This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted.
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
CR : smart radio that has the ability to sense the external environment, learn from the history and make intelligent decisions to adjust its transmission parameters according
to the current state of the environment.
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
CR : smart radio that has the ability to sense the external environment, learn from the history and make intelligent decisions to adjust its transmission parameters according
to the current state of the environment.
What is GSM?
The Global System for Mobile communications is a digital cellular communications system. It was developed in order to create a common European mobile telephone standard but it has been rapidly accepted worldwide.
Formerly it was “Groupe Spéciale Mobile” (founded in 1982)
now: Global System for Mobile Communication.
Services:
Tele-services
Bearer or Data Services
Supplementary services
Applications:
Mobile telephony
GSM-R
Telemetry System
- Fleet management
- Automatic meter reading
- Toll Collection
- Remote control and fault reporting of DG sets
Value Added Services
Advantages:
Better Quality of speech
Data transmission is supported
New services offered due to ISDN compatibility
International Roaming possible
Large market
Crisper, cleaner quieter calls
disadvantages:
Dropped and missed calls
Less Efficiency
Security Issues
conclusion
The mobile telephony industry rapidly growing and that has become backbone for business success and efficiency and a part of modern lifestyles all over the world.
In this session I have tried to give and over view of the GSM system. I hope that I gave the general flavor of GSM and the philosophy behind its design.
The GSM is standard that insures interoperability without stifling competition and innovation among the suppliers to the benefit of the public both in terms of cost and service quality.
This includes Digital signal data transmission, Base band and band pass transmission. Also detailed with PAM, PPM, PWM, PCM, DPCM, DM, ADM, ASK, PSK, FSK.
The Presentation includes Basics of Non - Uniform Quantization, Companding and different Pulse Code Modulation Techniques. Comparison of Various PCM techniques is done considering various Parameters in Communication Systems.
P ERFORMANCE A NALYSIS O F A DAPTIVE N OISE C ANCELLER E MPLOYING N LMS A LG...ijwmn
n voice communication systems, noise cancellation
using adaptive digital filter is a renowned techniq
ue
for extracting desired speech signal through elimin
ating noise from the speech signal corrupted by noi
se.
In this paper, the performance of adaptive noise ca
nceller of Finite Impulse Response (FIR) type has b
een
analysed employing NLMS (Normalized Least Mean Squa
re) algorithm.
An extensive study has been made
to investigate the effects of different parameters,
such as number of filter coefficients, number of s
amples,
step size, and input noise level, on the performanc
e of the adaptive noise cancelling system. All the
results
have been obtained using computer simulations built
on MATLAB platform.
What is GSM?
The Global System for Mobile communications is a digital cellular communications system. It was developed in order to create a common European mobile telephone standard but it has been rapidly accepted worldwide.
Formerly it was “Groupe Spéciale Mobile” (founded in 1982)
now: Global System for Mobile Communication.
Services:
Tele-services
Bearer or Data Services
Supplementary services
Applications:
Mobile telephony
GSM-R
Telemetry System
- Fleet management
- Automatic meter reading
- Toll Collection
- Remote control and fault reporting of DG sets
Value Added Services
Advantages:
Better Quality of speech
Data transmission is supported
New services offered due to ISDN compatibility
International Roaming possible
Large market
Crisper, cleaner quieter calls
disadvantages:
Dropped and missed calls
Less Efficiency
Security Issues
conclusion
The mobile telephony industry rapidly growing and that has become backbone for business success and efficiency and a part of modern lifestyles all over the world.
In this session I have tried to give and over view of the GSM system. I hope that I gave the general flavor of GSM and the philosophy behind its design.
The GSM is standard that insures interoperability without stifling competition and innovation among the suppliers to the benefit of the public both in terms of cost and service quality.
This includes Digital signal data transmission, Base band and band pass transmission. Also detailed with PAM, PPM, PWM, PCM, DPCM, DM, ADM, ASK, PSK, FSK.
The Presentation includes Basics of Non - Uniform Quantization, Companding and different Pulse Code Modulation Techniques. Comparison of Various PCM techniques is done considering various Parameters in Communication Systems.
P ERFORMANCE A NALYSIS O F A DAPTIVE N OISE C ANCELLER E MPLOYING N LMS A LG...ijwmn
n voice communication systems, noise cancellation
using adaptive digital filter is a renowned techniq
ue
for extracting desired speech signal through elimin
ating noise from the speech signal corrupted by noi
se.
In this paper, the performance of adaptive noise ca
nceller of Finite Impulse Response (FIR) type has b
een
analysed employing NLMS (Normalized Least Mean Squa
re) algorithm.
An extensive study has been made
to investigate the effects of different parameters,
such as number of filter coefficients, number of s
amples,
step size, and input noise level, on the performanc
e of the adaptive noise cancelling system. All the
results
have been obtained using computer simulations built
on MATLAB platform.
Comparison of different Sub-Band Adaptive Noise Canceller with LMS and RLSijsrd.com
Sub-band adaptive noise is employed in various fields like noise cancellation, echo cancellation and system identification etc. It reduces computational complexity and improve convergence rate. In this paper we perform different Sub-band noise cancellation method for simulation. The Comparison with different algorithm has been done to find out which one is best.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Design and Implementation of Polyphase based Subband Adaptive Structure for N...Pratik Ghotkar
With the tremendous growth in the Digital Signal processing technology, there are many techniques available to remove noise from the speech signals which is used in the speech processing. Widely used LMS algorithm is modified with much advancement but still there are many limitations are introducing. This paper consist of a new approach i.e. subband adaptive processing for noise cancelation in the speech signals. Subband processing employs the multirate signal processing. The polyphase based subband adaptive implementation finds better results in term of MMSE , PSNR and processing time; also the synthesis filter bank is works on the lower data rate which reduces the computational Burdon as compare to the direct implementation of Subband adaptive filter. The normalized least mean squares (NLMS) algorithm is a class of adaptive filter used.
Acoustic problems in an environment has gained more attention due to the tremendous growth of technology that lead to noisy engines, heavy machineries, pumps, air condition, music and other noise sources. Normally human ears are very sensitive at audio range (lower frequency) from 20 Hz to 20 kHz. So, any sound within these frequencies has the tendency to disturb human hearing and can be classified as noise. The reduction of acoustic noise in speech has been investigated for many Years .The major application of noise reduction is by improving voice communication and eliminating the noise using adaptive noise canceler.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
Echo Cancellation Algorithms using Adaptive Filters: A Comparative Studyidescitation
An adaptive filter is a filter that self-adjusts its transfer function according to an
optimization algorithm driven by an error signal. Adaptive filter finds its essence in
applications such as echo cancellation, noise cancellation, system identification and many
others. This paper briefly discusses LMS, NLMS and RLS adaptive filter algorithms for
echo cancellation. For the analysis, an acoustic echo canceller is built using LMS, NLMS
and RLS algorithms and the echo cancelled samples are studied using Spectrogram. The
analysis is further extended with its cross-correlation and ERLE (Echo Return Loss
Enhancement) results. Finally, this paper concludes with a better adaptive filter algorithm
for Echo cancellation. The implementation and analysis is done using MATLAB®,
SIMULINK® and SPECTROGRAM V5.0®.
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMSEditor IJMTER
This paper describes the concept of adaptive noise cancelling. The noise cancellation
using the Recursive Least Squares (RLS) to remove the noise from an input signal. The RLS adaptive
filter uses the reference signal on the Input port and the desired signal on the desired port to
automatically match the filter response in the Noise Filter block. The filtered noise should be completely
subtracted from the "noisy signal” of the input Sine wave & noise input signal, and the "Error Signal"
should contain only the original signal. Finally, the functions of field programmable gate array based
system structure for adaptive noise canceller based on RLS algorithm are synthesized, simulated, and
implemented on Xilinx XC3s200 field programmable gate array using Xilinx ISE tool.
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713CSCJournals
In noisy acoustic environment, audio signal in speech communication from mobile phone, moving car, train, aero plane, or over a noisy telephone channel is corrupted by additive random noise. The noise is unwanted signal and it is desirable to remove noise from original signal. Since noise is random process and varying at every instant of time, we need to estimate noise at every instant to remove it from original signal. There are many schemes for noise removal but most effective scheme to accomplish noise cancellation is to use adaptive filters. In this paper, we have carried out simulations for different adaptive algorithms (LMS, NLMS and RLS) and compared their performance for noise cancellation in noisy environment. Real time implementation of adaptive algorithm over DSP kit (TMS320C6713) is also presented in this paper. Performance of adaptive algorithm over hardware is also presented. Developed system incorporating best performance adaptive filter in any noisy environment can be used for noise cancellation.
In this paper, the performances of adaptive noise cancelling system employing Least Mean Square (LMS) algorithm are studied considering both white Gaussian noise (Case 1) and colored noise (Case 2)
situations. Performance is analysed with varying number of iterations, Signal to Noise Ratio (SNR) and tap size with considering Mean Square Error (MSE) as the performance measurement criteria. Results show that the noise reduction is better as well as convergence speed is faster for Case 2 as compared with Case 1. It is also observed that MSE decreases with increasing SNR with relatively faster decrease of MSE in Case 2 as compared with Case 1, and on average MSE increases linearly with increasing number of filter
coefficients for both type of noise situations. All the experiments have been done using computer
simulations implemented on MATLAB platform.
Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size (EDNSS) algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original EDNSS algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real-world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.
In this work, we develop a new approach to active noise cancellation. Theoretically, we use a 180 degree phase
shift of the noise, i.e. generate an anti-noise, to cancel the original signal. Here, we have designed FIR filter
which is driven by the input signal and has the noise signal as the feedback, and output of which is the required
result. The active noise control system contains an electro-acoustic device that cancels the unwanted audio
signal by generating an anti-noise of equal amplitude and opposite phase. The original, unwanted sound and the
anti-noise combine acoustically, resulting in the cancellation of both sounds. The effectiveness of cancellation
of the unwanted signal depends on the accuracy of the amplitude and phase of the generated anti-noise.
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
ACTIVE NOISE CANCELLATION IN A LABORATORY DUCT USING FUZZY LOGIC AND NEURAL ...Rishikesh .
The main goal of this paper is to present a simulation scheme to simulate an adaptive filter using LMS (Least mean square) adaptive algorithm for noise cancellation. The main objective of the noise cancellation is to estimate the noise signal and to subtract it from original input signal plus noise signal and hence to obtain the noise free signal. There is an alternative method called adaptive noise cancellation for estimating a speech signal corrupted by an additive noise or interference. This method uses a primary input signal that contains the speech signal and a reference input containing noise. The reference input is adaptively filtered and subtracted from the primary input signal to obtain the estimated signal. In this method the desired signal corrupted by an additive noise can be recovered by an adaptive noise canceller using LMS (least mean square) algorithm. This adaptive noise canceller is useful to improve the S/N ratio. Here we estimate the adaptive filter using Labview /MATLAB/SIMULINK environment . For achieving the goal we also use modern algorithms like ANFIS, FIS and Neural Network and compare the PSD of all the algorithms.
Performance Analysis of Acoustic Echo Cancellation TechniquesIJERA Editor
Mainly, the adaptive filters are implemented in time domain which works efficiently in most of the applications. But in many applications the impulse response becomes too large, which increases the complexity of the adaptive filter beyond a level where it can no longer be implemented efficiently in time domain. An example of where this can happen would be acoustic echo cancellation (AEC) applications. So, there exists an alternative solution i.e. to implement the filters in frequency domain. AEC has so many applications in wide variety of problems in industrial operations, manufacturing and consumer products. Here in this paper, a comparative analysis of different acoustic echo cancellation techniques i.e. Frequency domain adaptive filter (FDAF), Least mean square (LMS), Normalized least mean square (NLMS) &Sign error (SE) is presented. The results are compared with different values of step sizes and the performance of these techniques is measured in terms of Error rate loss enhancement (ERLE), Mean square error (MSE)& Peak signal to noise ratio (PSNR).
Performance Evaluation of Adaptive Filters Structures for Acoustic Echo Cance...CSCJournals
We have designed and simulated an acoustic echo cancellation system for conferencing. This system is based upon a least-mean-square (LMS) adaptive algorithm and uses multi filter technique. A comparative study of both structure has been carried out and it is found that this new multi-filter converge faster than similar single long adaptive filter. Index Terms: LMS,Multiple sub filter ,Echo cancellation
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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cancellation, adaptive system identification, linear prediction, adaptive equalization, inverse modeling, etc.
Noise is assumed to be a random process and adaptive filters have the capability to adjust their impulse
response to filter out the correlated signal in the input. They require modest or no a priori knowledge of the
signal and noise characteristics. In addition adaptive filters have the potential of adaptively tracking the
signal under non-stationary conditions. It has the unique characteristic of self-modifying [14] its frequency
response to change the behavior in time and allowing the filter to adapt the response to the input signal
characteristics change. The basic principle of an adaptive filter is shown in Figure 1.
Figure 1. Adaptive Filter
The objective is to filter the input signal, x(n), with an adaptive filter in such a manner that it
matches the desired signal, d(n). In order to generate an error signal the desired signal, d(n), is subtracted
from the filtered signal, y(n). An adaptive algorithm is driven by the error signal which generates the filter
coefficients in a manner that minimizes the error signal. Unlike from the fixed filter design, here the filter
coefficients are tunable, are adjusted in dependency of the environment that the filter is operated in, and can
therefore track any potential changes in this environment. Using this concept, adaptive filters can be tailored
to the environment set by these signals. However, if the environment changes filter through a new set of
factors, adjusts for new features [15]. The adaptive filter constitutes a vital part of the statistical signal
processing. The application of an adaptive filter offers a smart solution to the problem wherever there is a
need to process signals that result from operation in an environment of unknown statistics, as it typically
provides a significant enhancement in performance over the use of a fixed filter designed by conventional
methods [17-18]. The aim of this paper is to review the existing noise cancellation techniques for enhancing
speech and audio signal quality and to provide the understanding of suitability of various developed models.
Prior to this, a brief review of the adaptive noise cancellation methods and its application is presented in the
next section. Finally, a perception on upcoming research is suggested for further consideration.
2. ADAPTIVE NOISE CANCELLATION
Acoustic noise cancellation is indispensable from the health point of view as extensive exposures to
high level of noise may cause serious health hazards to human being. The conventional noise cancellation
method [19] uses a reference input signal (correlated noise signal) which is passed through the adaptive filter
to make it equal to the noise that is added to original information bearing signal. Subsequently this filtered
signal is subtracted from noise corrupted information signal. This makes the corrupted signal a noise free
signal. The fundamental concept of noise cancellation [19] is to produce a signal that is equal to a disturbance
signal in amplitude and frequency but has opposite phase. These two signals results in the cancellation of
noise signal. The original Adaptive noise cancellation (ANC) [20] uses two sensors to receive the noise
signal and target signal separately. The relationship between the noise reference x(n) and the component of
this noise that is contained in the measured signal d(n) may be determined by Adaptive noise cancellation
shown in Figure 2
Adaptive Filter
∑
x(n) y(n) d(n)
e(n)
+
-
Adaptive
algorithm
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Figure 2. Adaptive noise cancelling
If several unrelated noises corrupt the measurement of interest then several adaptive filters may be
deployed in parallel as long as suitable noise reference signals are available within the system. In noise
cancelling systems the objective is to produce a system output e(n) =[ s(n) + n1 ]- y(n) which is a best fit in
the least squares sense to the signal s(n). This objective is achieved by adjusting the filter through an adaptive
algorithm and feeding the system output back to the adaptive filter and to minimize total system output
power [20]. In an adaptive noise cancelling system, the system output serves as an error signal for the
adaptive process.
2.1. Digital Filters
The purpose of digital filters is to separate signals that have been combined and to restore signals
that have been distorted in some way [22]. Signal separation is required when a signal has been contaminated
with interference, noise, or other signals whereas restoration is used when a signal has been distorted in some
way. Broadly the digital filters are classified as Weiner and Kalman filters [23].
2.1.1. Wiener filter
A Wiener filter [24] is a digital filter, which is designed to reduce the mean square difference
between some desired signal and the filtered output. It is occasionally called a minimum mean square error
filter. A Wiener filter [25] can be finite-duration impulse response (FIR) filter or an infinite-duration impulse
response (IIR) filter or a [26]. Generally the formulation of an FIR Wiener filter results in a set of linear
equations and has a closed-form solution whereas the formulation of an IIR Wiener filter [27] results in a set
of non-linear equations. The Wiener filter represented by the coefficient vector w is depicted in Figure 3. The
filter accepts the input signal y(m), and generates an output signal x m , where x m is the least mean square
error estimate of a desired or target signal x(m). The filter input–output relation is shown in Equation 1.
yw
kmy
T
1-p
0k
k )(wx(m)
(1)
where m is the discrete-time index, yT
=[y(m), y(m–1), ..., y(m–P–1)] is the filter input signal, and the
parameter vector wT
=[w0
, w1
, ..., wP–1
] is the Wiener filter coefficient vector.
Figure 3. Illustration of a Wiener Filter Structure
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2.1.2. Kalman Filter
The Kalman filter is a mathematical power tool which plays an important role in computer graphics
as we include sensing of the real world in our systems. The Kalman filter can also be termed as a set of
mathematical equations that implement a predictor-corrector type estimator which is optimal in the sense that
it minimizes the estimated error covariance—when some presumed conditions are met. For the past decade
the Kalman filter has been the active area of research and application, particularly in the area of autonomous
or assisted navigation. The Kalman filter [28] (and its variants such as the extended Kalman filter [29] and
unscented Kalman filter [30] is one of the most popular data fusion algorithms in the field of information
processing. [31-36].
2.2. Adaptive Filters
An adaptive filter [37] is a system with a linear filter which consists of transfer function restrained
by variable parameters and a means to adjust those parameters according to an optimization algorithm.
Adaptive linear filters [38] are linear dynamical system with variable or adaptive structure and parameters
and have the property to modify the values of their parameters, i.e. their transfer function, during the
processing of the input signal, in order to generate signal at the output which is without undesired
components, noise, and degradation and also interference signals.
Figure.4 shows the basic concept of an adaptive filter [39] whose primary objective is to filter the
input signal, x(n), with an adaptive filter in such a manner that it matches the desired signal, d(n). The desired
signal, d(n), is subtracted from the filtered signal, y(n), to produce an error signal which in turn drives an
adaptive algorithm that generates the filter coefficients in a manner that minimizes the error signal. The
adaptation adjusts the characteristics of the filter through an interaction with the environment in order to
reach the desired values. Contrary to the conventional filter design techniques, adaptive filters do not have
constant filter coefficients and no priori information is known, such a filters with adjustable parameters are
called an adaptive filter. Adaptive filter adjust their coefficients to minimize an error signal and may be
termed as finite impulse response (FIR) [40], infinite impulse response (IIR) [41], lattice and transform
domain filter. Generally adaptive digital filters consist of two separate units: the digital filter, with a structure
determined to achieve desired processing (which is known with an accuracy to the unknown parameter
vector) and the adaptive algorithm for the update of filter parameters, with an aim to guarantee fastest
possible convergence to the optimum parameters from the point of view of the adopted criterion. Majority of
adaptive algorithms signify modifications of the standard iterative procedures for the solution of the problem
of minimization of criterion function in real time. The most common form of adaptive filters are the
transversal filter using least mean square (LMS) algorithm [42] and recursive least square (RLS) algorithm
[43].
2.3. Adaptive Algorithms
Adaptive algorithms [44] have been extensively studied in the past few decades and the most
popular adaptive algorithms are the least mean square (LMS) algorithm and the recursive least square (RLS)
algorithm. Attaining the best performance of an adaptive filter requires usage of the best adaptive algorithm
with low computational complexity and a fast convergence rate.
2.3.1. Least-Mean-Square Algorithm (LMS)
A very straightforward approach in noise cancelling is the use of LMS algorithm which was
developed by Windrow and Hoff [45]. This algorithm uses a gradient descent to estimate a time varying
signal. The gradient descent method finds a minimum, if it exists, by taking steps in the direction negative of
the gradient and it does so by adjusting the filter coefficients in order to minimize the error. The gradient is
the del-operator and is applied to find the divergence of a function, which is the error with respect to the nth
coefficient in this case. The LMS algorithm has been accepted by several researchers for hardware
implementation because of its simple structure. In order to implement it, modifications have to be made to
the original LMS algorithm because the recursive loop in its filter update formula prevents it from being
pipelined.
The following equation shows the detail of LMS algorithm,
Weights evaluation –
)(*)(*)()1( inxnenwnw ii (2)
Filtering output –
1
0
)(*)()(
M
i
i inxnwny
(3)
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Error estimation (where error is the desired output)–
)()()( nyndne (4)
where the output of an adaptive filter y(n) and the error signal e(n) are given by (3) and (4), respectively. In
these equations, x(n) is the input signal vector, and w(n) is the tap weight vector of the adaptive filter. The
equations employ the current estimate of the weight vector. From these equations it is clear that at each
iteration, the information of most recent values (d(n) , x(n), w(n) and e(n))are required and the iterative
procedure is started with an initial guess w(0). μ is the step size that depends on the power spectral density of
the reference input x(n) and filter length M-1 and control the stability and convergence speed of the LMS
algorithm.
In the recent times, a new version of the LMS algorithm with time varying convergence parameter
has been proposed Error! Reference source not found.. The time-varying LMS (TV-LMS) [47] algorithm
has shown better performance than the conventional LMS algorithm in terms of less mean square error MSE
and faster convergence. The TV-LMS algorithm is based on utilizing a time-varying convergence parameter
μn with a general power decaying law for the LMS algorithm. The basic concept of TV-LMS algorithm is to
exploit the fact that the LMS algorithms need a larger convergence parameter value to speed up the
convergence of the filter coefficients to their optimal values. After the coefficients converge to their optimal
values, the convergence parameter ought to be small for better estimation accuracy. In other words, we set
the convergence parameter to a large value in the initial state in order to speed up the algorithm convergence.
2.3.2. NLMS Algorithm
The main weakness of the conventional type LMS lies in its complexity in selecting a suitable value
for the step size parameter that guarantees stability. In order to overcome, NLMS has been proposed in
controlling the convergence factor of LMS through modification into a time-varying step size parameter. As
NLMS employs a variable step size parameter intended at minimizing the instantaneous output error hence
converges faster than the conventional LMS [48-49]. The conventional LMS algorithm experiences a
gradient noise amplification problem as the convergence factor μ is large. The correction applied to the
weight vector w(n) at iteration n+1 is “normalized” with respect to the squared Euclidian norm of the input
vector x(n) at iteration n. We may express the NLMS algorithm as a time-varying step-size algorithm,
calculating the convergence factor μ as in Equation 5.
µ(n) =
α
∥ ∥
(5)
where: α is the NLMS adaption constant, which optimize the convergence rate of the algorithm and should
satisfy the condition 0< α<2, and c is the constant term for normalization and is always less than 1. The Filter
weights are updated by the Equation 6.
w(n+1) = w(n) +
α
∥ ∥
e(n) x(n) (6)
In comparison to LMS, the NLMS has varying step size that makes the NLMS to converge more quickly. In
order to best serve various applications several variants of LMS have been developed. Some of the popular
variants are Modified Normalized LMS (MN-LMS) algorithm, Leaky LMS, Block LMS, Sign Error LMS,
Sign-Data LMS (SD-LMS), Sign-Data Normalized LMS (SDN-LMS), Sign-Sign LMS (SS-LMS) algorithm,
Sign-Sign LMS algorithm with leakage term (SS-LMS-LT), Variable step-size LMS (VS-LMS) algorithm,
Filtered X-LMS (Fx-LMS) algorithm, Frequency response shaped LMS (FRS-LMS) algorithm, Hybrid LMS
(H-LMS) algorithm are summarized in Table 1.
2.3.3. Recursive least square (RLS) Algorithm
RLS algorithm is another potential alternative to overcome slow convergence in colored
environments [43], which uses the least squares method to develop a recursive algorithm for the adaptive
transversal filter. The RLS [82] recursively finds the filter coefficients that minimize a weighted linear least
squares cost function relating to the input signals. RLS tracks the time variation of the process to the optimal
filter coefficient with relatively very fast convergence speed; though it has increased computational
complexity and stability problems as compared to LMS-based algorithms [83]. The RLS algorithm [84-85]
has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter
exhibiting the best convergence behavior. Unfortunately, practical applications of the algorithms are often
associated with high computational complexity and poor numerical properties. Several different standard
6. IJECE ISSN: 2088-8708
LMS Adaptive Filters for Noise Cancellation: A Review (Shubhra Dixit)
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RLS algorithms with varying degrees of computational complexity and stability exist. Amongst all the
conventional recursive least squares (CRLS) algorithm is considered to be the most stable, but requires O
(N2) (on the order of N2) operations per iteration, where N is the filter length [86].
Fast Transversal RLS Algorithm
Fast transversal filter (FTF) algorithm [87-88] involves the combined use of four transversal filters for
forward and backward predictions, joint process and gain vector computation estimation. The merit of FTF
algorithm lies in its reduced computational complexity as compared to other available solutions.
Table 1. Variation of LMS algorithm
3. CONCLUSION
A comprehensive review has been carried out to identify the existing literature related to adaptive
filtering in noise reduction using LMS adaptive algorithms in particular. LMS is preferred over RLS
algorithms for various noise cancellation purposes as RLS has increased computational complexity and
stability problems as compared to LMS-based algorithms which are robust and reliable. Various LMS
adaptive algorithms viz. N-LMS, MN-LMS, Leaky LMS, Block LMS, SE-LMS, SD-LMS, SDN-LMS, SS-
LMS, SS-LMS-LT, VS-LMS, FX-LMS, FRS-LMS, H-LMS are dealt in this paper for the purpose of
comparison in terms of simplicity and application. The LMS algorithm is relatively simple to implement and
is powerful enough to evaluate the practical benefits that may result from the application of adaptivity to the
problem at hand. Moreover, it provides a practical frame of reference for assessing any further improvement
that may be attained through the use of more sophisticated adaptive filtering algorithms.
REFERENCES
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S. No Algorithm type Recursion (Weighted) Reference
1. Conventional
LMS
)(*)(*)()1( inxnenwnw ii [45], [48]
2. NLMS w(n+1) = w(n) +
α
∥ ∥
e(n) x(n) [48-49]
3. (MN-LMS) W n 1 W n β ‖ ‖
μe n Where, 0 < β < 2 [50-51]
4. Leaky LMS W n 1 1 μγ W n X n μe n
Where, leaky coefficient γ, 0 < γ << 1
0 < µ < (γ + λmax)
[52-54]
5. (B-LMS)
W k 1 L W kL μ
1
L
e kL l X kL l
Where, l = 0, 1, 2, ... ... ... , L-1
[55-57]
6. (SE-LMS) W n 1 W n X n μsgn e n
Where, sgn(.) = signum function sgn[e(n)] =
1 for e n 0
0 for e n 0
1 for e n 0
[58-59]
7. (SD-LMS) W n 1 W n sgn X n μe n
Where, sgn(.) = signum function
[60-62]
8. (SDN-LMS) w n 1 w n
μ
|x n k |
e n x n k
Where, sgn(.) = signum function
[63]
9. (SS-LMS) W n 1 W n sgn X n μsgn e n
Where, sgn(.) = signum function
[64-65]
10. (VS-LMS) w n 1 w n μ e n x n k
Where, µmin < µ < µmax
[66-69]
11 (SS-LMS-LT) W n 1 1 μγ W n sgn X n μsgn e n [89-90]
12. (FX-LMS) W n 1 W n X′
μe n
Where, X′ n s n X n
[70-74]
13. (FRS-LMS) W n 1 I μF W n X n μe n
Where, F = □F0 and □ is constant.
[75-77]
14. (H-LMS) W n 1 W n X n μe n
for, 0 ≤ n ≤ p
W n 1 W n
X n μ n e n
E X n X n
for, n ≥ p+1
[78-81]
7. ISSN: 2088-8708
IJECE Vol. 7, No. 5, October 2017 : 2519 – 2528
2526
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BIOGRAPHIES OF AUTHORS
Shubhra Dixit was born in Varanasi, Uttar Pradesh in 1984. She received her Bachelors and
Masters Degree in Electronics Engineering and Digital Communications Systems from Uttar
Pradesh Technical University, Lucknow, INDIA, in the year 2006 and 2010 respectively.
In the year 2010 she joined Amity Institute of Telecom Engineering & Management, Amity
University, Uttar Pradesh, India as Assistant Professor and involved in various teaching and
research activities in the field of Signal Processing.
She is author and co-author of more than 10 National and International level conference and
journal papers and life time member of IETE, India. Her research interest includes Signal
Processing and Digital Communication. She is extensively working in the field of Noise
Cancellation using Adaptive filters.
Deepak Nagaria was born in Jhansi, Uttar Pradesh, in 1975. He received his B.E. in
Electronics & Instrumentation Engineering from Bundelkhand Institute of Engineering and
Technology, Jhansi, INDIA, in 1996, M.Tech in Control Systems from National Institute of
Technology, Kurukshetra, Haryana, India in 1999 and the Ph.D. degree in control systems
from Indian Institute of Technology, Roorkee, India in 2009.
In 1999 he joined Bundelkhand Institute of Engineering and Technology, Jhansi, INDIA and
currently working as Head of Department Electrical Engineering and Reader in the
Department of Electronics & Communication Engineering His research interest includes
Control Systems, Artificial Intelligence, Signal Processing and Communication. He is
member of various academic research councils and societies. He has many papers in
International and National Journals.