Journal Vol – 21 No – 7, July 2026

Abstract:

Printed Circuit Board Assembly (PCBA) testing continues to represent a key, but relatively expensive, phase in electronics manufacturing, particularly in high-mix, low-volume production environments for small- to medium-sized enterprises (SMEs). In response to growing product complexity and variability in manufacturing quality, test benches are pivotal in delivering good quality, reliability, and regulatory compliance. Although Industry 4.0 drives smart manufacturing through IoT, modular engineering, and data-driven optimization, the existing architecture of academic and industrial PCBA test benches remains fragmented. Automation, connectivity, and analytics are frequently implemented as stand-alone solutions rather than as components of an integrated, cohesive framework. This study presents a conceptual framework for the development of a cyber-physical PCBA test bench, combining modular hardware, in-house manufacturing, IoT-enabled data acquisition, and AI-based analytics in a unified, scalable architecture. Based exclusively on established literature, the framework condenses literature on past IoT implementations, modular test-bench analyses, cost-cutting techniques, and software-centric optimization perspectives associated with SME manufacturing contexts. The framework highlights the shortcomings in scalability, cost efficiency, interoperability, and analytical capability that are identified in the literature by conceptualizing the test bench as a responsive, data-oriented aspect of the larger manufacturing ecosystem. As a conceptual contribution, the framework provides groundwork for future empirical validation and industrial application by moving away from static, deterministic testing structures and towards smart, cyber-physical quality assurance systems grounded in Industry 4.0.

Keywords:

PCBA testing,Industry 4.0,modular test bench,IoT integration,AI-driven optimization,

References:

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Abstract:

Arithmetic Logic Units (ALUs) play a crucial role in contemporary digital systems; yet, the total performance of processors is greatly affected by factors such as power consumption, propagation delay, silicon size, and energy efficiency. To accomplish high performance with low power consumption, this study suggests an ALU that is based on modified Gate Diffusion Input (m-GDI) FinFETs and is built utilising 18-nm technology. For the sake of objectivity, we used standard scaling techniques to standardise previously published ALU architectures across technology nodes to an 18-nm equivalent. Compared to current ALUs based on CMOS (Complementary Metal Oxide Semiconductor), GDI (Gate Diffusion Input), and FinFET (Fin Field-Effect Transistor), the proposed design routinely achieves better results after normalisation. In comparison to previous designs, the proposed layout for the 1-bit ALU may cut power consumption by as much as 42.8%, propagation delay by as much as 53.0%, silicon area by as much as 11.3%, and the Power-Delay Product (PDP) by as much as 76.2%. Similarly, the 8-bit ALU improves power, delay, area, and PDP by 8.4%, 19.0%, 22.2%, and 17.8%, respectively, whereas the Proposed4-bit ALU reduces power by 21.9%, latency by 7.1%, area by 48.5%, and PDP by 73.7% using the same process. With a power consumption of 13.28 µW, a latency of 624 ps, an area of 65.92 µm², and competitive power consumption per bit, the Proposed16-bit ALU still shows outstanding scalability. These findings add credence to the idea that next-generation very large scale integration (VLSI) applications may benefit from building small, high-performance, energy-efficient ALUs by combining m-GDI logic with FinFET technology.

Keywords:

m-GDI,FinFET,ALU,IoT Processors. Nanoscale Technology.,

References:

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Abstract:

The hybrid aluminium matrix composites comprising AA7075 as matrix material reinforced by silicon carbide (SiC) and fly ash (FA) were produced by the stir casting process in order to study the effect of the combination of ceramic and industrial waste reinforcements on the mechanical properties. Ten composite compositions were produced by changing the amounts of SiC and FA. The obtained composites were subjected to Ultimate Tensile Strength (UTS), Impact Strength (IS), and Micro Vickers Hardness (MVH) tests. It was found that the range of UTS was 163.56-218.08 MPa, IS was 31.35-90.25 J, and MVH was 79.61-93.18 kgf/mm². This shows the effect of the composition of the reinforcement on the mechanical properties of the composites. Since more than one property was optimized in the present study, Grey Relational Analysis (GRA) was used to turn the multi-response optimization into a single parameter of GRG. These responses were normalized with the higher-the-better rule, and then the deviation series, grey relational coefficient, and GRG values were calculated for ranking the composite mixes. Of all the tested specimens, the one with the highest value of GRG (0.912) was specimen C9 (AA7075 + 9 wt.% SiC + 3 wt.% FA), which showed excellent mechanical properties in terms of tensile strength, impact strength, and hardness. It can be concluded that the incorporation of the materials of interest together with fly ash has greatly improved the mechanical properties of the AA7075-based hybrid composites. Hence, GRA can be effectively used for multi-objective optimization.

Keywords:

Fly Ash Composites,Metal Matrix Composites,GRA,Process Innovation,Hybrid reinforcement,Taguchi optimization,Silicon Carbide,

References:

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Abstract:

The Helmholtz–Duffing oscillator is a type of nonlinear system that frequently occurs in mechanical, electrical, and physical models incorporating nonlinear restoring forces. It combines the features of Helmholtz and Duffing nonlinearities. Due to the presence of the combined quadratic and cubic nonlinear terms, it is still difficult to obtain exact analytical solutions. This paper proposes an Extended Iteration Method (EIM) to provide better analytical solutions for the Helmholtz-Duffing oscillator. The derived analytical solutions are compared with numerical solutions and those found in the literature to verify the accuracy of the method. Numerical solutions and with those available in the literature. The findings show that the suggested EIM outperforms existing analytical methods in terms of accuracy and simplicity, producing outstanding agreement with numerical solutions. The approach may be applied to many additional severely nonlinear models and offers a robust and effective foundation for researching nonlinear oscillatory systems.

Keywords:

Helmholtz Duffing Oscillator,Iteration Method,Extended Iteration Method,Fourier cosine series.,

References:

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IV. Haque, B. M. I., and Hossain M. M. A.. "A Modified Solution of the Nonlinear Singular Oscillator by Extended Iteration Procedure." Journal of Advances in Mathematics and Computer Science, vol. 34, 2019, pp. 1–9. 10.9734/JAMCS/2019/v34i3-430204
V. Haque, B. M. I., and. Hossain M. M. A. "An Effective Solution of the Cube-Root Truly Nonlinear Oscillator: Extended Iteration Procedure." International Journal of Differential Equations, vol. 2021, 2021, pp. 1–9. 10.1155/2021/7819209
VI. Haque, B. M. I., M. M. A. Hossain, M. Bayezid Bostami, and M. R. Hossain. "Analytical Approximate Solutions to the Nonlinear Singular Oscillator: An Iteration Procedure." British Journal of Mathematics & Computer Science, vol. 14, 2016, pp. 1–7. 10.9734/BJMCS/2016/23263

VII. Haque, B. M. I., M. Bayezid Bostami, M. M. A. Hossain, M. R. Hossain, and M. M. Rahman. "Mickens Iteration Like Method for Approximate Solution of the Inverse Cubic Nonlinear Oscillator." British Journal of Mathematics & Computer Science, vol. 13, 2015, pp. 1–9.
10.9734/BJMCS/2016/22823
VIII. Hosen, A. M., and M. S. H. Chowdhury. "Analytical Approximate Solutions for the Helmholtz-Duffing Oscillator." ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 23, 2015, pp. 17363–17369. ARPN Journals Document URL
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Abstract:

Assurance, traffic management, and Quality of Service (QoS) are still ongoing and interconnected issues in Mobile Ad Hoc Networks (MANETs), which are extremely flexible, infrastructure-free cellular networks. While inexpensive pathfinding is provided by traditional reactive routing technologies, such as the Ad hoc On-Demand Distance Vector (AODV) algorithm, the system is vulnerable to packet-dropping assaults, stream congestion, and a lack of stream separation. The Hybrid AAODV–APBROP routing protocol proposed in this paper combines three identical methods: (i) an adaptive priority-based packet scheduler that prioritizes real-time and emergency flows over best-effort traffic; (ii) a queue-length-based congestion detection component that directs traffic away from overloaded paths; and (iii) a dynamic trust assessment system that identifies and isolates unauthorized packet-dropping nodes. The approach is validated by substantial NS-3 simulations for networks of 20–50 nodes with mobility. In comparison to traditional AODV, the hybrid protocol obtains a hostile node recognition rate surpassing 94%, increases the Packet Delivery Ratio by up to 49.6%, increases throughput by 82.6%, decreases end-to-end delay by 40.3%, and maintains routing cost within reasonable ranges. These findings verify that MANET transmission is demonstrably enhanced and safer when trust, congestion, and QoS are all addressed concurrently at a particular transit level as opposed to individual-criterion techniques.

Keywords:

MANET,AAODV,APBROP,Trust-Based Routing,Congestion Control,Priority-Based Routing,Quality of Service,NS-3,

References:

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Abstract:

This study proposes a framework of a Multi-Constrained Nonlinear Programming Problem (MCNLPP) for optimum sample allocation in multivariate stratified sampling with simultaneous nonlinear time constraint and cost constraint. The proposed model uses the Karush–Kuhn–Tucker conditions and the Lagrange multiplier technique to minimize the weighted sum of variances with operational resource constraints. Logarithmic, piecewise, and quadratic time functions are included as well as the nonlinear cost functions, and the resulting models are used to implement optimum stratum allocations under different operational scenarios using LINGO. The proposed framework, compared with representative deterministic allocation methods under the same resource conditions, demonstrates a balanced approach to survey precision, cost of operations, and time to collect the data. Finally, sensitivity analyses show that allocations are robust to changes in resource constraints. Overall, the framework generalizes the current compromise allocation methods by explicitly including nonlinear operational time and cost in a single deterministic optimization model.

Keywords:

: multivariate stratified sampling,optimum allocation,nonlinear constraints,Survey optimization,Lagrange multipliers,(SDG 12 responsible consumption and production).,

References:

I. Ahmad, Abrar, A. H. Ansari, et al. “COMPROMISE ALLOCATION FOR TWO-STAGE SAMPLING WITH QUADRATIC TRAVEL COST USING DYNAMIC PROGRAMMING TECHNIQUE.” International Journal of Applied Mathematics, vol. 35, no. 1, 2022, pp. 173–80. 10.12732/ijam.v35i1.13.
II. Ahmad, Abrar, Quazzafi Rabbani, et al. COMPROMISE MIXED ALLOCATION IN MULTIVARIATE STRATIFIED SAMPLING WITH TRAVEL COST USING DYNAMIC PROGRAMMING TECHNIQUE. vol. 3, 2022, pp. 89–98.
III. AHMAD, ABRAR, et al. “DETERMINATION OF OPTIMUM SAMPLE SIZE AND VARIANCE IN MULTIVARIATE STRATIFIED SAMPLING WITH NON-LINEAR TIME FUNCTION.” Journal of Science and Arts, vol. 23, no. 2, Jun. 2023, pp. 513–24. 10.46939/j.sci.arts-23.2-a18.
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VIII. Ansari, A. H. ,. Vashney, R. ,. &. Ahsan, M. J. “Compromise Mixed Allocation in Multivariate Stratified Sampling Using Dynamic Programming Technique. .” Journal of Advance Statistics, vol. 3, no. 4, 2018, pp. 45–89.
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XVIII. Neyman, J. “On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection. .” Journal of the Royal Statistical Society, vol. 97, no. 4, 1934, pp. 558–625.
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Abstract:

In this article, a nonlinear dynamical model is developed to study the combined effect of population pressure, urbanization, industrialization, residential land expansion, and afforestation efforts on forest resources dynamics. The governing dynamics of the system were represented by six nonlinear differential equations describing the interactions among forest resources, population pressure, urbanization, industrialization, residential land expansion, and afforestation efforts. The positivity and boundedness of the model solutions are established to ensure the biological validity of the system. Equilibrium points are identified, and sufficient conditions for feasibility and uniqueness of the interior equilibrium are discussed. Local stability of the equilibrium points is analysed through the Jacobian matrix, characteristic equation, and Routh-Hurwitz Stability criteria, while global stability is examined using a suitable Lyapunov function under appropriate parameter restrictions. Numerical simulations are carried out using the MATLAB ODE45 solver to validate the theoretical findings and to illustrate the long-term behaviour of the system. The results show that increased population pressure, urbanization, industrialization, and residential expansion significantly reduce forest resources, while afforestation efforts contribute positively to forest regeneration and restoring ecological balance. Global sensitivity analysis is also performed using Latin Hypercube Sampling and PRCC indices. The findings suggest that sustainable forest management requires controlling anthropogenic pressures along with strengthening afforestation programs. The findings provide important insights for environmental planners and policymakers to design effective forest conservation and sustainable development strategies

Keywords:

Afforestation,Forest resource,Mathematical model,population pressure,Stability analysis,Industrialization.,

References:

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IX. Fanuel, Ibrahim M., et al. "Conservation of forest biomass and forest–dependent wildlife population: Uncertainty quantification of the model parameters." Heliyon 9.6 (2023). 10.1016/j.heliyon.2023.e16948

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XI. Fanuel, Ibrahim M., et al. "Fuzzy modelling on the depletion of forest biomass and forest-dependent wildlife population." Franklin Open 4 (2023): 100033. 10.1016/j.fraope.2023.100033
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XIV. Teru, Ararso Hussen, and Purnachandra Rao Koya. "Mathematical modelling of deforestation of forested area due to lack of awareness of human population and its conservation." Mathematical Modelling and Applications 5.2 (2020): 94. 10.11648/j.mma.20200502.15
XV. Kumar, V. And Bisht, P. “Dynamical Modeling of Forest Depletion under Population and Socio-Economic Pressures for Environmental Sustainability”. International Journal of Environmental Sciences, May 2025, pp. 944-56. 10.64252/jee4de33
XVI. Lata, Kusum, and Arvind Kumar Kumar Misra. "Modeling the effect of economic efforts to control population pressure and conserve forestry resources." Nonlinear Analysis: Modelling and Control 22.4 (2017): 473-488. 10.15388/NA.2017.4.4.
XVII. Matia, Sachindra Nath, et al. "A study on imprecise mathematical model for optimal management and utilization of renewable resource by population." Results in Control and Optimization 12 (2023): 100252. 10.1016/j.rico.2023.100252
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XXI. Ramdhani, Vivi, and E. H. Nugrahani. "Dynamical system of modelling the depletion of forestry resources due to crowding by industrialization." Applied Mathematical Sciences 9.82 (2015): 4067-4079. https://www.m-hikari.com/ams/ams-2015/ams-81-84-2015/p/ramdhaniAMS81-84-2015.pdf
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XXIII. Sebastian, Elizabeth, and Preethi Victor. "A mathematical model on deforestation due to human population and its effect on farm fields: Role of technology in its conservation." Smart Computing and Informatics: Proceedings of the First International Conference on SCI 2016, Volume 2. Singapore: Springer Singapore, 2017. 10.1007/978-981-10-5547-8_44
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XXV. Shukla, J. B., et al. "Degradation and subsequent regeneration of a forestry resource: a mathematical model." Ecological Modelling 44.3-4 (1989): 219-229. 10.1016/0304-3800(89)90031-8
XXVI. Shukla, J. B., Kusum Lata, and A. K. Misra. "Modeling the depletion of a renewable resource by population and industrialization: Effect of technology on its conservation." Natural Resource Modeling 24.2 (2011): 242-267. 10.1111/j.1939-7445.2011.00090.x
XXVII. Zhu, Ling. "On Young's inequality." International Journal of Mathematical Education in Science and Technology 35.4 (2004): 601-603. 10.1080/00207390410001686698

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Abstract:

A high-gain, beam-tilted, kite-shaped patch antenna design is proposed for millimeter-wave (mmWave) communication and autonomous sensing in unmanned aerial vehicles (UAVs). The proposed antenna is based on a novel asymmetric kite-shaped radiator and a non-uniform via fence to achieve passive beam steering without the complications of phased-array architectures. Full-wave simulations performed in CST Studio Suite show that the impedance bandwidth is −10 dB at the center frequency of 30 GHz in the Ka band (26–38 GHz). The antenna achieves a peak realized gain of 8.58 dBi, with a passive beam tilt of 49° and an 80.2° 3 dB beamwidth, and is designed for drone-to-ground (D2G) wireless communication applications and lateral obstacle detection. The antenna is integrated on a UAV platform for practical testing. The results indicate that a 5 mm air gap is effective in suppressing platform-induced electromagnetic coupling, with minimal beam and pattern distortion. The proposed structure is a compact, lightweight, single-port design that offers a low-complexity solution for next-generation 5G/6G UAV communication systems without requiring a phased array or mechanical beam-steering mechanism.

Keywords:

Kite-shaped antenna,mmWave,beam tilting,UAV collision avoidance,5G/6G,high-gain patch antenna.,

References:

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XII. Chen, S., et al. "Wideband and High Gain Array Antenna for 5G Smart Phone Applications Using Frequency Selective Surface." IEEE Access, vol. 10, 2022, pp. 73157–73165. 10.1109/ACCESS.2022.3196687
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XIV. Costa, F., A. Monorchio, and G. Manara. "Analysis and Design of Ultra-Thin Electromagnetic Absorbers Comprising Resistive FSS." IEEE Transactions on Antennas and Propagation, vol. 58, no. 5, 2010, pp. 1551–1558. 10.1109/TAP.2010.2044356
XV. Dadgarpour, A., et al. "Mutual Coupling Reduction in Dielectric Resonator Antennas Using Metasurface Shield for 60-GHz MIMO Systems." IEEE Antennas and Wireless Propagation Letters, vol. 16, 2017, pp. 477–480. 10.1109/LAWP.2016.2585127
XVI. Ellis, M. S., et al. "Compact Wideband Printed Antenna with Circularly Polarized Tilted Beam Radiation Using a Quasi-Radiator." AEU – International Journal of Electronics and Communications, vol. 170, 2023, p. 154883. 10.1016/j.aeue.2023.154883
XVII. Fadhil, T. Z., et al. "Broadside Gain Enhancement of Wideband Monopole Circular Shaped Antenna Using FSS for Sub-6 GHz Applications." Infocommunications Journal, vol. 16, no. 3, 2024, pp. 82–88. 10.36244/ICJ.2024.3.8
XVIII. Gharaati, A., et al. "A Novel Single-Layer Microstrip Antenna with Tilted Radiation Pattern." Proceedings of the International Symposium on Antennas and Propagation (ISAP), 2021, pp. 479–480. 10.1109/IEEECONF35879.2020.9330222
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XXI. Hur, S., et al. "Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks." IEEE Transactions on Communications, vol. 61, no. 10, 2013, pp. 4391–4403. 10.1109/TCOMM.2013.090513.120848

XXII. Kumar, P., T. Ali, O. P. Kumar, S. Vincent, P. Kumar, Y. Nanjappa, and S. Pathan. "An Ultra-Compact 28 GHz Arc-Shaped Millimeter-Wave Antenna for 5G Application." Micromachines, vol. 14, no. 1, 2023, p. 5. 10.3390/mi14010005
XXIII. Li, M., et al. "A Compact, Single-Layer, Index-Modulated Microstrip Antenna with Stable Customized Tilted Beam over a Wide Bandwidth." IEEE Transactions on Antennas and Propagation, vol. 70, no. 12, 2022, pp. 11428–11441. 10.1109/TAP.2022.3213997
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XXXVIII. Zhang, J., et al. "A Bending-Insensitive Ku-Band SIW Microstrip Antenna with Tilted Beam Capability." Journal of Electromagnetic Waves and Applications, vol. 39, no. 3, 2024, pp. 412–427. 10.1080/09205071.2024.2430618

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Abstract:

In the present manuscript, first of all, we shall introduce a new concept of simulation function in interpolative S̃-metric space. Further, we shall prove a fixed point theorem for the same. An example is also provided to prove the validity of our result. Some results from the literature are also deduced, and an integral equation and boundary value problem are also solved with the help of our main result.

Keywords:

Fixed point,simulation function,interpolative S̃-metric space.,

References:

Argoubi, H., et al. “Non linear contractions involving simulation function in a metric space with a partial order.” Journal of Nonlinear Sci Appl, vol. 8, no.6, 2015, pp. 1082-1094. 10.22436/jnsa.008.06.18.
Banach S. “Sur les op´ erations dans les ensembles abstraits et leur application aux equations int´egrales.” Fundamenta Mathematicae, vol.3, no.1,1922, pp.133–181. 10.4064/fm-3-1-133-181.
Kajal, Kumar M, Abdelnaby O.A, Ramaswamy R. “Fixed Point Theorems In Interpolative G-Metric Spaces: A Novel Approach.” International Journal of Analysis and Applications, vol. 24, 2026, pp. 58.
10.28924/2291-8639-24-2026-58.
Kajal, Kumar M, “Fixed Point Theorems for (ϕ,ψ)-generalized weak contraction in an interpolative metric space.” Boletim da Sociedade Paranaense de Mathematica, 2026(Accepted).
Karapinar E. “On interpolative metric space.” Filomat, vol. 38, no. 22, 2024, pp. 7729-7734. 10.2298/fil2422729k.
Karapinar E., Agarwal R.P. “Some fixed point results on interpolative metric spaces.” Nonlinear Analysis: Real World Applications, vol. 82, 2025, pp. 1-7. 10.1016/j.nonrwa.2024.104244.
Khojasteh, F., et al. A new approach to the study of fixed point ´ a theorems via simulation functions, Filomat, vol. 29, 2015, pp. 1189–1194. 10.2298/FIL1506189K.
Mustafa Z. A new structure for generalized metric spaces – with applications to fixed point theory, Ph.D. Thesis, the University of Newcastle, Australia, (2005).
Mustafa Z., Sims B. “A new approach to generalized metric spaces.” Journal of Nonlinear and Convex Analysis, vol. 7, no. 2, 2006, pp. 289–297.
Ozyurt, S.G., and Alsahli G. “Fixed point results on interpolative metric spaces via simulation functions.” European Journal Of Pure And Applied Mathematics, vol.18, no.2, 2025.10.29020/nybg.ejpam.v18i2.6282

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Abstract:

This work studies the performance of an M-branch Maximum Ratio Combining (M-MRC) diversity receiver system with RFEH (Radio Frequency Energy Harvesting Approach) under Imperfect Channel Estimation, considering the Two-Wave Diffuse Power (TWDP) fading distribution. Two key performance metrics, Average Bit Error Rate (ABER) and Outage Probability (OP), are used to evaluate the effects of Energy Harvesting (EH) and Imperfect Channel Estimation (ICE) on system performance across various modulation schemes. Power Splitting (PS) factor (β) is incorporated to derive the probability density function (PDF) of the received signal within the Simultaneous Wireless Information and Power Transfer (SWIPT) framework. Monte Carlo simulations validate the analytical expressions for ABER and OP derived from the considered PDF, highlighting the impact of EH and ICE on system performance and offering insights for optimizing wireless communication systems in fading environments. These results offer key strategies for building energy-efficient, robust wireless networks by optimizing the diversity receiver to handle harsh fading conditions.

Keywords:

Energy Harvesting,SWIPT,ABER,Outage Probability,TWDP Fading,MRC Receiver,Imperfect Channel Estimation.,

References:

I. Aalo, V.A., 1995. Performance of maximal-ratio diversity systems in a correlated Nakagami-fading environment. IEEE Transactions on Communications, 43(8), pp. 2360–2369. 10.1109/26.403769.
II. Adeli, M.H., Abbasi-Moghadam, D., Fotouhi, H. & Razavizadeh, S.M., 2025. Optimizing Energy Efficiency in UPA-Assisted SWIPT Massive MIMO Systems Over Rician Fading Channels. IEEE Open Journal of the Computer Society. Available at: 10.1109/OJCS.2025.3525519.
III. Aruna, G., 2015. Methodology of analysis of L-branch equal gain combiner with carrier phase error and CCI over Nakagami-m fading. International Conference on Computing and Network Communications (CoCoNet), pp. 505–510. 10.1109/CoCoNet.2015.7411233.
IV. Aruna, G. & Sahu, P.R., 2014. Performance analysis of MRC receiver with channel estimation error and CCI in Nakagami-m fading channel. Computational Intelligence and Computing Research (ICCIC), pp. 1–4. 10.1109/ICCIC.2014.7238299.
V. Badarneh, O. & Almehmadi, F.S., 2016. Performance analysis of L-branch MRC over generalized fading channels with imperfect channel estimation. IET Communications, 10(10), pp. 1175–1182. 10.1049/iet-com.2015.1015.
VI. Deka, N. & Subadar, R., 2023. Energy harvesting techniques and performance analysis for L-MRC receiver in Rayleigh fading channels. CSIT, 11, pp. 157–161. 10.1007/s40012-023-00385-y.
VII. Deka, N. & Subadar, R., 2024. Energy harvesting-based performance analysis in Nakagami-m fading channels. International Journal of Autonomous and Adaptive Communications Systems, 17(1), pp. 89–97. 10.1504/IJAACS.2024.135940.
VIII. Devi, L.M. & Singh, A.D., 2022. Channel capacity analysis of various adaptive transmission schemes and BER performance at Beaulieu-Xie fading. Radioelectronics and Communications Systems, 65, pp. 18–26. 10.3103/S0735272722010034.
IX. Duong, T.Q., Shin, H. & Hong, E.K., 2007. Error probability of binary and M-ary signals with spatial diversity in Nakagami-q (Hoyt) fading channels. EURASIP Journal on Wireless Communication and Networking, 4, pp. 1–8. 10.1155/2007/53742.
X. Durgin, G.D. & Rappaport, T.S., 2002. New analytical models and probability density function for fading in wireless communication. IEEE Transactions on Communication, 50(6), pp. 1005–1015. 10.1109/TCOMM.2002.1010620.
XI. Gradshteyn, I.S. & Ryzhik, I.M., 2007. Tables of Integrals, Series and Products. Elsevier Inc., USA.
XII. Jeong, W., Lee, J. & Yoon, D., 2011. New BER expression of MPSK. IEEE Transactions on Vehicular Technology, 60(4), pp. 1916–1924. 10.1109/TVT.2011.2114903.
XIII. Kim, D., Lee, H. & Kang, J., 2018. Comprehensive analysis of the impact of TWDP fading on the achievable error rate performance of BPSK signaling. IEICE Transactions on Communications, E101-B(2), pp. 500–507. 10.1587/transcom.2017EBP3108.
XIV. Kumar, P. & Sahu, P.R., 2014. Analysis of M-PSK with MRC receiver over κ−μ fading channels with outdated CSI. IEEE Wireless Communications Letters, 3(6), pp. 557–560. 10.1109/LWC.2014.2355849.
XV. Le-Thanh, T. & Ho-Van, K., 2024. MIMO NOMA with nonlinear energy harvesting and imperfect channel information. Arabian Journal for Science and Engineering, 49, pp. 6675–6693. 10.1007/s13369-023-08401-8.
XVI. Ma, Y., Schober, R. & Pasupathy, S., 2005. Effect of channel estimation error on MRC diversity in Rician fading channels. IEEE Transactions on Vehicular Technology, 54(6), pp. 2137–2142. 10.1109/TVT.2005.853454.
XVII. Ma, Y., Schober, R. & Pasupathy, S., 2005. Performance of M-PSK with GSC and EGC with Gaussian weighting errors. IEEE Transactions on Vehicular Technology, 54, pp. 149–162. 10.1109/TVT.2004.839674.
XVIII. Maric, A. & Njemcevic, P., 2024. Performance analysis of M-ary RQAM and M-ary DPSK receivers over the channels with TWDP fading. Wireless Networks, 30(12), pp. 3337–3347. 10.1007/s11276-024-03723-3.
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XXI. Patangia, P.M. & Subadar, R., 2022. L-SC receiver over equally correlated TWDP channels. AEU – International Journal of Electronics and Communications, 149, p. 154173. 10.1016/j.aeue.2022.154173.
XXII. Radaydeh, R.M., 2007. Average error performance of M-ary modulation schemes in Nakagami-q (Hoyt) fading channels. IEEE Communications Letters, 11(3), pp. 255–257. 10.1109/LCOMM.2007.061323.
XXIII. Sahoo, S. & Kumar, N., 2025. Performance analysis of SWIPT-enabled two-way decode-and-forward relay network under kappa-miu fading. Annals of Telecommunications. 10.1007/s12243-025-01079-z.
XXIV. Saikia, B. & Subadar, R., 2018. Effect of estimation error on dual MRC receiver over Hoyt fading channels. International Journal of Electronics Letters. 10.1080/21681724.2018.1523470.

XXV. Saikia, B. & Subadar, R., 2019. Adaptive M-MRC scheme with estimation error over TWDP fading. International Journal of Autonomous and Adaptive Communications Systems, 12(1). 10.1504/IJAACS.2019.096668.
XXVI. Shaikh, F.K. & Zeadally, S., 2016. Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55, pp. 1041–1054. 10.1016/j.rser.2015.11.010.
XXVII. Simon, M.K. & Alouini, M.S., 2005. Digital Communication over Fading Channels, 2nd ed. John Wiley & Sons, Inc. 10.1002/0471715220.
XXVIII. Song, C. et al., 2014. MIMO broadcasting for simultaneous wireless information and power transfer. IEEE Globecom Workshops, pp. 1151–1156. 10.1109/GLOCOMW.2014.7063588.
XXIX. Tang, X., Alouini, M.-S. & Goldsmith, A.J., 1999. Effect of channel estimation error on M-QAM BER performance in Rayleigh fading. IEEE Transactions on Communications, 47, pp. 1856–1864. 10.1109/26.809706.
XXX. Trần, H.Q. & Van Khuong, H., 2024. Performance analysis for RIS-aided systems with energy harvesting. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 11(1), e2. 10.4108/eetinis.v11i1.4369.
XXXI. Varshney, L.R., 2008. Transporting information and energy simultaneously. IEEE ISIT, pp. 1612–1616. 10.1109/ISIT.2008.4595260.
XXXII. Van, P.T. et al., 2016. Performance analysis in wireless power transfer system over Nakagami fading channels. ICEIC, pp. 1–4. 10.1109/ELINFOCOM.2016.75629718.
XXXIII. Yao, M. & Jin, J., 2007. Effect of channel estimation errors on MQAM with MRC and EGC. IEEE Transactions on Vehicular Technology, 56, pp. 1239–1250. 10.1109/TVT.2007.895491.
XXXIV. Yao, M., Schober, R. & Dongbo, Z., 2007. Exact BER for M-QAM with MRC and imperfect channel estimation. IEEE Transactions on Wireless Communications, 6, pp. 926–936. 10.1109/TWC.2007.05243.
XXXV. Zhou, X., Zhang, R. & Ho, C.K., 2013. Wireless information and power transfer: Architecture design and rate-energy tradeoff. IEEE Transactions on Communications, 61(11), pp. 4754–4767. 10.1109/TCOMM.2013.13.120855.
XXXVI. Zhu, P.Z. et al., 2022. Secrecy rate optimization in nonlinear energy harvesting model-based mmWave IoT systems. IEEE Systems Journal, 16(4), pp. 5939–5949. https://doi.org/10.1109/JSYST.2022.3147889.

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Abstract:

The modified Humanoid-Jansen Walking Mechanism provides bio-inspired gait patterns for assistive mobility devices. This study presents a novel bipedal prosthetic-leg system designed to replace wheelchair locomotion by enabling users to traverse multiple terrains with reduced energy demand. Each leg consists of an eight-bar linkage actuated by a motor–gearbox assembly mounted beneath a medical saddle-chair support. Dynamic simulations show that the mechanism generates a natural foot trajectory with a 5.2-unit step length and 2.7-unit step height, closely matching human gait kinematics. Comparative kinematic analysis demonstrates that the hip, knee, and ankle angle patterns follow the temporal and amplitude trends of natural walking. Energy-consumption analysis reveals that the baseline fully motor-driven system requires relatively high electrical input, while the integration of a passive spring system reduces motor load by approximately 20–30%, and using lightweight materials lowers total energy consumption by an additional 15–25%. Implementing an intelligent control algorithm further decreases effective motor duty cycle by ~30% per gait cycle. Simulation results also show that the proposed mechanism maintains stable walking over sandy, uneven, and sloped terrains and improves dynamic weight distribution for the seated rider. These findings indicate that the system offers a feasible, energy-efficient mobility solution for individuals with severe physical disabilities.

Keywords:

Humanoid-Jansen walking mechanism,bipedal prosthetic leg,assistive mobility device,gait rehabilitation,energy-efficient actuation,kinematic analysis,

References:

I. Andrews, Karen L., et al. International Journal of Physical Medicine & Rehabilitation Determining K-Levels Following Transtibial Amputation. Vol. 5, no. 2, 2017, pp. 4–7. 10.4172/2329-9096.1000398.

II. Batten, Heather R., et al. “Gait Speed as an Indicator of Prosthetic Walking Potential Following Lower Limb Amputation.” Prosthetics and Orthotics International, vol. 43, no. 2, 2019, pp. 196–203. 10.1177/0309364618792723.

III. Botros, Michael, et al. “Development of a Powered Four-Bar Prosthetic Hip Joint Prototype.” Prosthesis, 2025, pp. 1–26.

IV. Brauckmann, Vesta, et al. “Report on Prosthetic Fitting, Mobility, and Overall Satisfaction after Major Limb Amputation at a German Maximum Care Provider.” Applied Sciences (Switzerland), vol. 14, no. 16, 2024. 10.3390/app14167274.

V. Collins, Steven H., et al. “Reducing the Energy Cost of Human Walking Using an Unpowered Exoskeleton.” Nature, vol. 522, no. 7555, Nature Publishing Group, 2015, pp. 212–15.

VI. Deshmukh, Nilaj, et al. “Design and Development of Bot Using Theo Jansen Mechanism.” Indian Journal of Engineering and Materials Sciences, vol. 31, no. 6, 2024, pp. 899–908. 10.56042/ijems.v31i6.10211.

VII. Dillon, Michael P., et al. Predict the Medicare Functional Classification Level ( K-Level ) Using the Amputee Mobility Predictor in People with Unilateral Transfemoral and Transtibial Amputation : A Pilot Study. 2018. 10.1177/0309364617706748.

VIII. Farris, Dominic James, and Gregory S. Sawicki. “The Mechanics and Energetics of Human Walking and Running: A Joint Level Perspective.” Journal of the Royal Society Interface, vol. 9, no. 66, 2012, pp. 110–18. 10.1098/rsif.2011.0182.

IX. Gailey, Robert, et al. “Review of Secondary Physical Conditions Associated with Lower-Limb Amputation and Long-Term Prosthesis Use.” Journal of Rehabilitation Research and Development, vol. 45, no. 1, 2008, pp. 15–30. 10.1682/JRRD.2006.11.0147.

X. Haghjoo, Mohammad Reza, et al. “Mech-Walker:A Novel Single-DOF Linkage Device with Movable Frame for Gait Rehabilitation.” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 1, 2021, pp. 13–23. 10.1109/TMECH.2020.2993799.

XI. Haghjoo, Mohammad Reza, and Jungwon Yoon. “Two-Stage Mechanism Path Synthesis Using Optimized Control of a Shadow Robot: Case Study of the Eight-Bar Jansen Mechanism.” Mechanism and Machine Theory, vol. 168, no. September 2021, Elsevier Ltd, 2022, p. 104569. 10.1016/j.mechmachtheory.2021.104569.

XII. Hernández, Alejandra Carolina, et al. “A Home Made Robotic Platform Based on Theo Jansen Mechanism for Teaching Robotics.” INTED2016 Proceedings, vol. 1, no. March, 2016, pp. 6689–98. 10.21125/inted.2016.0579.

XIII. Hua, Bin, et al. “Human-like Artificial Intelligent Wheelchair Robot Navigated by Multi-Sensor Models in Indoor Environments and Error Analysis.” Procedia Computer Science, vol. 105, no. December 2016, The Author(s), 2016, pp. 14–19. 10.1016/j.procs.2017.01.181.

XIV. Imran, Amer, Borhan Beigzadeh, et al. “A New Passive Transfemoral Prosthesis Mechanism Based on 3R36 Knee and ESAR Foot Providing Walking and Squatting.” Theoretical and Applied Mechanics Letters, vol. 13, no. 5, 2023. 10.1016/j.taml.2023.100476.

XV. Imran, Amer, Mohammad Reza Haghjoo, et al. “Design of a Novel Above-Knee Prosthetic Leg with a Passive Energy-Saving Mechanism.” Engineering Solid Mechanics, vol. 11, no. 4, 2023, pp. 339–52. 10.5267/j.esm.2023.5.009.

XVI. J Phaneendra Balaji, et al. “Iot-Based Smart Wheelchair for Elderly Healthcare Monitoring.” International Research Journal on Advanced Engineering Hub (IRJAEH), vol. 3, no. 05, 2025, pp. 2274–82. 10.47392/irjaeh.2025.0335.

XVII. Kim, Sun Wook, et al. “Analysis and Design of a Legged Walking Robot Based on Jansen Mechanism.” SCIS and ISIS 2010 – Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, 2010, pp. 920–24.

XVIII. Kundu, Ananda Sankar, et al. “Design and Performance Evaluation of 4 Wheeled Omni Wheelchair with Reduced Slip and Vibration.” Procedia Computer Science, vol. 105, no. December 2016, The Author(s), 2016, pp. 289–95. 10.1016/j.procs.2017.01.224.

XIX. Kurkure, Shubham R., et al. “Study of Theo Jansen Walking Machine.” International Journal of Innovations in Engineering and Science, vol. 4, no. 10, 2019, pp. 2456–3463, www.ijies.net.

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XXI. Li, Bo, et al. “Mechanism Design of a Novel Device to Facilitate Mobility, Sit-to-Stand Transfer Movement, and Walking Assistance.” Machines, vol. 13, no. 2, 2025. 10.3390/machines13020134.

XXII. Patnaik, Lalit, and Loganathan Umanand. “Kinematics and Dynamics of Jansen Leg Mechanism: A Bond Graph Approach.” Simulation Modelling Practice and Theory, vol. 60, Elsevier B.V., 2016, pp. 160–69. 10.1016/j.simpat.2015.10.003.
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XXIV. Sawicki, Gregory S., and Daniel P. Ferris. “Mechanics and Energetics of Level Walking with Powered Ankle Exoskeletons.” Journal of Experimental Biology, vol. 211, no. 9, 2008, pp. 1402–13. 10.1242/jeb.009241.

XXV. Sengupta, Somak, and Pramod Bhatia. “Study of Applications of Jansen’s Mechanism in Robot.” International Journal of Advance Research and Innovation, vol. 5, no. 3, 2017, pp. 92–98. 10.51976/ijari.531716.

XXVI. Song, Zhibin, et al. “Mechanism Design and Analysis of a Proposed Wheelchair-Exoskeleton Hybrid Robot for Assisting Human Movement.” Mechanical Sciences, vol. 10, no. 1, 2019, pp. 11–24. 10.5194/ms-10-11-2019.

XXVII. Sukerkar, Kedar, et al. “Smart Wheelchair: A Literature Review.” International Journal of Informatics and Communication Technology (IJ-ICT), vol. 7, no. 2, 2018, p. 63. 10.11591/ijict.v7i2.pp63-66.

XXVIII. Thomas, George, and Vladimir V. Vantsevich. “Wheel-Terrain-Obstacle Interaction in Vehicle Mobility Analysis.” Vehicle System Dynamics, vol. 48, no. SUPPL. 1, 2010, pp. 139–56. 10.1080/00423111003690496.

XXIX. Utaminingrum, Fitri, et al. “Indoor Staircase Detection for Supporting Security Systems in Autonomous Smart Wheelchairs Based on Deep Analysis of the Co-Occurrence Matrix and Binary Classification.” Intelligent Systems with Applications, vol. 23, no. June, Elsevier Ltd, 2024, p. 200405. 10.1016/j.iswa.2024.200405.

XXX. Wada, Masayoshi. “Development of a 4WD Omnidirectional Wheelchair.” Proceedings of the SICE Annual Conference, no. September, 2008, pp. 1767–71. 10.1109/SICE.2008.4654950.

XXXI. Yash Punde. “Design and Linkage Analysis of Theo Jansen Mechanism.” International Journal of Engineering Research And, vol. V9, no. 09, 2020, pp. 259–63. 10.17577/ijertv9is090170.

XXXII. Zubairuddin, M., et al. “Eight-Legged Robot Using Theo Jansen Mechanism.” International Journal for Advanced Research in Science and Technology, vol. 12, no. 12, 2022, pp. 332–61.

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Abstract:

Brain tumor detection from multi-institutional MRI datasets faces two compounding challenges: segmenting heterogeneous glioma sub-regions, and privacy regulations (HIPAA, GDPR) that prevent data centralization. This paper presents AdaFed-BrainGNN, an adaptive federated learning framework extending BrainGNN-Hybrid with three innovations: (1) AdaFedAvg — adaptive client-weighting aggregation via composite quality scores; (2) formal (ε, δ)-differential privacy via DP-SGD with Rényi DP (RDP) accounting; and (3) structured gradient sparsification that reduces communication by 73.4%. Evaluated on BraTS 2021 (1,251 cases), BraTS 2023 (450 cases), and a six-hospital dataset (N = 2,847), AdaFed-BrainGNN achieves Accuracy = 99.14%, F1 = 98.84%, AUC = 0.997, and ε = 2.31 (δ = 10⁻⁵) after 100 federation rounds, with AWS SageMaker inference at 38 ms per volume

Keywords:

Brain tumor detection,Federated learning,Differential privacy,Graph attention network,EfficientNet,Non-IID heterogeneity,BraTS,Adaptive aggregation,

References:

I. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., Zhang, L. : ‘Deep Learning with Differential Privacy’. Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS). pp. 308–318, 2016. 10.1145/2976749.2978318
II. Baid, U., Ghodasara, S., Mohan, S., et al. : ‘The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification’. arXiv preprint. 2021. https://arxiv.org/abs/2107.02314
III. Brody, S., Alon, U., Yahav, E. : ‘How Attentive are Graph Attention Networks?’. International Conference on Learning Representations (ICLR). 2022. https://arxiv.org/abs/2105.14491
IV. Chen, Y., Zhang, L., Wang, X., et al. : ‘MedGNN: Heterogeneous Graph Neural Network for Medical Image Analysis’. IEEE Transactions on Medical Imaging (TMI). Vol. 42, 2023. 10.1109/TMI.2023.3245678
V. Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., Ronneberger, O. : ‘3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation’. MICCAI. pp. 424–432, 2016. 10.1007/978-3-319-46723-8_49
VI. Deng, Y., Kamani, M. M., Mahdavi, M. : ‘Adaptive Personalized Federated Learning’. arXiv preprint. 2020. https://arxiv.org/abs/2003.13461
VII. Geiping, J., Bauermeister, H., Dröge, H., Moeller, M. : ‘Inverting Gradients — How Easy is it to Break Privacy in Federated Learning?’. Advances in Neural Information Processing Systems (NeurIPS). pp. 16937–16947, 2020. https://arxiv.org/abs/2003.14053
VIII. Geyer, R. C., Klein, T., Nabi, M. : ‘Differentially Private Federated Learning: A Client Level Perspective’. NeurIPS Workshop on Machine Learning on the Phone. 2017. https://arxiv.org/abs/1712.07557
IX. Gu, Y., Sun, X., Wang, Y., et al. : ‘Privacy-Preserving Federated Learning for Retinal Disease Diagnosis’. IEEE Transactions on Medical Imaging (TMI). 2024. 10.1109/TMI.2024.3367890
X. Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., Xu, D. : ‘Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images’. CVPR Workshops (BrainLes). pp. 272–282, 2022. https://arxiv.org/abs/2201.01266
XI. Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., Maier-Hein, K. H. : ‘nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation’. Nature Methods. Vol. 18, pp. 203–211, 2021. 10.1038/s41592-020-01008-z
XII. Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., Suresh, A. T. : ‘SCAFFOLD: Stochastic Controlled Averaging for Federated Learning’. International Conference on Machine Learning (ICML). pp. 5132–5143, 2020. https://arxiv.org/abs/1910.06378
XIII. Kazerooni, A. F., Khalili, N., Liu, X., et al. : ‘The Brain Tumor Segmentation (BraTS) Challenge 2023’. arXiv preprint. 2023. https://arxiv.org/abs/2305.17033
XIV. Ktena, S. I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., Rueckert, D. : ‘Metric Learning with Spectral Graph Convolutions on Brain Connectivity Networks’. NeuroImage. Vol. 169, pp. 431–442, 2018. 10.1016/j.neuroimage.2017.12.052
XV. Kumar, A., Lamba, M., Shekhawat, J. : ‘A Novel GNN-Based Hybrid Model for Efficient Brain Tumor Detection on Cloud Platform’. Computers in Biology and Medicine. 2024. 10.1016/j.compbiomed.2024.108001
XVI. Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V. : ‘Federated Optimization in Heterogeneous Networks (FedProx)’. Proceedings of Machine Learning and Systems (MLSys). 2020. https://arxiv.org/abs/1812.06127
XVII. Li, X., Zhao, H., Ren, T., et al. : ‘CNN-GAT: A Hybrid Architecture for Brain Tumor Classification’. Expert Systems with Applications. Vol. 213, 2023. 10.1016/j.eswa.2022.118995
XVIII. Liu, X., Chen, J., Wang, Q., et al. : ‘Federated Learning for Chest X-Ray Classification with Differential Privacy’. MICCAI. 2023. 10.1007/978-3-031-43895-0_45
XIX. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., Agüera y Arcas, B. : ‘Communication-Efficient Learning of Deep Networks from Decentralized Data’. Artificial Intelligence and Statistics (AISTATS). pp. 1273–1282, 2017. https://arxiv.org/abs/1602.05629
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XXI. Pati, S., Baid, U., Edwards, B., et al. : ‘Federated Learning Enables Big Data for Rare Cancer Boundary Detection’. Nature Communications. Vol. 13, 7346, 2022. 10.1038/s41467-022-33407-5
XXII. Ronneberger, O., Fischer, P., Brox, T. : ‘U-Net: Convolutional Networks for Biomedical Image Segmentation’. MICCAI. pp. 234–241, 2015. 10.1007/978-3-319-24574-4_28
XXIII. Shi, Y., Zu, C., Yang, P., et al. : ‘Graph Neural Network for Lymph Node Detection in Medical Images’. MICCAI. 2020. 10.1007/978-3-030-59719-1_38
XXIV. Stupp, R., Mason, W. P., van den Bent, M. J., et al. : ‘Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma’. New England Journal of Medicine. Vol. 352, No. 10, pp. 987–996, 2005. 10.1056/NEJMoa043330
XXV. Tan, M., Le, Q. V. : ‘EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks’. International Conference on Machine Learning (ICML). pp. 6105–6114, 2019. https://arxiv.org/abs/1905.11946
XXVI. Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., Zhou, Y. : ‘A Hybrid Approach to Privacy-Preserving Federated Learning’. ACM Workshop on Artificial Intelligence and Security (AISec). pp. 1–11, 2019. 10.1145/3338501.3357370
XXVII. Wang, L., Lin, Z. Q., Wong, A. : ‘COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images’. Scientific Reports. Vol. 10, 19549, 2020. 10.1038/s41598-020-76550-z
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XXX. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V. : ‘Federated Learning with Non-IID Data’. arXiv preprint. 2018. https://arxiv.org/abs/1806.00582

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Abstract:

The presence of volatile organic compounds (VOCs) is generally understood through their effects on the environment and human health. This paper presents a ZnO nano-foam-like thin film sensor to detect acetone and formaldehyde at 190- 2020 ppm in controlled laboratory settings. A quasi-steady time-resolved machine learning methodology is used to overcome the poor selectivity of ZnO-based sensors. A short portion of the sensor response is used to extract features instead of just the steady-state values, giving a more accurate representation of the dynamic sensing behavior. Various machine learning algorithms are evaluated for VOC classification. Among these, Artificial Neural Networks (ANN) have the best accuracy of 98%, whereas Gradient Boosting performs more balanced in terms of precision and recall. The findings indicate that time-resolved features could be added to enhance the classification performance without the necessity to use sophisticated sensor arrays. This paper demonstrates the integration of nanostructured sensing materials with machine learning for VOC classification using experimentally acquired sensor data. The experimentally acquired sensor data was used to validate the proposed framework under controlled laboratory conditions, demonstrating its feasibility for VOC classification

Keywords:

ZnO,Formaldehyde,Acetone,Supervised Machine Learning,Gradient Boost,ANN.,

References:

I. Acharyya, S., K. Mondal, and S. Basu. “Single Resistive Sensor for Selective Detection of Multiple VOCs Employing SnO₂ Hollow Spheres and Machine Learning Algorithm: A Proof of Concept.” Sensors and Actuators B: Chemical, vol. 321, 2020, p. 128484. 10.1016/j.snb.2020.128484.
II. Banerjee, N., B. Bhowmik, S. Roy, C. K. Sarkar, and P. Bhattacharyya. “Anomalous Recovery Characteristics of Pd Modified ZnO Nanorod Based Acetone Sensor.” Journal of Nanoscience and Nanotechnology, vol. 13, no. 10, 2013, pp. 6826–6834. 10.1166/jnn.2013.7786.
III. Banerjee, N., S. Roy, C. K. Sarkar, and P. Bhattacharyya. “High Dynamic Range Methanol Sensor Based on Aligned ZnO Nanorods.” IEEE Sensors Journal, vol. 13, no. 5, 2013, pp. 1669–1676. 10.1109/JSEN.2013.2237822.
IV. Binson, V. A., N. A. A. Rahim, and R. Shahriman. “Non-Invasive Diagnosis of COPD with E-Nose Using XGBoost Algorithm.” Proceedings of the 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems. IEEE, 2021, pp. 297–301. 10.1109/ACCESS51619.2021.9563303.
V. Dalis, C. “Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases.” Microorganisms, vol. 11, no. 7, 2023, p. 1822. https://www.mdpi.com/2076-2607/11/7/1822.
VI. Fan, X., et al. “Exhaled VOC Detection in Lung Cancer Screening: A Comprehensive Meta-Analysis.” BMC Cancer, vol. 24, no. 1, 2024, p. 775. 10.1186/s12885-024-12537-7.
VII. Ghosh, R., and A. Talukdar. “Lung Disease Classification from Chest X-Ray Images Using Ensemble Learning.” 2024 IEEE Calcutta Conference (CALCON), Kolkata, India, 2024, pp. 1–5. 10.1109/CALCON63337.2024.10914228.
VIII. Hayasaka, T., et al. “An Electronic Nose Using a Single Graphene FET and Machine Learning for Water, Methanol, and Ethanol.” Microsystems & Nanoengineering, vol. 6, no. 1, 2020, pp. 1–9. 10.1038/s41378-020-0161-3.
IX. Itoh, T., Y. Koyama, W. Shin, T. Akamatsu, A. Tsuruta, Y. Masuda, and K. Uchiyama. “Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases.” Sensors, vol. 20, no. 9, 2020, p. 2687. 10.3390/s20092687.
X. Jaeschke, C., M. Klein, R. Götz, C. Schütze, and S. Zimmermann. “An Innovative Modular eNose System Based on a Unique Combination of Analog and Digital Metal Oxide Sensors.” ACS Sensors, vol. 4, 2019, pp. 2277–2281. 10.1021/acssensors.9b01244.
XI. Mei, H., J. Peng, T. Wang, et al. “Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.” Nano-Micro Letters, vol. 16, 2024, p. 269. 10.1007/s40820-024-01489-z.
XII. Singh, S., S. S., P. Gajje, C. Adak, R. P. Shukla, and V. Kamble. “Metal Oxide-Based Gas Sensor Array for the VOCs Analysis in Complex Mixtures Using Machine Learning.” Sensors, vol. 23, no. 8, 2023, p. 1703. 10.3390/s23081703.
XIII. Venkatesh, C., K. Ramana, S. Y. Lakkisetty, S. S. Band, S. Agarwal, and A. Mosavi. “A Neural Network and Optimization Based Lung Cancer Detection System in CT Images.” Frontiers in Public Health, vol. 10, 2022, article 769692. 10.3389/fpubh.2022.769692.
XIV. Wang, H., S. Sun, J. Wang, and C. Zhao. “EEMD and GUCNN-XGBoost Joint Recognition Algorithm for Detection of Precursor Chemicals Based on Semiconductor Gas Sensor.” IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022, pp. 1–12. 10.1109/TIM.2022.3197762.
XV. Wilson, A. D. “Developments of Recent Applications for Early Diagnosis of Diseases Using Electronic-Nose and Other VOC-Detection Devices.” Sensors, vol. 23, no. 18, 2023, p. 7885. https://www.mdpi.com/1424-8220/23/18/7885.
XVI. Wilson, A. D., and L. B. Forse. “Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers.” Sensors, vol. 23, no. 6, 2023, p. 2887. 10.3390/s23062887.
XVII. Zhang, T., Y. Cao, M. Chen, and L. Xie. “Recent Advances in CNTs-Based Sensors for Detecting the Quality and Safety of Food and Agro-Products.” Journal of Food Measurement and Characterization, vol. 17, no. 5, 2023, pp. 3061–3075. 10.1007/s11694-023-01850-7.

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Abstract:

An Electroencephalogram (EEG) is frequently corrupted by both external noise and physiological artifacts arising from bodily activities such as cardiac, ocular, and muscular processes, which significantly degrade signal quality. Conventional filtering techniques suppress external noise; however, removing physiological artifacts is challenging because their spectral characteristics overlap with those of EEG signals. In this work, a novel hybrid adaptive framework is proposed to effectively remove cardiac and ocular artifacts from EEG signals. Initially, the instantaneous frequency (IF) of EEG signals is extracted using the Hilbert transform after decomposing the signal through Variational Mode Decomposition (VMD). Adaptive selection of decomposition modes using spectral flatness and kurtosis is performed. Subsequently, a state-space Recursive Least Squares (SSRLS) algorithm with a dynamic forgetting factor is employed to estimate and suppress artifact components. Sparse regularization is applied in recursive estimation for artifact isolation. A hybrid time–frequency consistency constraint is chosen for signal preservation. It provides an adaptive Intrinsic Mode Function (IMF) selection mechanism based on correlation and frequency characteristics to selectively process contaminated components only. As a result, useful neurological information is preserved. The performance measures evaluated include mean square error (MSE), relative error (RE), normalized mean square error (NMSE), signal-to-noise ratio (SNR), gain in signal-to-artifact ratio (GSAR), and correlation coefficient (CC), with corresponding values of 0.06 µV², 0.03, 0.18 µV², 76.28 dB, 12.49, and 99.38%, respectively. Experimental validation confirms improved robustness and computational efficiency compared to conventional VMD-RLS and transform-based approaches.

Keywords:

EEG Artifact Removal,Adaptive VMD,Sparse Recursive Filtering,State-Space RLS,Non-Stationary Signal Processing,Entropy-Based Filtering.,

References:

I. Aquilué-Llorens, David, and Aureli Soria-Frisch. "Eeg artifact detection and correction with deep autoencoders." arXiv preprint arXiv:2502.08686 (2025). 10.48550/arXiv.2502.08686.
II. Arpaia, Pasquale, et al. "A Systematic Review of Techniques for Artifact Detection and Artifact Category Identification in Electroencephalography from Wearable Devices." Sensors, vol. 25, no. 18, 2025, p. 5770. 10.3390/s25185770
III. Beach, Christopher, Mingjie Li, Ertan Balaban, and Alexander J. Casson. "Motion Artifact Removal in Electroencephalography and Electrocardiography by Using Multichannel Inertial Measurement Units and Adaptive Filtering." Healthcare Technology Letters, vol. 8, no. 5, 2021, pp. 128–138. 10.1049/htl2.12016
IV. Behera, Sandhyalati, and Mihir Narayan Mohanty. "A Statistical Approach for Ocular Artifact Removal in Brain Signals." 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), IEEE, 2018, pp. 500–503. 10.1109/ICDSBA.2018.00099.
V. Behera, Sandhyalati, and Mihir Narayan Mohanty. "Artifact Removal Using Deep WVFLN for Brain Signal Diagnosis through IoMT." Measurement: Sensors, vol. 24, 2022, article 100465. 10.1016/j.measen.2022.100465.
VI. Behera, Sandhyalati, and Mihir Narayan Mohanty. "Removal of artifact from the brain signal using Discrete Cosine Transform." International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy. Singapore: Springer Nature Singapore, 2020. 10.1007/978-981-15-4992-2_23.
VII. Goldberger, Ary L., et al. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals." Circulation, vol. 101, no. 23, 2000, pp. E215–E220. 10.1161/01.CIR.101.23.e215.
VIII. Judith, A. Mary, S. Baghavathi Priya, and Rakesh Kumar Mahendran. "Artifact Removal from EEG Signals Using Regenerative Multi-Dimensional Singular Value Decomposition and Independent Component Analysis." Biomedical Signal Processing and Control, vol. 74, 2022, article 103452. 10.1016/j.bspc.2021.103452.
IX. Klados, Manousos A., and Panagiotis D. Bamidis. "A Semi-Simulated EEG/EOG Dataset for the Comparison of EOG Artifact Rejection Techniques." Data in Brief, vol. 8, 2016, pp. 1004–1006. 10.1016/j.dib.2016.07.068.
X. Lee, Young-Eun, No-Sang Kwak, and Seong-Whan Lee. "A real-time movement artifact removal method for ambulatory brain-computer interfaces." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28.12 (2020): 2660-2670. 10.1109/TNSRE.2020.3032431.
XI. Liu, Chang, and Cheng Zhang. "Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI." Sensors, vol. 22, no. 17, 2022, article 6698. 10.3390/s22176698
XII. Maddirala, Ajay Kumar, and Kalyana C. Veluvolu. "SSA with CWT and k-means for eye-blink artifact removal from single-channel EEG signals." Sensors 22.3 (2022): 931. 10.3390/s22030931.
XIII. Mannan, Malik Muhammad Naeem, Muhammad Ahmad Kamran, and Myung Yung Jeong. "Identification and removal of physiological artifacts from electroencephalogram signals: A review." IEEE Access 6 (2018): 30630-30652. 10.1109/ACCESS.2018.2842082

XIV. Massar, Hicham, Tarik Ben Drissi, Badr Nsiri, and Mohamed Miyara. "Advancements in Blind Source Separation for EEG Artifact Removal: A Comparative Analysis of Variational Mode Decomposition and Discrete Wavelet Transform Approaches." Applied Acoustics, vol. 228, 2025, article 110300. 10.1016/j.apacoust.2024.110300.
XV. Mumtaz, Wajid, Suleman Rasheed, and Alina Irfan. "Review of challenges associated with the EEG artifact removal methods." Biomedical Signal Processing and Control 68 (2021): 102741. 10.1016/j.bspc.2021.102741
XVI. Panda, Sreelekha, et al. "Epileptic seizure detection using deep ensemble network with empirical wavelet transform." Measurement Science Review 21.4 (2021): 110-116. 10.2478/msr-2021-0016.
XVII. Pei, Yan, et al. "DTP-Net: Learning to reconstruct EEG signals in time-frequency domain by multi-scale feature reuse." IEEE Journal of Biomedical and Health Informatics 28.5 (2024): 2662-2673. 10.1109/JBHI.2024.3358917.
XVIII. Raj, Vandana Akshath, Tejasvi Parupudi, Vishnumurthy Kedlaya K, Ananthakrishna Thalengala, and Subramanya G. Nayak. "Physiological Artifact Suppression in EEG Signals Using an Efficient Multi-Scale Depth-Wise Separable Convolution and Variational Attention Deep Learning Model for Improved Neurological Health Signal Quality." Technologies, vol. 13, no. 12, 2025, article 578. 10.3390/technologies13120578.
XIX. Ranjan, Rakesh, Bikash Chandra Sahana, and Ashish Kumar Bhandari. "Motion artifacts suppression from EEG signals using an adaptive signal denoising method." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1-10. 10.1109/TIM.2022.3163062.
XX. Sayed, Ali H., and Thomas Kailath. "A State-Space Approach to Adaptive RLS Filtering." IEEE Signal Processing Magazine, vol. 11, no. 3, 1994, pp. 18–60. 10.1109/79.295229
XXI. Xing, Le, and Alexander J. Casson. "Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite with Hardware/Software Acceleration." IEEE Transactions on Biomedical Engineering, vol. 71, no. 11, 2024, pp. 3111–3122. 10.1109/TBME.2024.3408331.

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Abstract:

Multiplier efficiency is very significant in image processing and multimedia applications, which involve numerous floating-point multiplications. Although IEEE-754 multipliers are more precise, they also require more power, hardware, and time, which are not suitable for low-power and real-time systems. Simple control logic, partial-product encoding, and error correction are methods used in approximate floating-point multipliers to enhance efficiency. They consume less hardware and power, and still provide acceptable image quality compared to their more exact counterparts, the multipliers. Therefore, they can be used in image processing applications that require error tolerance.

Keywords:

Iterative approximate floating-point multiplier (IAFPM),IEEE-754 floating-point arithmetic,image multiplication,partial product encoding,error-tolerant image processing,low-power VLSI design,multimedia image processing applications,

References:

I. Towhidy, A., R. Omidi, and K. Mohammadi. "On the Design of Iterative Approximate Floating-Point Multipliers." IEEE Transactions on Computers, vol. 72, no. 6, June 2023, pp. 1623–1635. 10.1109/TC.2022.3216465
II. Edavoor, P. J., A. K. Samantaray, and A. D. Rahulkar. "Design of Floating Point Multiplier Using Approximate Hybrid Radix-4/Radix-8 Booth Encoder for Image Analysis." e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 2024, article 100546, 2024. 10.1016/j.prime.2024.100546
III. Tadigadapa, P., and P. Jagadamba. "Design of Power Efficient Approximate Multiplier." Procedia Computer Science, vol. 260, 2025, pp. 784–795. 10.1016/j.procs.2025.03.259
IV. Mondal, S., M. R., and R. S. "Approximate 8-Bit Multipliers and Their Physical Design Implementation." e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 6, no. 3, 2023, article 100300. 10.1016/j.prime.2023.100300
V. Chowdam, V. S., S. B. Potladurty, and P. R. Karipireddy. "Design and Evaluation of Clock-Gating-Based Approximate Multiplier for Error-Tolerant Applications." Memories – Materials, Devices, Circuits and Systems, vol. 9, no. 2, 2025. 10.1016/j.memori.2025.100123
VI. Chakraborty, A., V. P. A. Kumar, A. K. Vruddhula, J. N. K., and S. Balamurugan. "Energy Efficient Approximate Multiplier for Image Processing Applications." Results in Engineering, vol. 25, 2024, article 103798. 10.1016/j.rineng.2024.103798
VII. Nesam, J. J. J., S. S. Ganesh, and S. Ramachandran. "Effect of Bit-Size Reduced Half-Precision Floating-Point Format on Image Pixel Characterization for AI Applications." Results in Engineering, vol. 2024, 2024, article 103179. 10.1016/j.rineng.2024.103179
VIII. Rott, O., and E. Jarlebring. "An Iterative Method for the Multipliers of Periodic Delay-Differential Equations and the Analysis of a PDE Milling Model." WIAS Preprint, no. 1470, 2009. 10.20347/WIAS.PREPRINT.1470.
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X. Brogi, F., S. Bna, G. Boga, G. Amati, T. E. Ongaro, and M. Cerminara. "On Floating Point Precision in Computational Fluid Dynamics Using OpenFOAM." Future Generation Computer Systems, vol. 152, 2024, pp. 1–16. 10.1016/j.future.2023.10.006
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