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Go to Editorial ManagerThis paper presents an insufficient cyclic prefix (CP) Orthogonal Frequency Division Multiplexing (OFDM) system with equalizer whose coefficients are calculated using Least Mean Square (LMS) algorithm. The OFDM signal is passed through a channel with four multipath signals which cause the OFDM signal to be under high inter-symbol interference (ISI) and inter-carrier interference (ICI).8-QAM and 16-QAM digital modulation techniques are used to evaluate the performance of the proposed system. The simulation results have accentuated the high performance of the LMS equalizer via comparing its Bit Error Rate (BER) and constellation diagram with those of the Minimum Mean Square Error and Zero Forcing equalizers. Moreover, the results also reveal that the LMS equalizer provides BER performance close to that of the OFDM system with a hypothetical sufficient CP.
The identification of system parameters plays an essential role in system modeling and control. This paper presents a parameter estimation for a permanent magnetic DC motor using the simulink design optimization method. The parameter estimation may be represented as an optimization problem. Firstly, the initial values of the DC motor parameters are extracted using the dynamic model through measuring the values of voltage, current, and speed of the motor. Then, these values are used as an initial value for simulink design optimization. The experimentally input- output data can be collected using a suggested microcontroller based circuit that will be used later for estimating the DC motor parameters by building a simulink model. Two optimization algorithms are used, the pattern search and the nonlinear least square. The results show that the nonlinear least square algorithm gives a more accurate result that almost approaches to the actual measured speed response of the motor. )
The Leader detecting and following are one of the main challenges in designing a leader-follower multi-robot system, in addition to the challenge of achieving the formation between the robots, while tracking the leader. The biological system is one of the main sources of inspiration for understanding and designing such multi-robot systems, especially, the aggregations that follow an external stimulus such as light. In this paper, a multi-robot system in which the robots are following a spotlight is designed based on the behavior of the Artemia aggregations. Three models are designed: kinematic and two dynamic models. The kinematic model reveals the light attraction behavior of the Artemia aggregations. The dynamic model will be derived based on the newton equation of forces and its parameters are evaluated by two methods: first, a direct method based on the physical structure of the robot and, second, the Least Square Parameter Estimation method. Several experiments are implemented in order to check the success of the three proposed systems and compare their performance. The experiments are divided into three scenarios of simulation according to three paths: the straight line, circle, zigzag path. The V-Rep software has been used for the simulation and the results appeared the success of the proposed system and the high performance of tracking the spotlight and achieving the flock formation, especially the dynamic models.
Electrical motors have been engaged in many residential, industrial and commercial applications. The speed of an electric motor is an essential output quantity which is needed in many processing systems. Therefore, estimating the speed of an electrical motor is an integral part in the hierarchy of operational and control process. In this work, a new speed estimation method is proposed which is based on a naturally occurring signal; the mechanical vibrations the body of the motor endure during operation. These vibration signals are measured in multi-axial dimension through accelerometer and gyroscope. Furthermore, the collected data is trained in a machine learning model. The model is used subsequently to estimate the speed of a self-excited direct current (DC) motor. Two approaches (offline and onboard) are followed to evaluate the fitness and the performance of the proposed method. The offline approach is performed using regression learner MATLAB toolbox and many algorithms are tested and results with different performance metrics are presented. The algorithm that yields best performance in terms of minimum Root Mean Square and maximum regression factor (R2) is selected as candidate for offline revolutions per minute (rpm) estimation. Results documents that with Gaussian process regression algorithm, estimations are obtained with a mean square error of 7 rpm and an R2 value of 1 which is considered a very satisfactory performance. The second approach is motor speed estimation in real time using vibration signals with deep learning model implemented on limited resources electronic board which is proposed for the first time to the best of our knowledge. The proposed method has been successfully implemented by low consumption resources from the selected board with 6.5 kb of ram and 91ms latency. Even with the limited resources, a rated speed estimate percentage error of 0.18% was recorded from real time results. Moreover, the proposed method is characterized by its simplicity, low technical requirements and eventually low cost of implementation. The aforementioned features make this method an attractive platform for speed estimation in many industrial applications.
According to the characteristic of HVS (Human Visual System) and the association memory ability of neural network, an adaptive image watermarking algorithm based on neural network is proposed invisible image watermarking is secret embedding scheme for hiding of secret image into cover image file and the purpose of invisible watermarking is copyrights protection. Wavelet transformation-based image watermarking techniques provide better robustness for statistical attacks in comparison to Discrete Cosine Transform domain-based image watermarking. The joined method of IWT (Integer Wavelet Transform) and DCT (Discrete Cosine Transform) gives benefits of the two procedures. The IWT have impediment of portion misfortune in embedding which increments mean square estimate as SIM and results diminishing PSNR. The capacity of drawing in is improved by pretreatment and re-treatment of image scrambling and Hopfield neural network. The proposed algorithm presents hybrid integer wavelet transform and discrete cosine transform based watermarking technique to obtain increased imperceptibility and robustness compared to IWT-DCT based watermarking technique. The proposed watermarking technique reduces the fractional loss compared to DWT based watermarking.
In nowadays world of rapid evolution of exchanging digital data, data protection is required to protect data from the unauthorized parities. With the widely use of digital images of diverse fields, it is important to conserve the confidentiality of image’s data form any without authorization access. In this paper the problem of secret key exchanging with the communicated parities had been solved by using a random number generator which based on Linear Feedback Shift Register (LFSR). The encryption/decryption is based on Advance Encryption Standard (AES) with the random key generator. Also, in this paper, both grayscale and colored RGB images have been encrypted/decrypted. The functionality of proposed system of this paper, is concerned with three features: First feature, is dealing with the obstetrics of truly random and secure encryption key while the second one deals with encrypting the plain or secret image using AES algorithm and the third concern is the extraction the original image by decrypting the encrypted or cipher one. “Mean Square Error (MSE)”, “Peak Signal to Noise Ratio (PSNR)”, “Normalized Correlation (NK)”, and “Normalized Absolute Error (NAE)” are measured for both (original-encrypted) images and (original-decrypted) image in order to study and analyze the performance of the proposed system according to image quality features.
According to the growing interest in the soft robotics research field, where various industrial and medical applications have been developed by employing soft robots. Our focus in this paper will be the Pneumatic Muscle Actuator (PMA), which is the heart of the soft robot. Achieving an accurate control method to adjust the actuator length to a predefined set point is a very difficult problem because of the hysteresis and nonlinearity behaviors of the PMA. So the construction and control of a 30 cm soft contractor pneumatic muscle actuator (SCPMA) were done here, and by using different strategies such as the PID controller, Bang-Bang controller, Neural network controller, and Fuzzy controller, to adjust the length of the (SCPMA) between 30 cm and 24 cm by utilizing the amount of air coming from the air compressor. All of these strategies will be theoretically implemented using the MATLAB/Simulink package. Also, the performance of these control systems will be compared with respect to the time-domain characteristics and the root mean square error (RMSE). As a result, the controller performance accuracy and robustness ranged from one controller to another, and we found that the fuzzy logic controller was one of the best strategies used here according to the simplicity of the implementation and the very accurate response obtained from this method.
The paper presents a novel approach that merges the abilities of the biological environment with the concept of hierarchical trees to attack a specific stream cipher. The model being presented introduces a systematic method that targets a group of stream ciphers, such as the GCM family, these devices are composed of components that are suitable for the proposed method. A restricted set of binaries for the final key sequence is required to implement this technique as an input. The attacked algorithm comprises feedback shift registers, memories, delays, and so on. The stream ciphers are widely used in modern encryption to secure communication devices, so any attempt to analyze or attack it is of the utmost importance. The results of this method have been confirmed to lead to the destruction of the cipher’s security. Many novelties and contributions of the present work can be summarized as follows: firstly, the key generator’s components are attacked individually, disrupting the cohesion between them. This was not possible previously except in rare cases and under difficult conditions. Secondly, the method of verifying the correct initial values is unrelated to the generator’s operation. Thirdly, the technique applies biological concepts and processes to laboratory test tubes for genetic engineering, it can be said that the prepared model targets a broad class of stream key generators, rather than a single algorithm. The proposed technique requires a specific and deterministic number of final key sequence bits, which are easy to provide. The proposed technique creates a search E -tree in the style of hierarchical clusters, in which the first level containsE nodes. Then each successive level contains the square of E nodes of the number of nodes in the previous level, and the root is composed of the total solution space of the stream key generator and produces the nodes of each level from the intersection of the cluster contents in the test tubes for all clusters in the level above it. The contribution and novelty of the present work is cryptanalyzing and attacking shift register-based stream key generators involves fragmentation. The attacking principle entails disassembling generator components from registers and individually attacking them. DNA logic clustering aids in this process, as the strength of these generators relies on component cohesion. Because the components are cryptanalyzed individually, the time complexity of the attack is equal to O(C2N) , where N is the length of the largest component, and C is a constant.
In this paper, a modified wavelet neural network (WNN) (or wavenet)-based predictor is introduced to predict link status (congestion with load indication) of each link in the computer network. On the contrary of previous wavenet-based predictors, the proposed modified wavenet-based link state predictor (MWBLSP) generates two indicating outputs for congestion and load status of each link based on th e premeasured power burden (square values) of utilization on each link in the previous time intervals. Fortunately, WNNs possess all learning and generalization capabilities of traditional neural networks. In addition, the ability of such WNNs are efficiently enhanced by the local characteristics of wavelet functions to deal with sudden changes and burst network load. The use of power burden utilization at the predictor input supports some non-linear distri butions of the predicted values in a more efficient manner. The proposed MWBLSP pre dictor can be used in the context of active congestion control and link load balancing techniques to improve the performance of all links in the network with best utilization of network resources.
The security of communications in various transmitted information’s forms such as video, audio, image, and even text and preserving them from attackers has become of great importance in the age of the Internet and cellular networks. Perhaps one of the most important media used to transmit information is digital images. They are distinguished from video and audio by their lack of complexity, and at the same time they are distinguished from text by the possibility of containing more information. Due to the necessity of transmitting huge amounts of information via digital images through additive white Gaussian noise (AWGN) channels for various applications. This transmission of images over unsecured channels is vulnerable to many attacks that must be protected by information security tools. In this research, a hybrid chaos-based system was developed to encrypt and secure images and send them via an orthogonal frequency division multiplexing (OFDM) channel, which leads to transferring large amounts of transmitted information in a short time, with very little interference between the data, and maintaining the transfer rate. Two chaotic techniques, Rossler and Modified Chau system, are used together to create a secret encryption key. This combination of chaotic systems provides highly random sensitive keys with amplitude of 10252 that are difficult to predict by the attacker and which makes restoring the original image very difficult in the event of a very small change in the chaotic parameters. Many tests were conducted to determine the strength of the proposed system, including statistical and differential analysis and entropy to verify the strength of the image security approach, in addition to applying some types of attacks to the encrypted image, such as noise and cropping different parts of the image. It is clear that the proposed scheme has strong immunity to these attacks. This was proven by the comparative experimental results. The entropy ratio was very excellent compared to the rest of the results obtained in the rest of the research. This was also the case with the values of (NPCR), (UACI), (NPCR), and Mean Square Error (MSE) was also very good as compared with other researches in the literature. The proposed security approach for OFDM gave a low link and a low bit error rate. And a higher signal-to-noise ratio (PSNR).
An accurate model for a permanent magnet syn- chronous generator (PMSG) is important for the design of a high-performance PMSG control system. The performance of such control systems is influenced by PMSG parameter variations under real operation conditions. In this paper, the electrical parameters of a PMSG (the phase resistance, the phase inductance and the rotor permanent magnet (PM) flux linkage) are identified by a particle swarm optimisation (PSO) algorithm based on experimental tests. The advantages of adopting the PSO algorithm in this research include easy implementation, a high computational efficiency and stable convergence characteristics. For PMSG parameter identification, the normalised root mean square error (NRMSE) between the measured and simulated data is calculated and minimised using PSO.
This article emphasizes on a strategy to design a Super Twisting Sliding Mode Control (STSMC) method. The proposed controller depends on the device of Field Programmable Gate Array (FPGA) for controlling the trajectory of robot manipulator. The gains of the suggested controller are optimized using Chaotic Particle Swarm Optimization (PSO) in MATLAB toolbox software and Simulink environment. Since the control systems speed has an influence on their stability requirements and performance, (FPGA) device is taken in consideration. The proposed control method based on FPGA is implemented using Xilinx block sets in the Simulink. Integrated Software Environment (ISE 14.7) and System Generator are employed to create the file of Bitstream which can be downloaded in the device of FPGA. The results show that the designed controller based of on the FPGA by using System Generator is completely verified the effectiveness of controlling the path tracking of the manipulator and high speed. Simulation results explain that the percentage improvement in the Means Square Error (MSEs) of using the STSMC based FPGA and tuned via Chaotic PSO when compared with the same proposed controller tuned with classical PSO are 17.32 % and 13.98 % for two different cases of trajectories respectively.
Optical OFDM based on discrete Hartley transform (DHT-O-OFDM) has been proposed for large-size data mapping intensity modulation direct detection (IM/DD) scheme as an alter- native to the conventional optical OFDM. This paper presents a performance analysis and evaluation of IM/DD optical DC-biased DHT-O-OFDM over diffused multipath optical wireless channels. Zero-padding guard interval along with minimum mean-square error (MMSE) equalizer are used in electrical domain after the direct detection to remove the intersymbol interference (ISI) and eliminate the deleterious effects of the multipath channels. Simulation results show that the ZP-MMSE can effectively reduce the effects of multipath channels. The results also show that the effects of optical wireless multipath channel become more serious as the data signaling order increases.
Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face challenges in extracting essential featuresfrom electricity load data, resulting in diminished forecasting performance. To overcome this limitation, we propose a novel ensemble model that integratesa feature extraction module, densely connected residual block (DCRB), longshort-term memory layer (LSTM), and ensemble thinking. The feature extraction module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port decisionmaking processes, making it a valuable tool for stakeholders in the electricity sector.