TY  - JOUR
AU  - Jakšić, Zoran
AU  - Devi, Swagata
AU  - Jakšić, Olga
AU  - Guha, Koushik
PY  - 2023
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/7202
AB  - The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
PB  - MDPI
T2  - Biomimetics
T1  - A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics
VL  - 8
IS  - 3
SP  - 278
DO  - 10.3390/biomimetics8030278
ER  - 

@article{
author = "Jakšić, Zoran and Devi, Swagata and Jakšić, Olga and Guha, Koushik",
year = "2023",
abstract = "The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.",
publisher = "MDPI",
journal = "Biomimetics",
title = "A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics",
volume = "8",
number = "3",
pages = "278",
doi = "10.3390/biomimetics8030278"
}

Jakšić, Z., Devi, S., Jakšić, O.,& Guha, K.. (2023). A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics. in Biomimetics
MDPI., 8(3), 278.
https://doi.org/10.3390/biomimetics8030278

Jakšić Z, Devi S, Jakšić O, Guha K. A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics. in Biomimetics. 2023;8(3):278.
doi:10.3390/biomimetics8030278 .

Jakšić, Zoran, Devi, Swagata, Jakšić, Olga, Guha, Koushik, "A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics" in Biomimetics, 8, no. 3 (2023):278,
https://doi.org/10.3390/biomimetics8030278 . .

TY  - JOUR
AU  - Choudhury, Sagarika
AU  - Baishnab, Krishna Lal
AU  - Guha, Koushik
AU  - Jakšić, Zoran
AU  - Jakšić, Olga
AU  - Iannacci, Jacopo
PY  - 2023
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/7205
AB  - This study discusses the use of a triple material gate (TMG) junctionless tunnel field-effect transistor (JLTFET) as a biosensor to identify different protein molecules. Among the plethora of existing types of biosensors, FET/TFET-based devices are fully compatible with conventional integrated circuits. JLTFETs are preferred over TFETs and JLFETs because of their ease of fabrication and superior biosensing performance. Biomolecules are trapped by cavities etched across the gates. An analytical mathematical model of a TMG asymmetrical hetero-dielectric JLTFET biosensor is derived here for the first time. The TCAD simulator is used to examine the performance of a dielectrically modulated label-free biosensor. The voltage and current sensitivity of the device and the effects of the cavity size, bioanalyte electric charge, fill factor, and location on the performance of the biosensor are also investigated. The relative current sensitivity of the biosensor is found to be about 1013. Besides showing an enhanced sensitivity compared with other FET- and TFET-based biosensors, the device proves itself convenient for low-power applications, thus opening up numerous directions for future research and applications.
PB  - MDPI
T2  - Chemosensors
T1  - Modeling and Simulation of a TFET-Based Label-Free Biosensor with Enhanced Sensitivity
VL  - 11
IS  - 5
SP  - 312
DO  - 10.3390/chemosensors11050312
ER  - 

@article{
author = "Choudhury, Sagarika and Baishnab, Krishna Lal and Guha, Koushik and Jakšić, Zoran and Jakšić, Olga and Iannacci, Jacopo",
year = "2023",
abstract = "This study discusses the use of a triple material gate (TMG) junctionless tunnel field-effect transistor (JLTFET) as a biosensor to identify different protein molecules. Among the plethora of existing types of biosensors, FET/TFET-based devices are fully compatible with conventional integrated circuits. JLTFETs are preferred over TFETs and JLFETs because of their ease of fabrication and superior biosensing performance. Biomolecules are trapped by cavities etched across the gates. An analytical mathematical model of a TMG asymmetrical hetero-dielectric JLTFET biosensor is derived here for the first time. The TCAD simulator is used to examine the performance of a dielectrically modulated label-free biosensor. The voltage and current sensitivity of the device and the effects of the cavity size, bioanalyte electric charge, fill factor, and location on the performance of the biosensor are also investigated. The relative current sensitivity of the biosensor is found to be about 1013. Besides showing an enhanced sensitivity compared with other FET- and TFET-based biosensors, the device proves itself convenient for low-power applications, thus opening up numerous directions for future research and applications.",
publisher = "MDPI",
journal = "Chemosensors",
title = "Modeling and Simulation of a TFET-Based Label-Free Biosensor with Enhanced Sensitivity",
volume = "11",
number = "5",
pages = "312",
doi = "10.3390/chemosensors11050312"
}

Choudhury, S., Baishnab, K. L., Guha, K., Jakšić, Z., Jakšić, O.,& Iannacci, J.. (2023). Modeling and Simulation of a TFET-Based Label-Free Biosensor with Enhanced Sensitivity. in Chemosensors
MDPI., 11(5), 312.
https://doi.org/10.3390/chemosensors11050312

Choudhury S, Baishnab KL, Guha K, Jakšić Z, Jakšić O, Iannacci J. Modeling and Simulation of a TFET-Based Label-Free Biosensor with Enhanced Sensitivity. in Chemosensors. 2023;11(5):312.
doi:10.3390/chemosensors11050312 .

Choudhury, Sagarika, Baishnab, Krishna Lal, Guha, Koushik, Jakšić, Zoran, Jakšić, Olga, Iannacci, Jacopo, "Modeling and Simulation of a TFET-Based Label-Free Biosensor with Enhanced Sensitivity" in Chemosensors, 11, no. 5 (2023):312,
https://doi.org/10.3390/chemosensors11050312 . .

TY  - JOUR
AU  - Devi, Swagata
AU  - Guha, Koushik
AU  - Jakšić, Olga
AU  - Baishnab, Krishna Lal
AU  - Jakšić, Zoran
PY  - 2022
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/5373
AB  - This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.
PB  - Switzerland : Multidisciplinary Digital Publishing Institute (MDPI)
T2  - Micromachines
T1  - Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method
VL  - 13
VL  - 7
SP  - 1104
DO  - 10.3390/mi13071104
ER  - 

@article{
author = "Devi, Swagata and Guha, Koushik and Jakšić, Olga and Baishnab, Krishna Lal and Jakšić, Zoran",
year = "2022",
abstract = "This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.",
publisher = "Switzerland : Multidisciplinary Digital Publishing Institute (MDPI)",
journal = "Micromachines",
title = "Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method",
volume = "13, 7",
pages = "1104",
doi = "10.3390/mi13071104"
}

Devi, S., Guha, K., Jakšić, O., Baishnab, K. L.,& Jakšić, Z.. (2022). Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method. in Micromachines
Switzerland : Multidisciplinary Digital Publishing Institute (MDPI)., 13, 1104.
https://doi.org/10.3390/mi13071104

Devi S, Guha K, Jakšić O, Baishnab KL, Jakšić Z. Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method. in Micromachines. 2022;13:1104.
doi:10.3390/mi13071104 .

Devi, Swagata, Guha, Koushik, Jakšić, Olga, Baishnab, Krishna Lal, Jakšić, Zoran, "Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method" in Micromachines, 13 (2022):1104,
https://doi.org/10.3390/mi13071104 . .

TY  - JOUR
AU  - Pertin, Osor
AU  - Guha, Koushik
AU  - Jakšić, Olga
AU  - Jakšić, Zoran
AU  - Iannacci, Jacopo
PY  - 2022
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/5604
AB  - This paper proposes a monostable nonlinear Piezoelectric Energy Harvester (PEH). The harvester is based on an unconventional exsect-tapered fixed-guided spring design, which introduces nonlinearity into the system due to the bending and stretching of the spring. The physical–mathematical model and finite element simulations were performed to analyze the effects of the stretching-induced nonlinearity on the performance of the energy harvester. The proposed exsect-tapered nonlinear PEH shows a bandwidth and power enhancement of 15.38 and 44.4%, respectively, compared to conventional rectangular nonlinear PEHs. It shows a bandwidth and power enhancement of 11.11 and 26.83%, respectively, compared to a simple, linearly tapered and nonlinear PEH. The exsect-tapered nonlinear PEH improves the power output and operational bandwidth for harvesting low-frequency ambient vibrations.
PB  - MDPI AG
T2  - Micromachines
T1  - Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications
VL  - 13
IS  - 9
SP  - 1399
DO  - 10.3390/mi13091399
ER  - 

@article{
author = "Pertin, Osor and Guha, Koushik and Jakšić, Olga and Jakšić, Zoran and Iannacci, Jacopo",
year = "2022",
abstract = "This paper proposes a monostable nonlinear Piezoelectric Energy Harvester (PEH). The harvester is based on an unconventional exsect-tapered fixed-guided spring design, which introduces nonlinearity into the system due to the bending and stretching of the spring. The physical–mathematical model and finite element simulations were performed to analyze the effects of the stretching-induced nonlinearity on the performance of the energy harvester. The proposed exsect-tapered nonlinear PEH shows a bandwidth and power enhancement of 15.38 and 44.4%, respectively, compared to conventional rectangular nonlinear PEHs. It shows a bandwidth and power enhancement of 11.11 and 26.83%, respectively, compared to a simple, linearly tapered and nonlinear PEH. The exsect-tapered nonlinear PEH improves the power output and operational bandwidth for harvesting low-frequency ambient vibrations.",
publisher = "MDPI AG",
journal = "Micromachines",
title = "Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications",
volume = "13",
number = "9",
pages = "1399",
doi = "10.3390/mi13091399"
}

Pertin, O., Guha, K., Jakšić, O., Jakšić, Z.,& Iannacci, J.. (2022). Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications. in Micromachines
MDPI AG., 13(9), 1399.
https://doi.org/10.3390/mi13091399

Pertin O, Guha K, Jakšić O, Jakšić Z, Iannacci J. Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications. in Micromachines. 2022;13(9):1399.
doi:10.3390/mi13091399 .

Pertin, Osor, Guha, Koushik, Jakšić, Olga, Jakšić, Zoran, Iannacci, Jacopo, "Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications" in Micromachines, 13, no. 9 (2022):1399,
https://doi.org/10.3390/mi13091399 . .

TY  - JOUR
AU  - Jakšić, Olga
AU  - Jakšić, Zoran
AU  - Guha, Koushik
AU  - Silva, Ana G.
AU  - Laskar, Naushad Manzoor
PY  - 2022
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/5605
AB  - A new set of software tools for the prediction of the higher heating values (HHV) of arbitrarily chosen biomass species is presented. A comparative qualitative and quantitative analysis of 12 algorithms for training artificial neural networks (ANN) which predict the HHV of biomass using the proximate analysis is given. Fixed carbon, volatile matter and ash percentage were utilized as inputs. Each ANN had the same structure but a different training algorithm (BFGS Quasi Newton, Bayesian Regularization, Conjugate Gradient—Powell/Beale Restarts, Fletcher–Powell Conjugate Gradient, Polak–Ribiére Conjugate Gradient, Gradient Descent, Gradient Descent Momentum, Variable Learning Rate Gradient Descent, Levenberg–Marquardt, One Step Secant, Resilient Backpropagation, Scaled Conjugate Gradient). To ensure an extended applicability of our results to a wide range of different biomass species, the data conditioning was based on diverse experimental data gathered from the literature, 447 samples overall. Out of these, 301 datasets were used for the training, validation and testing by MathWorks MATLAB Neural Network Fitting Application and by custom designed codes, and 146 remaining datasets were used for the independent evaluation of all training algorithms. The HHV predictions of the ANN-based fitting functions were thoroughly tested and intercompared, to which purpose we developed a test suite which applies mean squared error, coefficient of the determination, mean Poisson deviance, mean Gamma deviance and Friedman test. The comparative analysis showed that several algorithms resulted in ANN-based fitting functions whose outputs correlated well with measured values of the HHV. All programming codes are freely downloadable.
PB  - Springer Science and Business Media LLC
T2  - Soft Computing
T1  - Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass
DO  - 10.1007/s00500-022-07641-4
ER  - 

@article{
author = "Jakšić, Olga and Jakšić, Zoran and Guha, Koushik and Silva, Ana G. and Laskar, Naushad Manzoor",
year = "2022",
abstract = "A new set of software tools for the prediction of the higher heating values (HHV) of arbitrarily chosen biomass species is presented. A comparative qualitative and quantitative analysis of 12 algorithms for training artificial neural networks (ANN) which predict the HHV of biomass using the proximate analysis is given. Fixed carbon, volatile matter and ash percentage were utilized as inputs. Each ANN had the same structure but a different training algorithm (BFGS Quasi Newton, Bayesian Regularization, Conjugate Gradient—Powell/Beale Restarts, Fletcher–Powell Conjugate Gradient, Polak–Ribiére Conjugate Gradient, Gradient Descent, Gradient Descent Momentum, Variable Learning Rate Gradient Descent, Levenberg–Marquardt, One Step Secant, Resilient Backpropagation, Scaled Conjugate Gradient). To ensure an extended applicability of our results to a wide range of different biomass species, the data conditioning was based on diverse experimental data gathered from the literature, 447 samples overall. Out of these, 301 datasets were used for the training, validation and testing by MathWorks MATLAB Neural Network Fitting Application and by custom designed codes, and 146 remaining datasets were used for the independent evaluation of all training algorithms. The HHV predictions of the ANN-based fitting functions were thoroughly tested and intercompared, to which purpose we developed a test suite which applies mean squared error, coefficient of the determination, mean Poisson deviance, mean Gamma deviance and Friedman test. The comparative analysis showed that several algorithms resulted in ANN-based fitting functions whose outputs correlated well with measured values of the HHV. All programming codes are freely downloadable.",
publisher = "Springer Science and Business Media LLC",
journal = "Soft Computing",
title = "Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass",
doi = "10.1007/s00500-022-07641-4"
}

Jakšić, O., Jakšić, Z., Guha, K., Silva, A. G.,& Laskar, N. M.. (2022). Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass. in Soft Computing
Springer Science and Business Media LLC..
https://doi.org/10.1007/s00500-022-07641-4

Jakšić O, Jakšić Z, Guha K, Silva AG, Laskar NM. Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass. in Soft Computing. 2022;.
doi:10.1007/s00500-022-07641-4 .

Jakšić, Olga, Jakšić, Zoran, Guha, Koushik, Silva, Ana G., Laskar, Naushad Manzoor, "Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass" in Soft Computing (2022),
https://doi.org/10.1007/s00500-022-07641-4 . .

TY  - JOUR
AU  - Jakšić, Olga
AU  - Jakšić, Zoran
AU  - Guha, Koushik
AU  - Jokić, Ivana
AU  - Frantlović, Miloš
PY  - 2021
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/4223
AB  - Random sequences and maps are essential for the applications in cryptography and many other fields in Information Technologies. To achieve true randomness, one still needs to refer to natural phenomena, be it physical, chemical or biological. Here we analyse the possibility to apply stochastic physico–chemical dynamics to generate truly random sequences and bitmaps. To this purpose, we utilize simulations of adsorption–desorption (AD) based intrinsic noise in realistic microsystems and nanosystems. We focus on affinity-based chemical or biological sensors, for instance those based on micro or nanocantilevers, as well as those utilizing plasmonic and generally metamaterial phenomena (refractometric nanosensors.) Random numbers in sequences or maps in both types of structures are generated by simulations of intrinsic fluctuations of AD processes. We present three novel AD-simulation based algorithms, two of them for bitstreams and one for dynamic bitmaps. We use the stochastic simulation algorithm developed for modelling of chemical kinetics. We tested the obtained pseudo random numbers by visual analysis and using our custom designed test suite. We have proven the applicability of the proposed method for generation of random sequences and maps. Our results could be used in digital data encryption and communication. They also point out to a possibility to a hardware implementation of a full random number generator that would incorporate the mentioned micro and nanosystems.
PB  - Springer Nature
T2  - Microsystem Technologies
T1  - Equilibrium fluctuations in chemical reactions: a viable source of random data (numbers, maps and sequences)
DO  - 10.1007/s00542-020-05137-5
ER  - 

@article{
author = "Jakšić, Olga and Jakšić, Zoran and Guha, Koushik and Jokić, Ivana and Frantlović, Miloš",
year = "2021",
abstract = "Random sequences and maps are essential for the applications in cryptography and many other fields in Information Technologies. To achieve true randomness, one still needs to refer to natural phenomena, be it physical, chemical or biological. Here we analyse the possibility to apply stochastic physico–chemical dynamics to generate truly random sequences and bitmaps. To this purpose, we utilize simulations of adsorption–desorption (AD) based intrinsic noise in realistic microsystems and nanosystems. We focus on affinity-based chemical or biological sensors, for instance those based on micro or nanocantilevers, as well as those utilizing plasmonic and generally metamaterial phenomena (refractometric nanosensors.) Random numbers in sequences or maps in both types of structures are generated by simulations of intrinsic fluctuations of AD processes. We present three novel AD-simulation based algorithms, two of them for bitstreams and one for dynamic bitmaps. We use the stochastic simulation algorithm developed for modelling of chemical kinetics. We tested the obtained pseudo random numbers by visual analysis and using our custom designed test suite. We have proven the applicability of the proposed method for generation of random sequences and maps. Our results could be used in digital data encryption and communication. They also point out to a possibility to a hardware implementation of a full random number generator that would incorporate the mentioned micro and nanosystems.",
publisher = "Springer Nature",
journal = "Microsystem Technologies",
title = "Equilibrium fluctuations in chemical reactions: a viable source of random data (numbers, maps and sequences)",
doi = "10.1007/s00542-020-05137-5"
}

Jakšić, O., Jakšić, Z., Guha, K., Jokić, I.,& Frantlović, M.. (2021). Equilibrium fluctuations in chemical reactions: a viable source of random data (numbers, maps and sequences). in Microsystem Technologies
Springer Nature..
https://doi.org/10.1007/s00542-020-05137-5

Jakšić O, Jakšić Z, Guha K, Jokić I, Frantlović M. Equilibrium fluctuations in chemical reactions: a viable source of random data (numbers, maps and sequences). in Microsystem Technologies. 2021;.
doi:10.1007/s00542-020-05137-5 .

Jakšić, Olga, Jakšić, Zoran, Guha, Koushik, Jokić, Ivana, Frantlović, Miloš, "Equilibrium fluctuations in chemical reactions: a viable source of random data (numbers, maps and sequences)" in Microsystem Technologies (2021),
https://doi.org/10.1007/s00542-020-05137-5 . .

TY  - CONF
AU  - Pertin, Osor
AU  - Guha, Koushik
AU  - Jakšić, Olga
AU  - Jakšić, Zoran
PY  - 2021
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/4847
AB  - This paper presents the results of the deploying machine learning models in the design and optimization of a unimorph tapered cantilever with proof mass, aimed for piezoelectric energy harvesting. Multiobjective optimization as described in the paper was performed in order to find the optimal dimensions of the structure, its length, its width at the anchor and the ratio between widths at the anchor and at the tip, with respect to the salient parameters for the energy harvesting applications, namely low frequency and high power generated by the structure. The method is applicable for the optimization of the design of more complex MEMS structures aimed for energy harvesting applications.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - 32nd IEEE International Conference on Microelectronics, MIEL 2021
T1  - AI Assisted Optimization of Unimorph Tapered Cantilever for Piezoelectric Energy Harvesting
SP  - 285
EP  - 288
DO  - 10.1109/MIEL52794.2021.9569184
ER  - 

@conference{
author = "Pertin, Osor and Guha, Koushik and Jakšić, Olga and Jakšić, Zoran",
year = "2021",
abstract = "This paper presents the results of the deploying machine learning models in the design and optimization of a unimorph tapered cantilever with proof mass, aimed for piezoelectric energy harvesting. Multiobjective optimization as described in the paper was performed in order to find the optimal dimensions of the structure, its length, its width at the anchor and the ratio between widths at the anchor and at the tip, with respect to the salient parameters for the energy harvesting applications, namely low frequency and high power generated by the structure. The method is applicable for the optimization of the design of more complex MEMS structures aimed for energy harvesting applications.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "32nd IEEE International Conference on Microelectronics, MIEL 2021",
title = "AI Assisted Optimization of Unimorph Tapered Cantilever for Piezoelectric Energy Harvesting",
pages = "285-288",
doi = "10.1109/MIEL52794.2021.9569184"
}

Pertin, O., Guha, K., Jakšić, O.,& Jakšić, Z.. (2021). AI Assisted Optimization of Unimorph Tapered Cantilever for Piezoelectric Energy Harvesting. in 32nd IEEE International Conference on Microelectronics, MIEL 2021
Institute of Electrical and Electronics Engineers Inc.., 285-288.
https://doi.org/10.1109/MIEL52794.2021.9569184

Pertin O, Guha K, Jakšić O, Jakšić Z. AI Assisted Optimization of Unimorph Tapered Cantilever for Piezoelectric Energy Harvesting. in 32nd IEEE International Conference on Microelectronics, MIEL 2021. 2021;:285-288.
doi:10.1109/MIEL52794.2021.9569184 .

Pertin, Osor, Guha, Koushik, Jakšić, Olga, Jakšić, Zoran, "AI Assisted Optimization of Unimorph Tapered Cantilever for Piezoelectric Energy Harvesting" in 32nd IEEE International Conference on Microelectronics, MIEL 2021 (2021):285-288,
https://doi.org/10.1109/MIEL52794.2021.9569184 . .

TY  - JOUR
AU  - Pertin, Osor
AU  - Guha, Koushik
AU  - Jakšić, Olga
PY  - 2021
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/4784
AB  - This paper presents a study on the design and multiobjective optimization of a bimorph-segmented linearly tapered piezoelectric harvester for low-frequency and multimode vibration energy harvesting. The procedure starts with a significant number of FEM simulations of the structure with different geometric dimensions—length, width, and tapering ratio. The datasets train the artificial neural network (ANN) that provides the fitting function to be modified and used in algorithms for optimization, aiming to achieve minimal resonant frequency and maximal generated power. Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) methods were used to train the ANN, then the goal attainment method (GAM) and genetic algorithm (GA) were used for optimi-zation. The dominant solution resulted from optimization by the genetic algorithm integrated with the ANN fitting function obtained by the SCG training method. The optimal piezoelectric harvester is 121.3 mm long and 71.56 mm wide and has a taper ratio of 0.7682. It ensures over five times greater output power at frequencies below 200 Hz, which benefits the low frequency of the vibration spectrum. The optimized design can harness the power of higher-resonance modes for multimode applications.
PB  - MDPI
T2  - Computation
T1  - Artificial intelligence-based optimization of a bimorph-segmented tapered piezoelectric mems energy harvester for multimode operation
VL  - 9
IS  - 8
SP  - 84
DO  - 10.3390/computation9080084
ER  - 

@article{
author = "Pertin, Osor and Guha, Koushik and Jakšić, Olga",
year = "2021",
abstract = "This paper presents a study on the design and multiobjective optimization of a bimorph-segmented linearly tapered piezoelectric harvester for low-frequency and multimode vibration energy harvesting. The procedure starts with a significant number of FEM simulations of the structure with different geometric dimensions—length, width, and tapering ratio. The datasets train the artificial neural network (ANN) that provides the fitting function to be modified and used in algorithms for optimization, aiming to achieve minimal resonant frequency and maximal generated power. Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) methods were used to train the ANN, then the goal attainment method (GAM) and genetic algorithm (GA) were used for optimi-zation. The dominant solution resulted from optimization by the genetic algorithm integrated with the ANN fitting function obtained by the SCG training method. The optimal piezoelectric harvester is 121.3 mm long and 71.56 mm wide and has a taper ratio of 0.7682. It ensures over five times greater output power at frequencies below 200 Hz, which benefits the low frequency of the vibration spectrum. The optimized design can harness the power of higher-resonance modes for multimode applications.",
publisher = "MDPI",
journal = "Computation",
title = "Artificial intelligence-based optimization of a bimorph-segmented tapered piezoelectric mems energy harvester for multimode operation",
volume = "9",
number = "8",
pages = "84",
doi = "10.3390/computation9080084"
}

Pertin, O., Guha, K.,& Jakšić, O.. (2021). Artificial intelligence-based optimization of a bimorph-segmented tapered piezoelectric mems energy harvester for multimode operation. in Computation
MDPI., 9(8), 84.
https://doi.org/10.3390/computation9080084

Pertin O, Guha K, Jakšić O. Artificial intelligence-based optimization of a bimorph-segmented tapered piezoelectric mems energy harvester for multimode operation. in Computation. 2021;9(8):84.
doi:10.3390/computation9080084 .

Pertin, Osor, Guha, Koushik, Jakšić, Olga, "Artificial intelligence-based optimization of a bimorph-segmented tapered piezoelectric mems energy harvester for multimode operation" in Computation, 9, no. 8 (2021):84,
https://doi.org/10.3390/computation9080084 . .

TY  - JOUR
AU  - Jokić, Ivana
AU  - Jakšić, Olga
AU  - Frantlović, Miloš
AU  - Jakšić, Zoran
AU  - Guha, Koushik
AU  - Rao, Karumuri Srinivasa
PY  - 2020
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/4024
AB  - The importance of adsorption-based biochemical/biological sensors in biochemistry and biophysics is paramount. Their temporal response gives information about the presence of a biochemical/biological analyte, its concentration and its interactions with the adsorption sites (which may be an integral part of the surface itself or immobilized functionalizing molecules). Mathematical models of the temporal response taking into account as many relevant effects as possible are essential for obtaining reliable information. We present a novel model taking into account the bimodal affinity of a sensing surface (adsorption occurs on two distinct site types), and the adsorption-caused depletion of the analyte from the sample. We perform qualitative and quantitative analysis of the analyte depletion influence on the bimodal adsorption, and of the influence of the sensing surface inhomogeneity on the sensor temporal response, for different analyte concentrations and different fractions of two types of adsorption sites. Since the presented mathematical model deals with the realistic cases of the sensing surface non-uniformity and the finite amount of analyte present in the sensor reaction chamber, it enables improved accuracy in interpreting the measurement data. Our results are general, i.e. valid for any adsorption sensor (microcantilevers, plasmonics) and for arbitrary sensor dimensions.
PB  - Springer
T2  - Microsystem Technologies
T1  - Temporal response of biochemical and biological sensors with bimodal surface adsorption from a finite sample
DO  - 10.1007/s00542-020-05051-w
ER  - 

@article{
author = "Jokić, Ivana and Jakšić, Olga and Frantlović, Miloš and Jakšić, Zoran and Guha, Koushik and Rao, Karumuri Srinivasa",
year = "2020",
abstract = "The importance of adsorption-based biochemical/biological sensors in biochemistry and biophysics is paramount. Their temporal response gives information about the presence of a biochemical/biological analyte, its concentration and its interactions with the adsorption sites (which may be an integral part of the surface itself or immobilized functionalizing molecules). Mathematical models of the temporal response taking into account as many relevant effects as possible are essential for obtaining reliable information. We present a novel model taking into account the bimodal affinity of a sensing surface (adsorption occurs on two distinct site types), and the adsorption-caused depletion of the analyte from the sample. We perform qualitative and quantitative analysis of the analyte depletion influence on the bimodal adsorption, and of the influence of the sensing surface inhomogeneity on the sensor temporal response, for different analyte concentrations and different fractions of two types of adsorption sites. Since the presented mathematical model deals with the realistic cases of the sensing surface non-uniformity and the finite amount of analyte present in the sensor reaction chamber, it enables improved accuracy in interpreting the measurement data. Our results are general, i.e. valid for any adsorption sensor (microcantilevers, plasmonics) and for arbitrary sensor dimensions.",
publisher = "Springer",
journal = "Microsystem Technologies",
title = "Temporal response of biochemical and biological sensors with bimodal surface adsorption from a finite sample",
doi = "10.1007/s00542-020-05051-w"
}

Jokić, I., Jakšić, O., Frantlović, M., Jakšić, Z., Guha, K.,& Rao, K. S.. (2020). Temporal response of biochemical and biological sensors with bimodal surface adsorption from a finite sample. in Microsystem Technologies
Springer..
https://doi.org/10.1007/s00542-020-05051-w

Jokić I, Jakšić O, Frantlović M, Jakšić Z, Guha K, Rao KS. Temporal response of biochemical and biological sensors with bimodal surface adsorption from a finite sample. in Microsystem Technologies. 2020;.
doi:10.1007/s00542-020-05051-w .

Jokić, Ivana, Jakšić, Olga, Frantlović, Miloš, Jakšić, Zoran, Guha, Koushik, Rao, Karumuri Srinivasa, "Temporal response of biochemical and biological sensors with bimodal surface adsorption from a finite sample" in Microsystem Technologies (2020),
https://doi.org/10.1007/s00542-020-05051-w . .