Archive

Abstract:

Microgrids (MGs) are having more difficulty sustaining power quality (PQ) as renewable energy sources (RES) become more widely integrated. The issue of supraharmonics (SH), characterized by high-frequency emissions spanning from 2 kHz to 150 kHz, holds significant importance. The switching processes used in these RES power electronic converters are to blame for these harmonics. Traditional passive filters and capacitor banks are ill-equipped to deal with the dynamic changes in system characteristics that occur at the PCC. Voltage swell, unbalance, and power factor problems result from this. The Shunt Active Filter (SAF) has shown superior efficacy in mitigating harmonic issues in power systems. Nonetheless, its performance is contingent upon the rapidity and precision of its control algorithms. This paper employs the parabolic carrier-based pulse-width modulation (PWM) technique to regulate current in SAF, thereby minimizing SH. This method incorporates using a pair of positive and negative parabolic PWM carriers to control the switching states of the two switches in the converter phase leg, simultaneously constraining the current tracking error within the nonlinear parabolic region. The proposed filter is designed using the MATLAB/Simulink environment and used in a modelled MG with specified ratings. The results of the harmonic analysis showed a distortion contribution in the SH range of merely 0.03%. The minimal increase in THD when extending the analysis up to 150 kHz demonstrates the active filter’s effectiveness in suppressing SH.

Keywords:

Harmonic Mitigation,Microgrid,Parabolic PWM,Power Quality,Shunt Active Filter,Supraharmonics,

Refference:

I. Abdrabba, S. I., et al. “Analysis of Feasible Solutions for the Improvement of Voltage Profile in Alkufra Network Containing PV-Generation Unit.” Proceedings of the 14th International Renewable Energy Congress (IREC), Sousse, Tunisia, Mar. 2023, pp. 1–6. 10.1109/IREC59750.2023.10389229
II. Abidin, M. N. Zainal. “IEC 61000-3-2 Harmonics Standards Overview”. Schaffner EMC Inc., Edison, NJ, May 2006. https://www.emcfastpass.com/wp-content/uploads/2017/04/Class_definitions.pdf
III. Addala, S., and I. E. S. Naidu. “Mitigation of PQP in Distributed Generation Using CPD’s.” Proceedings of the International Conference on Futuristic Technologies (INCOFT), Karnataka, India, 25–27 Nov. 2022, pp. 1–4. 10.1109/INCOFT55651.2022.10094476
IV. Alfalahi, S. T. Y., et al. “Sizing Passive Filters for Mitigation of Harmonics in a Low Voltage Network Containing Solar PV Units.” Franklin Open, Vol. 10, No. 1, Mar. 2025, p. 100220. 10.1016/j.fraope.2025.100220
V. Alsaeed, I., and M. Shafiullah. “Harmonic Mitigation Using Hybrid Control Method in Energy Storage Integrated Microgrid.” Proceedings of the 4th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), 2022. 10.1109/REEPE53907.2022.9731404
VI. Al-Sharif, Y. M., G. M. Sowilam, and T. A. Kawady. “Harmonic Analysis of Large Grid-Connected PV Systems in Distribution Networks: A Saudi Case Study.” International Journal of Photoenergy, Vol. 2022, Article ID 8821192, 14 pages, Nov. 2022. 10.1155/2022/8821192
VII. Azzam-Jai, A., and M. Ouassaid. “Control of a Multifunctional PV-Integrated SAPF with Simpler Neural Harmonic Extraction.” Proceedings of the 9th International Conference on Systems and Control, Caen, France, 24–26 Nov. 2021, pp. 44–48. 10.1109/ICSC50472.2021.9666700
VIII. Barva, A., and S. Joshi. “A Comprehensive Survey on Hybrid Active Power Filter Topologies, Controller, and Application in Microgrid.” Proceedings of the IEEE Region 10 Symposium (TENSYMP), 2022. 10.1109/TENSYMP54529.2022.9864377
IX. Barva, A. V., and S. Joshi. “Comparative Analysis of Passive, Active, and Hybrid Active Filters for Power Quality Improvement in Grid-Connected Photovoltaic System.” Proceedings of the 7th International Conference on Computer Applications in Electrical Engineering – Recent Advances (CERA), 2023. 10.1109/CERA59325.2023.10455311
X. Barik, P. K., et al. “Simulation and Real-Time Implementation of a Combined Control Strategy-Based Shunt Active Power Filter in Microgrid.” Sustainable Computing: Informatics and Systems, Vol. 35, Feb. 2025. 10.1016/j.suscom.2025.101103
XI. Cheng, J. “IEEE Standard 519-2014 Compliances, Updates, Solutions and Case Studies.” Schneider Electric, presented by Dr. John Cheng, CEng, CEM, CEA, CMVP. https://studylib.net/doc/25803589/ieee-std-519-1992
XII. Chishti, F., S. Murshid, and B. Singh. “Frequency Adaptive Multistage Harmonic Oscillator for Renewable-Based Microgrid under Nonideal Grid Conditions.” IEEE Transactions on Industrial Electronics, vol. 68, no. 1, Jan. 2021, pp. 358–367. 10.1109/TIE.2020.2965474
XIII. “EMC Standards: A Practical Guide for EN 61000-3-2 – Limits for Harmonic Current Emissions.” Stafford: EMC Standards, 2010. https://www.emcstandards.co.uk/files/61000-3-2_mains_harmonics.pdf?utm_source=chatgpt.com
XIV. Fahad, A. H., and M. S. Reza. “Single-Phase Shunt Active Power Filter Using Parabolic PWM for Current Control.” Proceedings of the 7th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, Aug. 2019, pp. 134–138. 10.1109/SEGE.2019.8859868
XV. Halpin, S. M. “Revisions to IEEE Standard 519-1992.” Proceedings of the IEEE Power Engineering Society General Meeting, Auburn, AL, USA, 2022. 10.1109/TDC.2006.1668665
XVI. International Energy Agency. Renewables 2023: Analysis and forecast to 2028. Paris, France: IEA, 2023. https://www.iea.org/reports/renewables-2023
XVII. Kancharla, P. “Harmonic Elimination by Reference Current Strategy Using Shunt Active Power Filter (SAPF).” AIP Conference Proceedings, Vol. 3007, No. 1, Feb. 2024. 10.1063/5.0194906
XVIII. Khan, A. S., S. Vijay, and S. Doolla. “Nonlinear Load Harmonic Mitigation Strategies in Microgrids: State of the Art.” IEEE Systems Journal, vol. 16, no. 3, 2022, pp. 4243–4255. 10.1109/JSYST.2021.3130612
XIX. Khan, S. A., M. Junaid, and M. Ali. “Design of an Active Power Filter in Cascaded Power Converters for Harmonic Mitigation.” Proceedings of the International Conference on Emerging Power Technologies (ICEPT), 2023. 10.1109/ICEPT58859.2023.10152426
XX. Kumar, A., R. Garg, and P. Mahajan. “Harmonics Mitigation Techniques in Grid Integrated PV-Based Microgrid: A Comparative Analysis.” Proceedings of the First IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies (ICAECT), India, 19–20 Feb. 2021, pp. 634–639. 10.1109/ICAECT49130.2021.9392631
XXI. Li, J., Y. He, L. Xu, and J. Liu. “Design of Hybrid Power Filters for Supraharmonics Suppression in Power Electronics Dominated Power Systems.” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, No. 2, Apr. 2024, pp. 2163–2175. 10.1109/JESTPE.2024.3365167
XXII. Matas-Díaz, F. J., et al. “Active Harmonic Filtering with Selective Overcurrent Limitation for Grid-Forming VSCs: Stability Analysis and Experimental Validation.” IEEE Transactions on Industry Applications, Vol. 60, No. 3, May–June 2024, pp. 4762–4775. 10.1109/TIA.2024.3369595
XXIII. Nireekshana, N., et al. “Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor.” International Journal of Innovative Science and Research Technology, Vol. 9, No. 9, Sep. 2024, pp. 2890–2891. 10.38124/ijisrt/IJISRT24SEP1656
XXIV. Sant, A. V. “Shunt Active Power Filtering with Reference Current Generation Based on Dual Second Order Generalized Integrator and LMS Algorithm.” Energy Reports, vol. 8, 2022, pp. 886–893. 10.1016/j.egyr.2022.08.099
XXV. Xu, L., J. Qiu, Y. He, Q. Deng, and C. Lei. “Research on Active Filter for Supraharmonics Suppression of Power Grid.” IEEE Student Conference on Electric Machines and Systems (SCEMS), Xi’an, China, 2020, pp. 735–740. 10.1109/SCEMS48876.2020.9352290

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Abstract:

The fount of battery in a hybrid automobile plays an important role in furnishing energy to sustain the seamless functionality of the vehicle and mitigate petroleum expenditure. This power source is mostly used as a collection of many cells combined into a single power source for the vehicle. This research concentrates on considering the charging attributes of the battery supplied in a hybrid automobile, encompassing charging flow, the state of charge (SoC), and the charging environment thermal influence on the efficacy of the battery power. Matlab Simulink software is applied to investigate and simulate the characteristic curves of the above factors to propose effective ways to use batteries in hybrid vehicles. The results of the article are used as basic knowledge for researching the battery, and at the same time serve the development of batteries for electric vehicles in the future.

Keywords:

Charging time,Battery,Hybrid,Matlab Simulink,Pin Ni-MH,

Refference:

I. Buchmann I., Batteries I., in a Portable World: A Handbook on Rechargeable Batteries for Non-Engineers. Cadex Electronics Inc., 2012.
II. Demirci, O., Taskin, S., Schaltz, E., and Acar Demirci, B. “Review of Battery State Estimation Methods for Electric Vehicles—Part I: SOC Estimation.” Journal of Energy Storage, vol. 87, 2024, article 111435. 10.1016/j.est.2024.111435
III. Dimauro, L. “Power Transmission Systems: From Traditional to Magnetic Gearboxes.” 2021. PhD diss., Politecnico di Torino. IRIS Politecnico di Torino. https://iris.polito.it/retrieve/e384c434-246e-d4b2-e053-9f05fe0a1d67/PhDThesis_Dimauro_Luca.pdf
IV. Ferriday, T. B., and Middleton, P. H. “Alkaline Fuel Cell Technology—A Review.” International Journal of Hydrogen Energy, vol. 46, no. 35, 2021, pp. 18489-18510. 10.1016/j.ijhydene.2021.02.203
V. Gifford, P., Adams, J., Corrigan, D., and Venkatesan, S. “Development of Advanced Nickel/Metal Hydride Batteries for Electric and Hybrid Vehicles.” Journal of Power Sources, vol. 80, nos. 1-2, 1999, pp. 157-63. 10.1016/S0378-7753(99)00070-1
VI. Jung-Ho, W. “Applications of Proton Exchange Membrane Fuel Cell Systems.” Renewable and Sustainable Energy Reviews, vol. 11, no. 8, 2007, pp. 1720-38. 10.1016/j.rser.2006.01.005
VII. Khan, M. M. R., Amin, M. K., and Chakraborty, N. “Advances and Prospects of Biodegradable Polymer Nanocomposites for Fuel Cell Applications.” Biodegradable and Biocompatible Polymer Nanocomposites, edited by Deba Kumar Tripathy et al., Elsevier, 2023, pp. 599-637. 10.1016/B978-0-323-91696-7.00018-0
VIII. Linden, D., and Reddy, T. B. Linden’s Handbook of Batteries, Fourth Edition. McGraw-Hill, 2011, AccessEngineering.
IX. Luntz, A. “Beyond Lithium-Ion Batteries.” SLAC National Accelerator Laboratory, vol. 6, no. 2, 2015, pp. 224-313.
X. Martínez-Sánchez, R., Molina-García, A., Mateo-Aroca, A., and Ramallo-González, A. P. “Evaluating a Nickel–Metal Hydride (NiMH) Battery Regeneration Patent Based on a Non-Intrusive and Unsupervised Prototype.” Batteries, vol. 10, no. 11, 2024, article 402. 10.3390/batteries10110402
XI. Madani, S. S., Ziebert, C., and Marzband, M. “Thermal Behavior Modeling of Lithium-Ion Batteries: A Comprehensive Review.” Symmetry, vol. 15, no. 8, 2023, article 1597. 10.3390/sym15081597
XII. Noga, M., and Juda, Z. “The Application of NiMH Batteries in a Light-Duty Electric Vehicle.” Technical Transactions, no. 1, 2019, pp. 197-222. 10.4467/2353737XCT.19.014.10054
XIII. Nguyen, Q. T. “Solid Oxide Fuel Cell Technology—Features and Applications.” Solid State Ionics, vols. 174, nos. 1-4, 2004, pp. 271-77. 10.1016/j.ssi.2004.07.042
XIV. Pierozynski, B. “On the Low Temperature Performance of Nickel-Metal Hydride (NiMH) Batteries.” International Journal of Electrochemical Science, vol. 6, 2011, pp. 860-66.
XV. Quintero Pulido, D. F., Covrig, C.-F., and Bruchhausen, M. “On the Performance of Portable NiMH Batteries of General Use.” Batteries, vol. 11, no. 1, 2025, article 30. 10.3390/batteries11010030
XVI. Sammes, N., Bove, R., and Stahl, K. “Phosphoric Acid Fuel Cells: Fundamentals and Applications.” Current Opinion in Solid State and Materials Science, vol. 8, no. 5, 2004, pp. 372-78. 10.1016/j.cossms.2005.01.001

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Abstract:

A Group Key Agreement Protocol enables secure multi-party communication by establishing a common cryptographic key, which is especially critical at the edge layer of IoT networks where devices often operate in decentralized and resource-constrained environments. However, existing protocols face several challenges, including high computational overhead, single points of failure, and a lack of integrity validation during the Distribution of the Group Key. To address these challenges, we propose a lightweight edge-layer protocol that combines Shamir’s Secret Sharing Scheme (SSS) and Elliptic Curve Cryptography (ECC) for secure and efficient group key distribution among IoT edge devices. ECC (Curve25519) is used for secure peer-to-peer sharing, with key sizes that are 12 times smaller and operations that are four times faster than traditional RSA. SSS splits the group key into shares and reconstructs it using a threshold, reducing computation and eliminating the need for full key generation on each device. It also removes single points of failure because no device retains the complete key. ECC enables secure peer-to-peer exchange of encrypted shares using ChaCha20 for efficient confidentiality. ChaCha20 enhances encryption speed, performing nearly three times faster than AES on resource-constrained devices. To ensure shared authenticity and detect tampering, HMAC is applied. This offers a lightweight integrity check suitable for constrained IoT devices. The proposed protocol is quantitatively validated through entropy and key-strength analysis, confirming 128-bit equivalent security and O(n) scalability up to 100 nodes. Communication-cost evaluation demonstrates low bandwidth overhead, while formal verification using BAN Logic and ProVerif under the Dolev–Yao adversarial model establishes confidentiality, authenticity, and forward secrecy with provable resilience against replay, impersonation, and man-in-the-middle attacks.

Keywords:

ChaCha20,Elliptic Curve Cryptography,Group Key Agreement,HMAC Integrity,IoT Security,Lightweight Cryptography,Secure Key Distribution,Shamir’s Secret Sharing,

Refference:

I. Abdel Hakeem, S. A., & Kim, H., Centralized threshold key generation protocol based on Shamir Secret Sharing and HMAC authentication, Sensors, 22, 331, 2022. 10.3390/s22010331.
II. Ashraf, Z., Sohail, A., & Yousaf, M., Robust and lightweight symmetric key exchange algorithm for next-generation IoE. Internet of Things, 22, 100703, 2023. 10.1016/j.iot.2023.100703.
III. Cui, W., Cheng, R., Wu, K., Su, Y., & Lei, Y. (2021). A certificateless authenticated key agreement scheme for the power IoT, Energies, 14(19), 6317, 2021. 10.3390/en14196317.
IV. Ding, Z., et al., A lightweight and secure communication protocol for the IoT environment, IEEE Transactions on Dependable and Secure Computing, 21(3),2024, 1050–1067. 10.1109/TDSC.2023.3267979.
V. Fang, D., Qian, Y., & Hu, R. Q., A flexible and efficient authentication and secure data transmission scheme for IoT applications, IEEE Internet of Things Journal, 7(4),2020, 3474–3484. 10.1109/JIOT.2020.2970974.
VI. Ghebleh, M., Kanso, A., & Abuhasan, H., Verifiable secret sharing with changeable access structure, Discrete Mathematics, Algorithms and Applications, 2024. 10.1142/S179383092450037X.
VII. Lee, J., Kim, M., Park, K., Noh, S.-K., Bisht, A., Das, A. K., & Park, Y.-H., Blockchain-based data access control and key agreement system in IoT environment, Sensors, 23(11), 5173, 2023. 10.3390/s23115173
VIII. Lemnouar, N., Security limitations of Shamir’s secret sharing, Journal of Discrete Mathematical Sciences and Cryptography,2022, 1–13. 10.1080/09720529.2021.1961902.
IX. Li, B., Zhang, G., Lei, S., Fu, H., & Wang, J., A Lightweight Authentication And Key Agreement Protocol For Iot Based On ECC, In Proceedings of the 2021 International Conference on Advanced Computing and Endogenous Security, 2022, (pp 1–5), Nanjing, China. 10.1109/IEEECONF52377.2022.10013341.
X. Meng, K., Miao, F., Huang, W., & Xiong, Y., Threshold changeable secret sharing with secure secret reconstruction, Information Processing Letters, 157, 105928, 2020. 10.1016/j.ipl.2020.105928.
XI. Muhammad, T., Allaoua Chelloug, S., Alabdulhafith, M., & Abd El-Latif, A. A., Lightweight authentication protocol for connected medical IoT through privacy-preserving access, Egyptian Informatics Journal, 2024. 10.1016/j.eij.2024.100474.
XII. Oudah, M. S., & Maolood, A. T., Lightweight authentication model for IoT environments based on enhanced elliptic curve digital signature and Shamir Secret Share, International Journal of Intelligent Engineering and Systems, 15(5), 2024,81–90. 10.22266/ijies2022.1031.08.
XIII. R. Subrahmanyam, N. R. Rekha, and Y. V. S. Rao, “Authenticated Distributed Group Key Agreement Protocol Using Elliptic Curve Secret Sharing Scheme,” in IEEE Access, vol. 11, pp. 45243-45254, 2023. 10.1109/ACCESS.2023.3274468.
XIV. Sheikh, A. S., Keerthi, A., Dhuli, S., Likhita, G., Jahnavi, B. S. V. N. J., & Atik, F., A novel security system for IoT applications,In Proceedings of the 2021 12th International Conference on Computing, Communication and Networking Technologies (ICCCNT),2021, (pp. 1–5). Kharagpur, India. 10.1109/ICCCNT51525.2021.9579502.
XV. S., K., & Rengarajan, A., Advancing IoT security: A comprehensive survey of lightweight cryptography solutions. International Journal of Advanced Research in Computer and Communication Engineering, 2024. 10.17148/ijarcce.2024.13511.
XVI. Tomar, A., Gupta, N., D. L., Rani, S. P., & Tripathi, S., Blockchain-assisted authenticated key agreement scheme for IoT-based healthcare system, Internet of Things, 23, 100849, 2023. 10.1016/j.iot.2023.100849.
XVII. Vora, P., Upadhyay, R., & Wazid, M., Secure and lightweight key management scheme for resource-constrained IoT devices, Computer Networks, 245, 110853, 2024. 10.1016/j.comnet.2024.110853.
XVIII. Weidner, M., Klepmann, M., Hugenroth, D., & Beresford, A. R., Key agreement for decentralized secure group messaging with strong security guarantees, In Proceedings,2021, (pp. 2024–2045). 10.1145/3460120.3484542.
XIX. Zhang, R., Zhang, L., Choo, K.-K. R., & Chen, T., Dynamic authenticated asymmetric group key agreement with sender non-repudiation and privacy for group-oriented applications, IEEE Transactions on Dependable and Secure Computing, 20(1),2023, 492–505. 10.1109/TDSC.2021.3138445.

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Abstract:

This study introduces a novel system called Generalized Joint Permutation-Carrier Index Modulation Based MC-DCSK (GJPC-IM-MCDCSK) aimed at enhancing energy efficiency, spectral efficacy, and data transmission rates of GCI-DCSK technology by generating and utilizing permuted forms of the reference chaotic series with quasi-orthogonal properties to distribute the modulated bits across each operational carrier. Furthermore, rather than employing correlation detection, the receiving end of the proposed system utilizes greedy detection to streamline the process. Evaluating the data rate, spectral efficiency, and complexity of the proposed GJPC-IM-MCDCSK system against GCI-DCSK, CI-DCSK1, and MC-DCSK technologies illustrates the superior performance of the recommended design. To illustrate the proposed design's superiority, the simulated Bit Error Rate (BER) performance is evaluated for the GJPC-IM-MCDCSK scheme and compared with the GCI-DCSK, CI-DCSK1, and MC-DCSK systems under AWGN and multipath Rayleigh fading channels.

Keywords:

Active carrier,permutation,Index,greedy detection,correlation detection,

Refference:

I. Al Bassam, N., & Al-Jerew, O. (2021). Design and implementation of enhanced permutation index differential chaos shift keying system. Physical Communication, 46, 101312. 10.1016/j.phycom.2021.101312
II. Au, M., Kaddoum, G., Alam, M. S., Başar, E., & Gagnon, F. (2019). Joint code-frequency index modulation for IoT and multi-user communications. IEEE Journal of Selected Topics in Signal Processing, 13, 1223–1236. 10.1109/JSTSP.2019.2933056
III. Bai, C., Ren, H. P., & Grebogi, C. (2019). Experimental phase separation differential chaos shift keying wireless communication based on matched filter. IEEE Access, 7, 25274–25287. 10.1109/ACCESS.2019.2900729
IV. Basar, E., Wen, M., Mesleh, R., Di Renzo, M., Xiao, Y., & Haas, H. (2017). Index modulation techniques for next-generation wireless networks. IEEE Access, 5, 16693–16746. 10.1109/ACCESS.2017.2737528
V. Başar, E., Aygölü, Ü., Panayırcı, E., & Poor, H. V. (2013). Orthogonal frequency division multiplexing with index modulation. IEEE Transactions on Signal Processing, 61, 5536–5549. 10.1109/TSP.2013.2279771
VI. Cai, X., Xu, W., Lau, F. C. M., & Wang, L. (2020). Joint carrier-code index modulation aided M-ary differential chaos shift keying system. IEEE Transactions on Vehicular Technology, 69, 15486–15499. 10.1109/TVT.2020.3041927
VII. Cai, X., Xu, W., Miao, M., & Wang, L. (2020). Design and performance analysis of a new M-ary differential chaos shift keying with index modulation. IEEE Transactions on Wireless Communications, 19, 846–858. 10.1109/TWC.2019.2949315
VIII. Cai, X., Xu, W., Wang, L., & Xu, F. (2019). Design and performance analysis of differential chaos shift keying system with dual-index modulation. IEEE Access, 7, 26867–26880. 10.1109/ACCESS.2019.2901016
IX. Chen, H., Chen, P., Wang, S., Lai, S., & Chen, R. (2022). Reference-modulated PI DCSK: A new efficient chaotic permutation index modulation scheme. IEEE Transactions on Vehicular Technology, 71, 9663–9673. 10.1109/TVT.2022.3181180
X. Cheng, G., Chen, X., Liu, W., & Xiao, W. (2019). GCI DCSK: Generalized carrier index differential chaos shift keying modulation. IEEE Communications Letters, 23, 2012–2016. 10.1109/LCOMM.2019.2933827
XI. Cheng, G., Wang, L., Chen, Q., & Chen, G. (2018). Design and performance analysis of generalised carrier index M ary differential chaos shift keying modulation. IET Communications, 12, 1324–1331. 10.1049/iet-com.2017.0800
XII. Cheng, G., Wang, L., Xu, W., & Chen, G. (2017). Carrier index differential chaos shift keying modulation. IEEE Transactions on Circuits and Systems II: Express Briefs, 64, 907–911. 10.1109/TCSII.2016.2613093
XIII. Dai, W., Yang, H., Song, Y., & Jiang, G. (2018). Two-layer carrier index modulation scheme based on differential chaos shift keying. IEEE Access, 6, 56433–56444. 10.1109/ACCESS.2018.2872748
XIV. Fang, Y., Peng, D., Ma, H., Han, G., & Li, Y. (2024). A neural network-aided detection scheme for index modulation DCSK system. IEEE Transactions on Vehicular Technology, 73, 2109–2121. 10.1109/TVT.2023.3313813
XV. Fang, Y., Zhuo, J., Ma, H., Mumtaz, S., & Li, Y. (2023). Design and analysis of a new index-modulation aided DCSK system with frequency and time resources. IEEE Transactions on Vehicular Technology, 72, 7411–7425. 10.1109/TVT.2023.3238379
XVI. Galias, Z., & Maggio, G. M. (2001). Quadrature chaos shift keying: Theory and performance analysis. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48, 1510–1519. 10.1109/TCSI.2001.972858
XVII. Hasan, F. S. (2021). Design and analysis of grouping subcarrier index modulation for differential chaos shift keying communication system. Physical Communication, 47, 101325. 10.1016/j.phycom.2021.101325
XVIII. Hasan, F. S., & Fahad, H. F. (2020). Design and analysis of an OFDM based orthogonal multilevel code shifted differential chaos shift keying. Indonesian Journal of Electrical Engineering and Computer Science, 20, 1369–1378. 10.11591/ijeecs.v20.i3.pp1369-1378
XIX. Herceg, M., Vranjes, D., Kaddoum, G., & Soujeri, E. (2018). Commutation code index DCSK modulation technique for high data rate communication systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 65, 1954–1958. 10.1109/TCSII.2018.2817930
XX. Herceg, M., Kaddoum, G., Vranjes, D., & Soujeri, E. (2018). Permutation index DCSK modulation technique for secure multiuser high data rate communication systems. IEEE Transactions on Vehicular Technology, 67, 2997–3011. 10.1109/TVT.2017.2774108
XXI. Huang, T., Wang, L., Xu, W., & Lau, F. C. M. (2016). Multilevel code shifted differential chaos shift keying system. IET Communications, 10, 1189–1195. 10.1049/iet-com.2015.1109
XXII. Kaddoum, G., Richardson, F. D., & Gagnon, F. (2013). Design and analysis of a multi carrier differential chaos shift keying communication system. IEEE Transactions on Communications, 61, 3281–3291. 10.1109/TCOMM.2013.071013.130225
XXIII. Kaddoum, G., Soujeri, E. (2016). NR DCSK: A noise reduction differential chaos shift keying system. IEEE Transactions on Circuits and Systems II: Express Briefs, 63, 648–652. 10.1109/TCSII.2016.2532041
XXIV. Kaddoum, G., Soujeri, E., & Nijsure, Y. (2016). Design of a short reference noncoherent chaos based communication systems. IEEE Transactions on Communications, 64, 680–689. 10.1109/TCOMM.2015.2514089
XXV. Kaddoum, G., Tran, H. V., Kong, L., & Atallah, M. (2017). Design of simultaneous wireless information and power transfer scheme for short reference DCSK communication systems. IEEE Transactions on Communications, 65, 431–443. 10.1109/TCOMM.2016.2619707

XXVI. Lau, F. C. M., Cheong, K. Y., & Tse, C. K. (2003). Permutation based DCSK and multiple access DCSK systems. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 50, 733–742. 10.1109/TCSI.2003.812616
XXVII. Li, S., Zhao, Y., & Wu, Z. (2015). Design and analysis of an OFDM based differential chaos shift keying communication system. Journal of Communications, 10, 199–205. 10.12720/jcm.10.3.199-205
XXVIII. Liu, S., Chen, P., & Chen, G. (2021). Differential permutation index DCSK modulation for chaotic communication system. IEEE Communications Letters, 25, 2029–2033
XXIX. Luo, R., Yang, H., Meng, C., & Zhang, X. (2022). A novel SR DCSK based ambient backscatter communication system. IEEE Transactions on Circuits and Systems II: Express Briefs, 69, 1707–1711. 10.1109/TCSII.2021.3109020
XXX. Ma, H., Fang, Y., Chen, P., Mumtaz, S., & Li, Y. (2023). A novel differential chaos shift keying scheme with multidimensional index modulation. IEEE Transactions on Wireless Communications. 10.1109/TWC.2022.3192347
XXXI. Nazar, B., & Hasan, F. S. (2023). Joint grouping subcarrier and permutation index modulations based differential chaos shift keying system. Physical Communication, 61, 102213. 10.1016/j.phycom.2023.102213
XXXII. Nazar, B., & Hasan, F. S. (2024). Enhanced two way cooperative DCSK system via grouping subcarrier permutation index modulation. In Evolution in Signal Processing and Telecommunication Networks (pp. 23–35). Springer. 10.1007/978-981-97-0644-0_3
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Abstract:

This study aims to develop and investigate several classes of interval-valued sets within the framework of Interval-Valued topology. Interval-Valued sets, originally proposed by Yao and later enriched through various generalizations, provide a more expressive structure for modelling uncertainty than classical or fuzzy sets. In this paper, we introduce and analyse different categories of Interval-Valued sets, particularly focusing on their weak and strong forms, and we explore how these forms influence topological behaviour. Special emphasis is placed on the study of minimal and maximal Interval-Valued open sets, pre-open sets, and semi-open sets, which serve as extremal elements in the lattice of Interval-Valued topologies. We also examine the interrelationships between various kinds of Interval-Valued closed sets and their generalized counterparts, thereby clarifying how such structures are embedded within the hierarchy of Interval-Valued topology. By establishing these connections, we provide a deeper understanding of the internal organisation of generalized closed sets in this setting. To support the theoretical results, illustrative and carefully constructed examples are presented, which highlight both the necessity and the distinctions among the introduced concepts. Overall, this work contributes to the enrichment of Interval-Valued topological theory and lays the foundation for further applications in mathematical modelling and decision-making under uncertainty.

Keywords:

Mini-IVOs,Maxi-IVOs,Mini-IVSOs,Mini-IVPOs,Maxi-IVSOs ,Maxi-IVPOs ,

Refference:

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Abstract:

This research focuses on utilizing Convolutional Neural Networks (CNN) to predict the likelihood of colorectal cancer by analyzing medical images of cancerous and non-cancerous tissues. The model demonstrates strong performance with high precision, recall, and accuracy, and employs various techniques such as data augmentation and early stopping to improve generalization and prevent overfitting. The study highlights the potential of machine learning in enhancing diagnostic accuracy and supporting oncologists in making informed treatment decisions.

Keywords:

CNN,Colorectal Cancer,Confusion matrix,Medical Imaging,

Refference:

I. Alshakree F, Akbas A, Rahebi J. “Human Identification Using Palm Print Images Based on Deep Learning Methods and Gray Wolf Optimization Algorithm.” Signal Image Video Process. 18, 2024, pp. 961–973. 10.1007/s11760-023-02787-6.
II. Al Shalchi N F A, Rahebi J. “Human Retinal Optic Disc Detection with Grasshopper Optimization Algorithm. Multimed.” Tools Appl. 81, 2022, pp. 24937–24955. 10.1007/s11042-022-12838-8.
III. Ananthakrishnan B, Shaik A, Chakrabarti S, Shukla V, Paul D, Kavitha M S. “Smart Diagnosis of Adenocarcinoma Using Convolution Neural Networks and Support Vector Machines.” Sustainability 15, 2023, pp. 1399-1417. 10.3390/su15021399.
IV. Ben Hamida A, Devanne M, Weber J, Truntzer C, Derangère V, Ghiringhelli F, Forestier G, Wemmert C. “Deep Learning for Colon Cancer Histopathological Images Analysis.” Comput. Biol. Med. 36, 2021, 104730. 10.1016/j.compbiomed.2021.104730.
V. Chahal P K, Pandey S A. “Hybrid Weighted Fuzzy Approach for Brain Tumor Segmentation Using MR Images.” Neural Comput. Appl. 35, 2023, pp. 23877–23891. 10.1007/s00521-021-06010-w.
VI. Du Y, Hu L, Wu G, Tang Y, Cai X, Yin L. “Diagnoses in Multiple Types of Cancer Based on Serum Raman SpectroscopyCombined with a Convolutional Neural Network: Gastric Cancer, Colon Cancer, Rectal Cancer, Lung Cancer.” Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 298, 2023, 122743. 10.1016/j.saa.2023.122743.
VII. Gonzalez Y, Shen C, Jung H, Nguyen D, Jiang S B, Albuquerque K, Jia X. “Semi-Automatic Sigmoid Colon Segmentation in CT for Radiation Therapy Treatment Planning via an Iterative 2.5-D Deep Learning Approach.” Med. Image Anal. 68, 2021, 101896. 10.1016/j.media.2020.101896.

VIII. https://github.com/tampapath/lung_colon_image_set.
IX. Joshi R. “Gender Disparities: 5 Year Survival Rates of Elderly Colorectal Cancer Patients with Depression.” Ph.D. Thesis, Walden University, Minneapolis, MN, USA, 2023.
X. Karthikeyan A, Jothilakshmi S, Suthir S. “Colorectal Cancer Detection Based on Convolutional Neural Networks (CNN) and Ranking Algorithm.” Meas. Sens. 31, 2024, 100976. 10.1016/j.measen.2023.100976.
XI. Kour H, Manhas J, Sharma V. “Usage and Implementation of Neuro-Fuzzy Systems for Classification and Prediction in the Diagnosis of Different Types of Medical Disorders: A Decade Review.” Artif. Intell. Rev. 53, 2020, pp. 4651–4706. 10.1007/s10462-020-09804-x.
XII. Masud M, Sikder N, Al Nahid A, Bairagi A K, Alzain M A. “A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-based Classification Framework.” Sensors 21, 2021, pp. 748-767. 10.3390/s21030748.
XIII. Nisha J S, Gopi V P, Palanisamy P. “Automated Colorectal Polyp Detection Based on Image Enhancement and Dual-Path CNN Architecture.” Biomed. Signal Process. Control 73, 2022, 103465. 10.1016/j.bspc.2021.103465.
XIV. Nazari E, Aghemiri M, Avan A, Mehrabian A, Tabesh, H. “Machine Learning Approaches for Classification of Colorectal Cancer with and without Feature Selection Method on Microarray Data.” Gene Rep. 25, 2021, 101419. 10.1016/j.genrep.2021.101419.
XV. Nanni L, Fantozzi C, Loreggia A, Lumini A. “Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation.” Sensors 23, 2023, 4688. 10.3390/s23104688.
XVI. Siegel R L, Wagle N S, Cercek A, Smith R A, Jemal A. “Colorectal Cancer Statistics, 2023” J. Clin. 73, 2023, 233–254. 10.3322/caac.21772.
XVII. Talukder M A, Islam M M, Uddin M A, Akhter A, Hasan K F, Moni M A. “Machine Learning-Based Lung and Colon Cancer Detection Using Deep Feature Extraction and Ensemble Learning.” Expert Syst. Appl. 205, 2022, pp. 117695. 10.1016/j.eswa.2022.117695.
XVIII. Tulum G, Osman O, Bolat B, Dandin O, Ergin T, Cuce, F. “Colonic Polyp Classification Using Projection Image and Convolutional Neural Network.” Proceedings of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 24–26 April 2019, pp. 1–4. 10.1109/EBBT.2019.8741701.
XIX. Yaghoubi A, Khazaei M, Avan A, Hasanian S M, Soleimanpour S. “The Bacterial Instrument as a Promising Therapy for Colon Cancer.” Int. J. Colorectal Dis. 35, 2020, pp. 595–606. 10.1007/s00384-020-03535-9.
XX. Yaghoubi E, Yaghoubi E, Khamees A, Vakili A H. “A Systematic Review and Meta-Analysis of Artificial Neural Network, Machine Learning, Deep Learning, and Ensemble Learning Approaches in Field of Geotechnical Engineering.” Neural Comput Appl. 26, 2024, pp. 12655 – 12699. 10.1007/s00521-024-09893-7.
XXI. Yusupov Z, Yaghoubi E, Yaghoubi, E. “Controlling and Tracking the Maximum Active Power Point in a Photovoltaic System Connected to the Grid Using the Fuzzy Neural Controller.” Proceedings of the 2023 14th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 30 November -–02 December 2023, pp. 1–5. 10.1109/ELECO60389.2023.10416016.
XXII. Zhang R, Zheng Y, Mak T W C, Yu R, Wong S H, Lau, J Y W, Poon C C Y. “Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features from Nonmedical Domain.” IEEE J. Biomed. Health Inform. 21, 2016, pp. 41–47. 10.1109/JBHI.2016.2635662.

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