parameters like MFCCs that can distinguish speakers. Feature matching compares extracted features from an unknown voice to known speaker models. The document describes the process of MFCC feature extraction in detail, including framing the speech signal, windowing frames, taking the FFT, mapping to the mel scale, and finally the DCT to produce MFCC coefficients.
Speaker identification using mel frequency Phan Duy
This document summarizes a paper that presents a speaker identification system using Mel Frequency Cepstral Coefficients (MFCCs). MFCCs are used to extract features from speech signals that are less susceptible to variations between recordings of the same speaker. Vector quantization is then used to compress the extracted features for matching against enrolled speaker models. The system contains modules for feature extraction using MFCCs and feature matching, which are the two main components of all speaker recognition systems.
A New Method for Pitch Tracking and Voicing Decision Based on Spectral Multi-...CSCJournals
This paper proposes a new voicing detection and pitch estimation method that is particularly robust for noisy speech. This method is based on the spectral analysis of the speech multi-scale product. The multi-scale product (MP) consists of making the product of wavelet transform coefficients. The wavelet used is the quadratic spline function. We argue that the spectral of Multi-scale Product Analysis is capable of revealing an estimate of a pitch-harmonic more accurately even in a heavy noisy scenario. We evaluate our approach on the Keele database. The experimental results show the robustness of our method for noisy speech, and the good performance for clean speech in comparison with state-of-the-art algorithms.
Text-Independent Speaker Verification ReportCody Ray
Provides an introduction to the task of speaker recognition, and describes a not-so-novel speaker recognition system based upon a minimum-distance classification scheme. We describe both the theory and practical details for a reference implementation. Furthermore, we discuss an advanced technique for classification based upon Gaussian Mixture Models (GMM). Finally, we discuss the results of a set of experiments performed using our reference implementation.
This document summarizes a research paper on advanced speaker recognition using hidden Markov models. It begins with an abstract that outlines using discrete wavelet transforms to reduce noise in speech signals before extracting mel frequency cepstral coefficients features and applying vector quantization and hidden Markov models for recognition. The document then provides more detail on the speaker recognition system and methods used, including denoising with discrete wavelet transforms, feature extraction with MFCCs, vector quantization to normalize feature size, training hidden Markov models, and using the Viterbi algorithm during testing to determine the recognized speaker. The research achieved 98% recognition accuracy on a database of 50 speakers in a normal noise environment.
Realization and design of a pilot assist decision making system based on spee...csandit
A system based on speech recognition is proposed fo
r pilot assist decision-making. It is based
on a HIL aircraft simulation platform and uses the
microcontroller SPCE061A as the central
processor to achieve better reliability and higher
cost-effect performance. Technologies of
LPCC (linear predictive cepstral coding) and DTW (D
ynamic Time Warping) are applied for
isolated-word speech recognition to gain a smaller
amount of calculation and a better real-time
performance. Besides, we adopt the PWM (Pulse Width
Modulation) regulation technology to
effectively regulate each control surface by speech
, and thus to assist the pilot to make decisions.
By trial and error, it is proved that we have a sat
isfactory accuracy rate of speech recognition
and control effect. More importantly, our paper pro
vides a creative idea for intelligent human-
computer interaction and applications of speech rec
ognition in the field of aviation control. Our
system is also very easy to be extended and applied
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Speaker recognition systems aim to automatically identify or verify a speaker's identity based on characteristics of their voice. There are two main types: speaker identification determines which registered speaker is speaking, while speaker verification accepts or rejects a speaker's claimed identity. All systems contain modules for feature extraction and feature matching. Feature extraction represents the voice signal with parameters like MFCCs that can distinguish speakers. Feature matching compares extracted features from an unknown voice to known speaker models. The document describes the process of MFCC feature extraction in detail, including framing the speech signal, windowing frames, taking the FFT, mapping to the mel scale, and finally the DCT to produce MFCC coefficients.
Speaker identification using mel frequency Phan Duy
This document summarizes a paper that presents a speaker identification system using Mel Frequency Cepstral Coefficients (MFCCs). MFCCs are used to extract features from speech signals that are less susceptible to variations between recordings of the same speaker. Vector quantization is then used to compress the extracted features for matching against enrolled speaker models. The system contains modules for feature extraction using MFCCs and feature matching, which are the two main components of all speaker recognition systems.
A New Method for Pitch Tracking and Voicing Decision Based on Spectral Multi-...CSCJournals
This paper proposes a new voicing detection and pitch estimation method that is particularly robust for noisy speech. This method is based on the spectral analysis of the speech multi-scale product. The multi-scale product (MP) consists of making the product of wavelet transform coefficients. The wavelet used is the quadratic spline function. We argue that the spectral of Multi-scale Product Analysis is capable of revealing an estimate of a pitch-harmonic more accurately even in a heavy noisy scenario. We evaluate our approach on the Keele database. The experimental results show the robustness of our method for noisy speech, and the good performance for clean speech in comparison with state-of-the-art algorithms.
Text-Independent Speaker Verification ReportCody Ray
Provides an introduction to the task of speaker recognition, and describes a not-so-novel speaker recognition system based upon a minimum-distance classification scheme. We describe both the theory and practical details for a reference implementation. Furthermore, we discuss an advanced technique for classification based upon Gaussian Mixture Models (GMM). Finally, we discuss the results of a set of experiments performed using our reference implementation.
This document presents research on emotion recognition from speech using a combination of MFCC and LPCC features with support vector machine (SVM) classification. Two databases were used: the Berlin Emotional Database and SAVEE database. MFCC and LPCC features were extracted from the speech samples and combined. SVM with radial basis function kernel achieved the highest accuracy of 88.59% for emotion recognition on the Berlin database using the combined features. Confusion matrices are presented to evaluate performance on each database.
This document discusses using deep neural networks for speech enhancement by finding a mapping between noisy and clean speech signals. It aims to handle a wide range of noises by using a large training dataset with many noise/speech combinations. Techniques like global variance equalization and dropout are used to improve generalization. Experimental results show improvements over MMSE techniques, with the ability to suppress nonstationary noise and avoid musical artifacts. The introduction provides background on speech enhancement, recognition using HMMs and other models, and the role of deep learning advances.
This document discusses polyphase filters and their applications. It begins with an introduction to polyphase filters and how they are used for decimation and interpolation in digital signal processing applications. Next, it describes active and passive polyphase filter implementations and their advantages. Finally, it discusses some example applications of polyphase filters, including predistortion for linearization of nonlinear systems and nonlinear echo cancellation using Volterra filters.
Speaker Recognition System using MFCC and Vector Quantization Approachijsrd.com
This paper presents an approach to speaker recognition using frequency spectral information with Mel frequency for the improvement of speech feature representation in a Vector Quantization codebook based recognition approach. The Mel frequency approach extracts the features of the speech signal to get the training and testing vectors. The VQ Codebook approach uses training vectors to form clusters and recognize accurately with the help of LBG algorithm.
Designing an Efficient Multimodal Biometric System using Palmprint and Speech...IDES Editor
This document summarizes a research paper that proposes a multimodal biometric system using palmprint and speech signals. It extracts features from each modality using different methods. For speech, it uses Subband Cepstral Coefficients extracted via a wavelet packet transform. For palmprint, it uses a Modified Canonical Form method. The features are fused at the score level using a weighted sum rule. The system is tested on a database of over 300 subjects, and results show improved recognition rates compared to single modalities.
This document describes how to build a simple automatic speaker recognition system. It discusses the principles of speaker recognition, which can be identification (determining which registered speaker is speaking) or verification (accepting or rejecting a speaker's claimed identity). The key components are feature extraction and feature matching. Feature extraction converts the speech waveform into features using techniques like MFCC. Feature matching then compares the extracted features to stored reference models to identify the speaker. The document focuses on the speech feature extraction process, which involves framing the speech signal, windowing frames, taking the FFT, and calculating MFCCs to characterize the signal in a way that mimics human hearing.
This document discusses automated verification of security policies in mobile code. It motivates the need for formal methods to specify and verify mobile distributed systems focusing on security issues. The objectives are to expressively specify mobile systems with explicit thread locations and location networks, and formally analyze mobile systems through model checking of security and safety properties. The outline presents preliminaries on mobile code languages and verification techniques like software model checking. It proposes modeling mobile systems with location-aware threads as labeled Kripke structures. The framework allows specifying security policies and model checking programs to verify properties in an abstraction-based manner.
Presentation slides discussing the theory and empirical results of a text-independent speaker verification system I developed based upon classification of MFCCs. Both mininimum-distance classification and least-likelihood ratio classification using Gaussian Mixture Models were discussed.
Speaker and Speech Recognition for Secured Smart Home ApplicationsRoger Gomes
The paper published in discusses implementation of a robust text-independent speaker recognition system using MFCC extraction of feature vectors its matching using VQ and optimization using LBG, further a text dependent speech recognition system using the DTW algorithm's implementation is discussed in the context of home automation.
Ber performance analysis of mimo systems using equalizationAlexander Decker
The document discusses equalization techniques for analyzing bit error rate (BER) performance in multiple-input multiple-output (MIMO) systems. It analyzes different equalization techniques like zero forcing (ZF), minimum mean squared error (MMSE), ZF with successive interference cancellation (ZF-SIC), MMSE with SIC (MMSE-SIC), maximum likelihood (ML) and sphere decoding. Simulation results show that successive interference methods outperform ZF and MMSE, but have higher complexity. ML provides better performance than others, while sphere decoding gives the best performance but highest complexity compared to ML.
Performance analysis of image compression using fuzzy logic algorithmsipij
With the increase in demand, product of multimedia is increasing fast and thus contributes to insufficient
network bandwidth and memory storage. Therefore image compression is more significant for reducing
data redundancy for save more memory and transmission bandwidth. An efficient compression technique
has been proposed which combines fuzzy logic with that of Huffman coding. While normalizing image
pixel, each value of pixel image belonging to that image foreground are characterized and interpreted. The
image is sub divided into pixel which is then characterized by a pair of set of approximation. Here
encoding represent Huffman code which is statistically independent to produce more efficient code for
compression and decoding represents rough fuzzy logic which is used to rebuilt the pixel of image. The
method used here are rough fuzzy logic with Huffman coding algorithm (RFHA). Here comparison of
different compression techniques with Huffman coding is done and fuzzy logic is applied on the Huffman
reconstructed image. Result shows that high compression rates are achieved and visually negligible
difference between compressed images and original images
CURVELET BASED SPEECH RECOGNITION SYSTEM IN NOISY ENVIRONMENT: A STATISTICAL ...ijcsit
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of
speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech
recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
HGS-Assisted Detection Algorithm for 4G and Beyond Wireless Mobile Communicat...Rosdiadee Nordin
This document presents a novel hybrid algorithm called HGS for detecting transmitted data in 4G and beyond mobile communication systems. The HGS algorithm uses metaheuristic approaches to achieve optimal detection performance with low complexity. It aims to develop adaptive detection methods that have faster convergence speeds and require fewer parameters over time-varying MIMO channels. Simulation results show the HGS algorithm achieves better bit error rate than other detection methods like MMSE-MUD and GACE, with lower computational complexity. Future work could involve applying the metaheuristic approaches to systems using interleaved division multiple access or independent component analysis.
This document summarizes a research paper that proposes a method for emotion identification in continuous speech using cepstral analysis and generalized gamma mixture modeling. The key contributions are:
1) It extracts MFCC and LPC features from speech signals to model emotions like happy, angry, boredom and sad.
2) It uses a generalized gamma distribution instead of GMM for more accurate feature extraction and classification, as GGD can model speech signal variations better.
3) An experiment is conducted on a database of 50 speakers' speech in 5 emotions, achieving over 90% recognition accuracy using the proposed MFCC-LPC features and GGD modeling.
Environmental Sound detection Using MFCC techniquePankaj Kumar
This document describes a project to develop an environmental sound detection and classification technique using Mel Frequency Cepstral Coefficients (MFCC) and Content Based Retrieval (CBR). The methodology involves extracting features from input sounds, clustering similar sounds, and finding matches for query sounds from the clusters. The technique was able to accurately recognize sounds already in the database, but had difficulty rejecting sounds not present. Potential applications include environmental monitoring, speaker recognition, and robot awareness. The technique shows promise but could be improved by using additional sound features and clustering.
Text independent speaker recognition systemDeepesh Lekhak
This document outlines a project to develop a text-independent speaker recognition system. It lists the project members and provides an overview of the presentation sections, which include the system architecture, methodology, results and analysis, and applications. The methodology section describes implementing the system in MATLAB, including voice capturing, pre-processing, MFCC feature extraction, GMM matching, and identification/verification. It also outlines implementing the system on an FPGA, including analog conversion, storage, framing, FFT, mel spectrum, MFCC extraction, and UART transmission to MATLAB for further processing. The results show over 99% recognition accuracy with longer training and test data.
A Dynamic MAC Protocol for WCDMA Wireless Multimedia NetworksIDES Editor
Existing MAC protocols like TDMA and 802.11
have many disadvantages for scheduling multimedia traffic in
CDMA wireless networks. Our objective is to develop a
dynamic MAC protocol for WCDMA networks to avoid
congestion and improve the channel utilization and
throughput of the bulky real-time flows. In this paper, we
propose to develop a dynamic MAC protocol for wireless
multimedia networks. In the design, we combine the merits of
the CSMA, TDMA MAC protocols with WCDMA systems to
improve the throughput of the multimedia WLAN in a
cellular environment. We use these MAC protocols
adaptively, to handle both the low and high data traffics of the
mobile users. It uses multiple slots per frame allowing
multiple users to transmit simultaneously using their own
CDMA codes. By simulation results, we show that our
proposed MAC protocol achieves high channel utilization and
improved throughput with reduced average delay under low
and high data traffic.
Human face detection is a significant problem of
image processing and is usually a first step for face
recognition and visual surveillance. This paper presents the
details of face detection approach that is implemented to
achieve accurate face detection in group color images which
are based on facial feature and Support Vector Machine. In
the first step, the proposed approach quickly separates skin
color regions from the background and from non-skin color
regions using YCbCr color space transformation. After the
detection of skin regions, the images are processed with,
wavelet transforms (WT) and discrete cosine transforms
(DCT) as a result of which the 30×30 pixel sub images are
found. These sub images are then assigned to SVM classifier
as an input. The SVM is used to classify non-face regions from
the remaining regions more accurately, that are obtained
from previous steps and having big difference between faces
regions and non-faces regions. The experimental results on
different types of group color images show that this approach
improves the detection speed and minimizes the false
detection rate in less time and detects faces in different color
images.
A Quality of Service Strategy to Optimize Bandwidth Utilization in Mobile Net...IDES Editor
The mobile network that supports network mobility
is an emerging technology. It is also referred as NEMO
(NEtwork MObility). It is more appropriate for mobile
platforms such as car, bus, train, air plane, etc. It is a great
challenge to provide Quality of Service (QoS) in NEMO. QoS
is a set of service requirements to be met by the network.
There are various parameters by which QoS is provided. This
paper concentrates on providing optimum bandwidth for data
traffic. The objective of this paper is to propose a strategy to
use Virtual Circuit (VC) approach in NEMO. It helps to
utilize the bandwidth effectively, to consume minimum time to
transfer the data and also to reduce overload of the mobile
router due to the minimum size of the header. Ultimately, it
gives better results to enhance the QoS in mobile networks.
Towards a Software Framework for Automatic Business Process RedesignIDES Editor
A key element to the success of any organization is
the ability to continuously improve its business process
performance. Efficient Business Process Redesign (BPR)
methodologies are needed to allow organizations to face the
changing business conditions. For a long time, practices for
BPR were done case-by-case and were based on the insights
and knowledge of an expert to the organization. It can be
argued that efficiency, however, can further be achieved with
the support of automatic process redesign tools which are few
at the moment. Process mining as a recent approach allows
for the extraction of information from event logs recorded in
different information systems. In this paper we argue that
results driven by process mining techniques can be used to
capture the various types of inefficiencies in the organization
and hence propose efficient redesigns of its business model.
We first give an outline on the current directions towards
automatic BPR followed by a review on the different process
mining techniques and its usage in different applications.
Then, a specific framework of a Software tool that uses process
mining to support automatic BPR is presented.
Different Attacks on Selective Encryption in RSA based Singular Cubic Curve w...IDES Editor
In this paper, the security of Selective Encryptionin
RSA based Singular Cubic Curve with Automatic Variable Key
(AVK) for some well known attacks are analysed. It is proved
that this cryptosystem is more secure than Koyama scheme
from which the algorithm has been generated. The proposed
cryptographic algorithm makes justified use of Koyama
Schemes. Koyama scheme is not semantically secure. The
proposed Scheme is efficient and semantically secure public
key cryptosystem based on Singular Cubic Curve with AVK.
Further, the partially known attacks, linearly related plain text
attacks, isomorphism attacks, low exponent attacks, Wiener’s
attack and Hastad’s attack are analyzed for effect with the
proposed scheme. The Selective Encryption in RSA based
Singular Cubic Curve with AVK for text based documents is
found to be robust enough to encounter all these attacks.
This document presents research on emotion recognition from speech using a combination of MFCC and LPCC features with support vector machine (SVM) classification. Two databases were used: the Berlin Emotional Database and SAVEE database. MFCC and LPCC features were extracted from the speech samples and combined. SVM with radial basis function kernel achieved the highest accuracy of 88.59% for emotion recognition on the Berlin database using the combined features. Confusion matrices are presented to evaluate performance on each database.
This document discusses using deep neural networks for speech enhancement by finding a mapping between noisy and clean speech signals. It aims to handle a wide range of noises by using a large training dataset with many noise/speech combinations. Techniques like global variance equalization and dropout are used to improve generalization. Experimental results show improvements over MMSE techniques, with the ability to suppress nonstationary noise and avoid musical artifacts. The introduction provides background on speech enhancement, recognition using HMMs and other models, and the role of deep learning advances.
This document discusses polyphase filters and their applications. It begins with an introduction to polyphase filters and how they are used for decimation and interpolation in digital signal processing applications. Next, it describes active and passive polyphase filter implementations and their advantages. Finally, it discusses some example applications of polyphase filters, including predistortion for linearization of nonlinear systems and nonlinear echo cancellation using Volterra filters.
Speaker Recognition System using MFCC and Vector Quantization Approachijsrd.com
This paper presents an approach to speaker recognition using frequency spectral information with Mel frequency for the improvement of speech feature representation in a Vector Quantization codebook based recognition approach. The Mel frequency approach extracts the features of the speech signal to get the training and testing vectors. The VQ Codebook approach uses training vectors to form clusters and recognize accurately with the help of LBG algorithm.
Designing an Efficient Multimodal Biometric System using Palmprint and Speech...IDES Editor
This document summarizes a research paper that proposes a multimodal biometric system using palmprint and speech signals. It extracts features from each modality using different methods. For speech, it uses Subband Cepstral Coefficients extracted via a wavelet packet transform. For palmprint, it uses a Modified Canonical Form method. The features are fused at the score level using a weighted sum rule. The system is tested on a database of over 300 subjects, and results show improved recognition rates compared to single modalities.
This document describes how to build a simple automatic speaker recognition system. It discusses the principles of speaker recognition, which can be identification (determining which registered speaker is speaking) or verification (accepting or rejecting a speaker's claimed identity). The key components are feature extraction and feature matching. Feature extraction converts the speech waveform into features using techniques like MFCC. Feature matching then compares the extracted features to stored reference models to identify the speaker. The document focuses on the speech feature extraction process, which involves framing the speech signal, windowing frames, taking the FFT, and calculating MFCCs to characterize the signal in a way that mimics human hearing.
This document discusses automated verification of security policies in mobile code. It motivates the need for formal methods to specify and verify mobile distributed systems focusing on security issues. The objectives are to expressively specify mobile systems with explicit thread locations and location networks, and formally analyze mobile systems through model checking of security and safety properties. The outline presents preliminaries on mobile code languages and verification techniques like software model checking. It proposes modeling mobile systems with location-aware threads as labeled Kripke structures. The framework allows specifying security policies and model checking programs to verify properties in an abstraction-based manner.
Presentation slides discussing the theory and empirical results of a text-independent speaker verification system I developed based upon classification of MFCCs. Both mininimum-distance classification and least-likelihood ratio classification using Gaussian Mixture Models were discussed.
Speaker and Speech Recognition for Secured Smart Home ApplicationsRoger Gomes
The paper published in discusses implementation of a robust text-independent speaker recognition system using MFCC extraction of feature vectors its matching using VQ and optimization using LBG, further a text dependent speech recognition system using the DTW algorithm's implementation is discussed in the context of home automation.
Ber performance analysis of mimo systems using equalizationAlexander Decker
The document discusses equalization techniques for analyzing bit error rate (BER) performance in multiple-input multiple-output (MIMO) systems. It analyzes different equalization techniques like zero forcing (ZF), minimum mean squared error (MMSE), ZF with successive interference cancellation (ZF-SIC), MMSE with SIC (MMSE-SIC), maximum likelihood (ML) and sphere decoding. Simulation results show that successive interference methods outperform ZF and MMSE, but have higher complexity. ML provides better performance than others, while sphere decoding gives the best performance but highest complexity compared to ML.
Performance analysis of image compression using fuzzy logic algorithmsipij
With the increase in demand, product of multimedia is increasing fast and thus contributes to insufficient
network bandwidth and memory storage. Therefore image compression is more significant for reducing
data redundancy for save more memory and transmission bandwidth. An efficient compression technique
has been proposed which combines fuzzy logic with that of Huffman coding. While normalizing image
pixel, each value of pixel image belonging to that image foreground are characterized and interpreted. The
image is sub divided into pixel which is then characterized by a pair of set of approximation. Here
encoding represent Huffman code which is statistically independent to produce more efficient code for
compression and decoding represents rough fuzzy logic which is used to rebuilt the pixel of image. The
method used here are rough fuzzy logic with Huffman coding algorithm (RFHA). Here comparison of
different compression techniques with Huffman coding is done and fuzzy logic is applied on the Huffman
reconstructed image. Result shows that high compression rates are achieved and visually negligible
difference between compressed images and original images
CURVELET BASED SPEECH RECOGNITION SYSTEM IN NOISY ENVIRONMENT: A STATISTICAL ...ijcsit
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of
speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech
recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
HGS-Assisted Detection Algorithm for 4G and Beyond Wireless Mobile Communicat...Rosdiadee Nordin
This document presents a novel hybrid algorithm called HGS for detecting transmitted data in 4G and beyond mobile communication systems. The HGS algorithm uses metaheuristic approaches to achieve optimal detection performance with low complexity. It aims to develop adaptive detection methods that have faster convergence speeds and require fewer parameters over time-varying MIMO channels. Simulation results show the HGS algorithm achieves better bit error rate than other detection methods like MMSE-MUD and GACE, with lower computational complexity. Future work could involve applying the metaheuristic approaches to systems using interleaved division multiple access or independent component analysis.
This document summarizes a research paper that proposes a method for emotion identification in continuous speech using cepstral analysis and generalized gamma mixture modeling. The key contributions are:
1) It extracts MFCC and LPC features from speech signals to model emotions like happy, angry, boredom and sad.
2) It uses a generalized gamma distribution instead of GMM for more accurate feature extraction and classification, as GGD can model speech signal variations better.
3) An experiment is conducted on a database of 50 speakers' speech in 5 emotions, achieving over 90% recognition accuracy using the proposed MFCC-LPC features and GGD modeling.
Environmental Sound detection Using MFCC techniquePankaj Kumar
This document describes a project to develop an environmental sound detection and classification technique using Mel Frequency Cepstral Coefficients (MFCC) and Content Based Retrieval (CBR). The methodology involves extracting features from input sounds, clustering similar sounds, and finding matches for query sounds from the clusters. The technique was able to accurately recognize sounds already in the database, but had difficulty rejecting sounds not present. Potential applications include environmental monitoring, speaker recognition, and robot awareness. The technique shows promise but could be improved by using additional sound features and clustering.
Text independent speaker recognition systemDeepesh Lekhak
This document outlines a project to develop a text-independent speaker recognition system. It lists the project members and provides an overview of the presentation sections, which include the system architecture, methodology, results and analysis, and applications. The methodology section describes implementing the system in MATLAB, including voice capturing, pre-processing, MFCC feature extraction, GMM matching, and identification/verification. It also outlines implementing the system on an FPGA, including analog conversion, storage, framing, FFT, mel spectrum, MFCC extraction, and UART transmission to MATLAB for further processing. The results show over 99% recognition accuracy with longer training and test data.
A Dynamic MAC Protocol for WCDMA Wireless Multimedia NetworksIDES Editor
Existing MAC protocols like TDMA and 802.11
have many disadvantages for scheduling multimedia traffic in
CDMA wireless networks. Our objective is to develop a
dynamic MAC protocol for WCDMA networks to avoid
congestion and improve the channel utilization and
throughput of the bulky real-time flows. In this paper, we
propose to develop a dynamic MAC protocol for wireless
multimedia networks. In the design, we combine the merits of
the CSMA, TDMA MAC protocols with WCDMA systems to
improve the throughput of the multimedia WLAN in a
cellular environment. We use these MAC protocols
adaptively, to handle both the low and high data traffics of the
mobile users. It uses multiple slots per frame allowing
multiple users to transmit simultaneously using their own
CDMA codes. By simulation results, we show that our
proposed MAC protocol achieves high channel utilization and
improved throughput with reduced average delay under low
and high data traffic.
Human face detection is a significant problem of
image processing and is usually a first step for face
recognition and visual surveillance. This paper presents the
details of face detection approach that is implemented to
achieve accurate face detection in group color images which
are based on facial feature and Support Vector Machine. In
the first step, the proposed approach quickly separates skin
color regions from the background and from non-skin color
regions using YCbCr color space transformation. After the
detection of skin regions, the images are processed with,
wavelet transforms (WT) and discrete cosine transforms
(DCT) as a result of which the 30×30 pixel sub images are
found. These sub images are then assigned to SVM classifier
as an input. The SVM is used to classify non-face regions from
the remaining regions more accurately, that are obtained
from previous steps and having big difference between faces
regions and non-faces regions. The experimental results on
different types of group color images show that this approach
improves the detection speed and minimizes the false
detection rate in less time and detects faces in different color
images.
A Quality of Service Strategy to Optimize Bandwidth Utilization in Mobile Net...IDES Editor
The mobile network that supports network mobility
is an emerging technology. It is also referred as NEMO
(NEtwork MObility). It is more appropriate for mobile
platforms such as car, bus, train, air plane, etc. It is a great
challenge to provide Quality of Service (QoS) in NEMO. QoS
is a set of service requirements to be met by the network.
There are various parameters by which QoS is provided. This
paper concentrates on providing optimum bandwidth for data
traffic. The objective of this paper is to propose a strategy to
use Virtual Circuit (VC) approach in NEMO. It helps to
utilize the bandwidth effectively, to consume minimum time to
transfer the data and also to reduce overload of the mobile
router due to the minimum size of the header. Ultimately, it
gives better results to enhance the QoS in mobile networks.
Towards a Software Framework for Automatic Business Process RedesignIDES Editor
A key element to the success of any organization is
the ability to continuously improve its business process
performance. Efficient Business Process Redesign (BPR)
methodologies are needed to allow organizations to face the
changing business conditions. For a long time, practices for
BPR were done case-by-case and were based on the insights
and knowledge of an expert to the organization. It can be
argued that efficiency, however, can further be achieved with
the support of automatic process redesign tools which are few
at the moment. Process mining as a recent approach allows
for the extraction of information from event logs recorded in
different information systems. In this paper we argue that
results driven by process mining techniques can be used to
capture the various types of inefficiencies in the organization
and hence propose efficient redesigns of its business model.
We first give an outline on the current directions towards
automatic BPR followed by a review on the different process
mining techniques and its usage in different applications.
Then, a specific framework of a Software tool that uses process
mining to support automatic BPR is presented.
Different Attacks on Selective Encryption in RSA based Singular Cubic Curve w...IDES Editor
In this paper, the security of Selective Encryptionin
RSA based Singular Cubic Curve with Automatic Variable Key
(AVK) for some well known attacks are analysed. It is proved
that this cryptosystem is more secure than Koyama scheme
from which the algorithm has been generated. The proposed
cryptographic algorithm makes justified use of Koyama
Schemes. Koyama scheme is not semantically secure. The
proposed Scheme is efficient and semantically secure public
key cryptosystem based on Singular Cubic Curve with AVK.
Further, the partially known attacks, linearly related plain text
attacks, isomorphism attacks, low exponent attacks, Wiener’s
attack and Hastad’s attack are analyzed for effect with the
proposed scheme. The Selective Encryption in RSA based
Singular Cubic Curve with AVK for text based documents is
found to be robust enough to encounter all these attacks.
Detection of Carotid Artery from Pre-Processed Magnetic Resonance AngiogramIDES Editor
Boundary detection is playing an important role in
the medical image analysis. In certain cases it becomes very
difficult for the doctors to assess the carotid arteries from the
magnetic resonance angiography (MRA) of the neck. In this
paper an attempt has been made to detect carotid arteries
from the neck magnetic resonance angiograms, so as to
overcome such difficulties. The algorithm pre-processes the
magnetic resonance angiograms and subsequently detects the
carotid artery. Stenosis is expected to reduce the diameter of
the vessel. The diameter can be measured from the vasculature
detected image. As the algorithm successfully detects the
carotid artery from the neck magnetic resonance angiograms,
therefore it will help doctors for diagnosis and serve as a step
in the prevention of cardiovascular diseases.
Using PageRank Algorithm to Improve Coupling MetricsIDES Editor
Existing coupling metrics only use the number of
methods invocations, and does not consider the weight of the
methods. Thus, they cannot measure coupling metrics
accurately. In this paper, we measure the weight of methods
using PageRank algorithm, and propose a new approach to
improve coupling metrics using the weight. We validate the
proposed approach by applying them to several open source
projects. And we measure several coupling metrics using
existing approach and proposed approach. As a result, the
correlation between change-proneness and improved coupling
metrics were significantly higher than existing coupling
metrics. Hence, our improved coupling metrics can more
accurately measure software.
Modified Epc Global Network Architecture of Internet of Things for High Load ...IDES Editor
This paper proposes a flexible and novel
architecture of Internet of Things (IOT) in a high density and
mobility environment. Our proposed architecture solves the
problem of over-loading on the network by monitoring the
total number of changed objects changing global location
crossing the fringe boundaries rather than the actual number
of objects present or those that move within the local area. We
have modified the reader architecture of the EPCglobal
Architecture. The components and the working of the model
has been illustrated in detail. We have also discussed the
physical implementation of our model taking the examples of
a smart home sample application and the performance results
have been tabulated and represented graphically.
Power System State Estimation - A ReviewIDES Editor
This document provides a review of power system state estimation techniques. It discusses both static and dynamic state estimation algorithms. For static state estimation, it covers weighted least squares, decoupled, and robust estimation methods. Weighted least squares is commonly used but can have numerical instability issues. Decoupled state estimation approximates the gain matrix for faster computation. Robust estimation uses M-estimators and other techniques to handle outliers and bad data. Dynamic state estimation applies Kalman filtering, leapfrog algorithms, and other methods to continuously monitor system states over time.
An Effective Approach for Chinese Speech Recognition on Small Size of Vocabularysipij
This document summarizes an approach for Chinese speech recognition using small vocabularies. It proposes recognizing Chinese words independently based on Hidden Markov Models (HMMs). Speech samples from 8 speakers recorded Chinese characters segmented into sub-syllables to generate features. Preliminary testing of the HMM approach achieved 89.6% accuracy within the system and 77.5% for outside testing. Applying a keyword spotting criterion improved accuracy to 92.7% within the system and 83.8% for outside testing, demonstrating the effectiveness of the approach for small vocabulary Chinese speech recognition.
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition TechniqueCSCJournals
Automatic speaker recognition system is used to recognize an unknown speaker among several reference speakers by making use of speaker-specific information from their speech. In this paper, we introduce a novel, hierarchical, text-independent speaker recognition. Our baseline speaker recognition system accuracy, built using statistical modeling techniques, gives an accuracy of 81% on the standard MIT database and our baseline gender recognition system gives an accuracy of 93.795%. We then propose and implement a novel state-space pruning technique by performing gender recognition before speaker recognition so as to improve the accuracy/timeliness of our baseline speaker recognition system. Based on the experiments conducted on the MIT database, we demonstrate that our proposed system improves the accuracy over the baseline system by approximately 2%, while reducing the computational time by more than 30%.
Emotion Recognition Based On Audio SpeechIOSR Journals
This document summarizes a research paper on emotion recognition based on audio speech. It discusses how acoustic features are extracted from speech signals by applying preprocessing techniques like preemphasis and framing. It describes extracting features like Mel frequency cepstral coefficients (MFCCs) that capture characteristics of the vocal tract. Support vector machines (SVMs) are used as pattern classification methods to build models for each emotion and compare test speech features to recognize emotions. The paper confirms the advantage of its audio-based emotion recognition approach through experimental results and discusses potential improvements and future work on increasing efficiency and recognizing emotion intensity.
IRJET- Emotion recognition using Speech Signal: A ReviewIRJET Journal
This document provides a review of speech emotion recognition techniques. It discusses how speech emotion recognition systems work, including common features extracted from speech like MFCCs and LPC coefficients. Classification techniques used in these systems are also examined, such as DTW, ANN, GMM, and K-NN. The document concludes that speech emotion recognition could be useful for applications requiring natural human-computer interaction, like car systems that monitor driver emotion or educational tutorials that adapt based on student emotion.
This paper presents a speaker identification system using Mel Frequency Cepstral Coefficients (MFCCs) for feature extraction and vector quantization. MFCCs capture important characteristics of speech in the mel frequency scale, which is closer to human perception. Vector quantization clusters similar acoustic vectors into codewords to minimize data size. Testing on 21 speakers achieved 100% identification rate using MFCCs, hamming window, and a codebook size of 64 vectors. Identification rate increased with larger codebooks and the mel scale performed better than linear frequency scales.
This document presents a novel feature reconstruction technique for robust automatic speech recognition. The technique is based on modeling speech occlusion using a log-max approximation. It estimates clean speech features using an MMSE approach, requiring noise estimates but not a missing data mask. Experimental results on Aurora2 and Aurora4 databases show the technique outperforms traditional missing data techniques, providing noise robust ASR without needing an explicit missing data mask.
The document describes the implementation of a wideband spectrum sensing algorithm using a software-defined radio. It discusses using an energy detection based approach to sense the local frequency spectrum and determine which portions are unused. The algorithm is first tested via simulations in MATLAB using known signal parameters. It is then tested using real data collected from a Universal Software Radio Peripheral (USRP) to analyze the actual wireless spectrum.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
We present a causal speech enhancement model working on the
raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with
skip-connections. It is optimized on both time and frequency
domains, using multiple loss functions. Empirical evidence
shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises,
as well as room reverb. Additionally, we suggest a set of
data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard
benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working
directly on the raw waveform.
Index Terms: Speech enhancement, speech denoising, neural
networks, raw waveform
A comparison of different support vector machine kernels for artificial speec...TELKOMNIKA JOURNAL
As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.
Voice recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.
This document describes how to build a simple, yet complete and representative automatic speaker recognition system. Such a speaker recognition system has potential in many security applications. For example, users have to speak a PIN (Personal Identification Number) in order to gain access to the laboratory door, or users have to speak their credit card number over the telephone line to verify their identity. By checking the voice characteristics of the input utterance, using an automatic speaker recognition system similar to the one that we will describe, the system is able to add an extra level of security.
A Novel Uncertainty Parameter SR ( Signal to Residual Spectrum Ratio ) Evalua...sipij
This document presents a novel speech enhancement evaluation approach called SR (Signal to Residual spectrum ratio). SR aims to improve speech intelligibility for hearing impaired individuals in non-stationary noisy environments. The approach segments noisy speech into pure, quasi, and non-speech frames using threshold conditions on the signal and estimated noise spectra. Noise power is estimated differently for each frame type. SR and LLR (log likelihood ratio) are used to measure distortions and compare the proposed approach to weighted averaging techniques. Results show the proposed SR approach achieves better segmental SNR and LLR scores than weighted averaging, indicating it enhances speech quality and intelligibility more effectively in car, airport, and train noise environments.
A Text-Independent Speaker Identification System based on The Zak TransformCSCJournals
This paper presents a novel text-independent speaker identification system based on the discrete Zak transform. The system uses the Zak transform coefficients as features to model 23 speakers from the ELSDSR database. During identification, the Euclidean distance between the Zak transform of the test speech and each speaker model is calculated. The speaker with the minimum distance is identified. The system achieves an identification efficiency of 91.3% using a single test file and 100% using two test files. The Zak-based method is also faster and has comparable accuracy to MFCC-based speaker identification. The paper also explores dividing signals into segments and averaging the Zak transforms, which improves efficiency while only slightly increasing modeling time.
A Combined Voice Activity Detector Based On Singular Value Decomposition and ...CSCJournals
voice activity detector (VAD) is used to separate the speech data included parts from silence parts of the signal. In this paper a new VAD algorithm is represented on the basis of singular value decomposition. There are two sections to perform the feature vector extraction. In first section voiced frames are separated from unvoiced and silence frames. In second section unvoiced frames are silence frames. To perform the above sections, first, windowing the noisy signal then Hankel’s matrix is formed for each frame. The basis of statistical feature extraction of purposed system is slope of singular value curve related to each frame by using linear regression. It is shown that the slope of singular values curve per different SNRs in voiced frames is more than the other types and this property can be to achieve the goal the first part can be used. High similarity between feature vector of unvoiced and silence frame caused to approach for separation of the two categories above cannot be used. So in the second part, the frequency characteristics for identification of unvoiced frames from silent frames have been used. Simulation results show that high speed and accuracy are the advantages of the proposed system.
Design and implementation of different audio restoration techniques for audio...eSAT Journals
This document summarizes research on designing and implementing different audio restoration techniques for removing distortions like clipping, clicks, and broadband noise from audio signals. It presents methods for declipping audio using sparse representations and frame-based reconstruction. Clicks are addressed using an adaptive filtering method, and broadband noise is reduced via spectral subtraction. The performance of these techniques is evaluated using metrics like SNR and algorithms like OMP. Hardware implementation of click removal is done on a TMS320C6713 DSK board using tools like MATLAB and Code Composer Studio.
Blind, Non-stationary Source Separation Using Variational Mode Decomposition ...CSCJournals
The Fourier Transform (FT) is the single best-known technique for viewing and reconstructing signals. It has many uses in all realms of signal processing, communications, image processing, radar, optics, etc. The premise of the FT is to decompose a signal into its frequency components, where a coefficient is determined to represent the amplitude of each frequency component. It is rarely ever emphasized, however, that this coefficient is a constant. The implication of that fact is that Fourier Analysis (FA) is limited in its accuracy at representing signals that are time-varying, e.g. non-stationary. Another novel technique called empirical mode decomposition (EMD) was introduced in the late 1990s to overcome the limits of FA, but the EMD was shown to have stability issues in reconstructing non-stationary signals in the presence of noise or sampling errors. More recently, a technique called variational mode decomposition (VMD) was introduced that overcomes the limitations of both aforementioned methods. This is a powerful technique that can reconstruct non-stationary signals blindly. It is only limited in the choice of the number of modes, K, in the decomposition. In this paper, we discuss how K may be determined a priori, using several examples. We also present a new approach that applies VMD to the problem of blind source separation (BSS) of two signals, estimating the strong powered signal, termed the interferer, first and then extracting the weaker one, termed the signal-of-interest (SOI). The baseline approach is to use all the predetermined K modes to reconstruct the interferer and then subtract its estimate from the received signal to estimate the SOI. We then devise an approach to choose a subset of the K modes to better estimate the interferer, termed culling, based on a very rough a priori frequency estimate of the weak SOI. We show that the VMD method with culling results in improvement in the mean-square error (MSE) of the estimates over the baseline approach by nearly an order of magnitude.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Automatic speech recognition (ASR) systems convert spoken words to text. ASR systems have a speech database for training, extract acoustic features like MFCCs from speech, and use hidden Markov models trained on large datasets to recognize speech in real-time. ASR has applications in dictation, command and control, telephony, and assisting those with disabilities. The timeline of ASR shows steady improvements from isolated word recognition to today's systems that can understand continuous speech.
This document summarizes a speech recognition system (SRS). SRS uses speech identification and verification. Speech identification determines which registered speaker provided an utterance by extracting features like mel-frequency cepstrum coefficients and comparing them. Speech verification accepts or rejects an identity claim by clustering training vectors from an enrollment session into speaker-specific codebooks using vector quantization. Applications of SRS include banking by phone, voice dialing, voice mail, and security control.
Comparative Analysis of Distortive and Non-Distortive Techniques for PAPR Red...IDES Editor
OFDM is a popular and widely accepted modulation
and multiplexing technique in the area of wireless
communication. IEEE 802.15, a wireless specification defined
for WPAN is an emerging wireless technology for short range
multimedia applications. Two general categories of 802.15
are the low rate 802.15.4 (ZigBee) and high rate 802.15.3
(UWB). In their physical (PHY) layer design, OFDM is a
competing technique due to the various advantages it renders
in the practical wireless media. OFDM has been a popular
technique for many years and adopted as the core technique
in a number of wireless standards. It makes the system more
immune to interference like InterSymbol Interference (ISI)
and InterCarrier Interference (ICI) and dispersive effects of
the channel. It is also a spectrally efficient scheme since the
spectra of the signal are overlapping in nature. Despite these
advantages OFDM suffers from a serious problem of high
Peak to Average Power. This limits the system’s capabilities
and increases the complexity. This paper compares the signal
distortion technique of Amplitude Clipping and the
distortionless technique of SLM for Peak to Average Power
reduction
Similar to A Novel Method for Speaker Independent Recognition Based on Hidden Markov Model (20)
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
This document summarizes a research paper that proposes using artificial intelligence techniques and FACTS controllers for reactive power planning in real-time power transmission systems. The paper formulates the reactive power planning problem and incorporates flexible AC transmission system (FACTS) devices like static VAR compensators (SVC), thyristor controlled series capacitors (TCSC), and unified power flow controllers (UPFC). Evolutionary algorithms like evolutionary programming (EP) and differential evolution (DE) are applied to find the optimal locations and settings of the FACTS controllers to minimize losses and costs. Simulation results on IEEE 30-bus and 72-bus Indian test systems show that UPFC performs best in reducing losses compared to SVC and TCSC.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This document summarizes and analyzes secure multi-party negotiation protocols for electronic payments in mobile computing. It presents a framework for secure multi-party decision protocols using lightweight implementations. The main focus is on synchronizing security features to avoid agreement manipulation and reduce user traffic. The paper describes negotiation between an auctioneer and bidders, showing multiparty security is better than existing systems. It analyzes the performance of encryption algorithms like ECC, XTR, and RSA for use in the multiparty negotiation protocols.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
This document summarizes a proposed cloud security and data integrity framework that provides client accountability. The framework aims to address issues like lack of user control over cloud data, need for data transparency and tracking, and ensuring data integrity. It proposes using JAR (Java Archive) files for data sharing due to benefits like portability. The framework incorporates client-side verification using MD5 hashing, digital signature-based authentication of JAR files, and use of HMAC to ensure data integrity. It also uses password-based encryption of log files to keep them tamper-proof. The framework is intended to provide both accountability and security for data sharing in cloud environments.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
This document summarizes a research paper that proposes a method for enhancing data security in cloud computing through steganography. The method hides user data in digital images stored on cloud servers. When data needs to be accessed, it is extracted from the images. The document outlines the cloud architecture and security issues addressed. It then describes the proposed system architecture, security model, and data storage and retrieval process. Data is partitioned and hidden in multiple images to improve security. The goal is to prevent unauthorized access to user data stored on cloud servers.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Mental Stress Evaluation using an Adaptive ModelIDES Editor
Chronic stress can have serious physiological and
psychological impact on an individual’s health. Wearable
sensor systems can enable physicians to monitor physiological
variables and observe the impact of stress over long periods of
time. To correlate an individual’s physiological measures with
their perception of psychological stress, it is essential that
the stress monitoring system accounts for individual
differences in self-reporting. Self-reporting of stress is highly
subjective as it is dependent on an individual’s perception of
stress and thus prone to errors. In addition, subjects can tailor
their answers to present their behavior more favorably. In
this paper we present an adaptive model which allows recorded
stress scores and physiological variables to be tuned to remove
biases in self-reported scores. The model takes an individual’s
physiological and psychological responses into account and
adapts to the user’s variations. Using our adaptive model,
physiological data is mapped efficiently to perceived stress
levels with 90% accuracy.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen