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Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

computers, Materials & Continua 2022年第2期70卷 3821-3835页

作者： Nitin Mittal Harbinder Singh Vikas Mittal Shubham Mahajan Amit Kant Pandit Mehedi Masud Mohammed Baz Mohamed Abouhawwash Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India School of VLSI Design and Embedded Systems National Institute of TechnologyKurukshetra136119India School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral *** improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the *** Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter *** meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user ***-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of *** this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary *** results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

关键词： Cognitive radio meta-heuristic algorithm cognitive decision engine salp swarm algorithm

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine 13

Development of a Model for Discriminating Electroencephalogr...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Tanabe, Akifumi Nakatani, Kyosuke Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Kamae-Shigenobu, Kazue Shinosaki, Kazuhiro Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Aino University Department of Medical Engineering Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Aomori University Faculty of Software and Information Technology Aomori Japan Osaka University Cyber Media Center Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (纸本)9798350373332

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients. © 2024 IEEE.

关键词： Electroencephalography

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Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

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computer systems Science & engineering 2023年第12期47卷 2741-2757页

作者： Abdelwahed Motwakel Hala J.Alshahrani Jaber S.Alzahrani Ayman Yafoz Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and *** of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so *** is expressed in several means,like facial and speech expressions,gestures,and written *** recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)*** article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this *** presented DHODBN-EC model aims to examine the existence of distinct emotion classes in *** the introductory level,the DHODBN-EC technique pre-processes the tweets at different ***,the word2vec feature extraction process is applied to generate the word embedding *** emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class ***,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN *** extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

关键词： Deer hunting optimization deep belief network emotion classification Twitter data sentiment analysis english corpus

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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A Low-power High-gain Inverter Stacking Amplifier with Rail-to-Rail Output 3

A Low-power High-gain Inverter Stacking Amplifier with Rail-...

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3rd IEEE International Conference on Design and Test of Integrated Micro and Nano-systems, DTS 2021

作者： Shad, Erwin H. T. Moeinfard, Tania Molinas, Marta Ytterdal, Trond Faculty of Information Technology and Electrical Engineering Engineering Cybernetics Trondheim Norway York University Faculty of Electrical Engineering and Computer Science Toronto Canada Faculty of Information Technology and Electrical Engineering Electronic Systems Trondheim Norway

ISBN: (纸本)9780738132631

In this article, a rail-to-rail low-power amplifier is presented based on stacking inverter-based amplifiers. The output voltages of each inverter-based amplifier are converted to a current and then mirrored to the output so that a rail-to-rail output is achieved. Besides, extensive simulations have been carried out to show the effect of drain-source voltage on the intrinsic gain of a transistor. Based on these simulations, a minimum supply voltage is chosen to achieve high open-loop gain and low closed-loop gain error. All the simulations are carried out in a commercially available 0.18 μm CMOS technology. The proposed amplifier achieves 88 dB open-loop gain. It is exploited in a capacitively-coupled amplifier structure. The closed-loop gain is 40 dB in the bandwidth of 0.1 Hz to 10 kHz when the power consumption is 0.54 μW at a 1.2 V supply voltage. The total input-referred noise is 4.7 μVrms in the whole bandwidth. The proposed neural amplifier achieved 0.02 SEF in the bandwidth from 200 Hz to 10 kHz. The proposed amplifier achieved a rail-to-rail output swing while the SEF is among the best reported SEF in the literature. Besides, to show the robustness of the proposed structure in the presence of process and mismatch variation, 500 Monte Carlo simulations are carried out. The PSRR and CMRR mean values are 89 dB and 68 dB, respectively. Finally, the proposed neural amplifier area consumption is 0.03 mm2 without pads. © 2021 IEEE.

关键词： Bandwidth

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Children's Speech Disorders Identification and Therapy Treatment

Children's Speech Disorders Identification and Therapy Treat...

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Advancements in Computing (ICAC), International Conference on

作者： Pasan Karunasekara Sheshan Deshitha Dinuja De Alwis Desan Karunarathna Shashika Lokuliyana Narmada Gamage Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute Information Technology Malabe Sri Lanka

PhraseFluent is a project to develop a complete web-based solution to help people who are suffering from speech disorders. This application is specifically designed for people who are suffering from articulation and stuttering speech disorders. There are few solutions available for speech disorders identification and speech therapies. Those available applications have several drawbacks that lead to the research gap for this research. Those findings do not have any disorder-specific speech detection methods. Those applications generally detect speech disorders not the type of the disorder. Also, the speech therapy exercises suggest applications are practicing words according to the order of the alphabet. Those solutions are not based on the sounds that need to be practiced in order to cure speech disorders. Also, applications that have detection mechanisms are not currently available for wide access. This application mainly contains React-based interactive interfaces and a Python-programmed backend with machine-learning algorithms for functionality. This application can help to automate the process of detecting speech disorders and suggest therapy activities. Also, this application can track the patient's progress with the therapies. Used datasets contain the data with 70% accuracy and the test results gave a 68% percent success rate. This approach will be the first step in automating the complete process of speech disorder diagnosis.

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Nonequilibrium phase transitions of a nematic liquid crystal under ac field-driven electroconvection

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Physical Review E 2022年第1期106卷 014702-014702页

作者： Jong-Hoon Huh Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka 820-8502 Japan

We report on phase transitions between isotropic and nematic liquid crystal phases in nonequilibrium systems (NESs). ac field-driven electroconvection (EC) provides an NES, which can be well controlled by the voltage V and frequency f; it arises electrohydrodynamically at a threshold voltage Vc. In continuous cooling and heating processes with various rates R, the critical temperature Tc was determined at a critical time tc for phase transitions. Moreover, the morphological and dynamical features in the phase transitions were examined using an electro-optical image processing method. In comparison with an equilibrium system (V=0), two typical turbulent ECs (i.e., NESs), which are called dynamic scattering mode 1 (DSM1 for V>Vc) and DSM2 (for V≫Vc), were examined to understand the nonequilibrium phase transitions. In particular, our results show that in high voltage-induced turbulence (i.e., DSM2), Tc can be determined effectively without considering R; this provides a possibility for a material technology application in nonequilibrium-based circumstances.

关键词： Dissipative dynamics Patterns in complex systems Phase transitions by order Transition temperature Liquid crystals

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A Compact Rhombus Shaped Antenna with Extended Stubs for Ultra-Wideband Applications

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computers, Materials & Continua 2022年第11期73卷 2637-2650页

作者： Syed Misbah un Noor Muhammad Amir Khan Shahid Khan NZ Jhanjhi Mamoona Humayun Hesham A.Alhumyan Department of Electrical Engineering IIC University of TechnologyPhnom PenhCambodia Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad Campus22060Pakistan School of Computer Science SCSTaylor’s UniversitySubang Jaya47500SelangorMalaysia 5Center for Smart Society 0 CSS5 Faculty of Innovation and TechnologyTaylor’s UniversitySubang Jaya47500Malaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufKSA

Ultra-wideband(UWB)is highly preferred for short distance *** a result of this significance,this project targets the design of a compact UWB *** paper describes a printed UWB rhombusshaped antenna with a partial ground *** achieve wideband response,two stubs and a notch are incorporated at both sides of the rhombus design and ground plane *** excite the antenna,a simple microstrip feed line is *** suggested antenna is built on a 1.6 mm thick FR4 *** proposed design is very compact with overall electrical size of 0.18λ×0.25λ(14×18 mm2).The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance *** proposed design simulated and measured bandwidths are 83.33%and 80%,*** pattern in terms of E-field and H-field are discussed at 4,5.5 and 10 GHz *** proposed design has 65%radiation efficiency and 1.5 dBi peak *** proposed design is simulated in CST(computer Simulation technology)simulator and the simulated design is fabricated for the measured *** simulated and measured findings are in close *** obtained results confirm the application of the proposed design for the ultra-wide band applications.

关键词： Ultra-wideband impedance bandwidth radiation pattern CST electrical size

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