1. Introduction

This paper is an extension of work originally presented in the 19^th International Symposium on Communications and Information Technologies (ISCIT) 2019 [1].

The Internet of Things (IoT) relies on smart environments comprises of wireless sensor networks [2] and personal area networks [3], [4] in which one interacts with IoT infrastructure must be uniquely identified. However, one big challenge is a need to commence seamless integration of IoT solutions with surroundings and with people [5]. Flexible materials for various IoT applications require a high level of integrity of components and mechanical robustness with repeated rolling and bending capabilities. In particular, flexible smart fitness watches, RFID tags and wearable sensors are good examples of applications that use flexible material. Besides, Elasticity and stretch-ability of materials are the key properties required by electronic devices that require large and reversible deformation. These bendable devices will also need to be versatile and may require the ability to store energy, operate with low power, and integrated with other devices and Internet of Things (IoT) applications [6]-[8].

The growth of wearable technology and the success of wearable devices has been hampered by limits such as the lack of materials and manufacturing techniques for seamless integration with electronic parts [9], the robustness in washing, drying and moulding [10], [11]. To address these challenges flexible polymer-based chipless RFID tag can be a good candidate.

In recent years, the research on wearable wireless communication and flexible devices has increased rapidly due to its usability and applications in personal communication systems [12]. For examples; Flexible antennas and Radio Frequency Identification (RFID) tags are used to keep track of several daily-routine activities, such as heartbeat and blood pressure measurement, the distance walked, the steps are taken and the calories burnt [13].

RFID Technology is growing rapidly and has significant importance in various medical, industrial and commercial applications [13], [14] such as UHF RFID tags, Chipless RFID tags and Near Field Magnetic Coupled tags are being in used in various commercial applications and medical applications [15]. In the traditional RFID technology, the backscattered field from the tags are used to transmit information by using time-variant loading or scatterer modulation [16]-[19] however, this technique requires the physical movement of the object which needs to be coded by using inductive coupling or the impedance modulation. Besides, the conventional RFID tags are active circuit, carrying a small near field chip including a power source and a memory at the centre of the body. Whereas the chipless RFID technology doesn’t require memory or a chip to save codes, instead, it uses the physical aspects of the body such as geometry and the shape to send the data [13]. The primary principle of the chipless technology is to get the resonance from the body and to encode the backscattered signal at the receiver, without additional circuitry or communication protocols [20]. Various techniques have been proposed but generally, the chipless RFID technology can be categorised into two groups. The first one is the time domain-based also called time-domain reflectometry-based designs, Surface Acoustic Wave (SAW) is an example of this. The second group is frequency domain-based RFID tags sometimes known as spectral-based tags, Frequency Selective Surface (FSS) is an example of this technique. The SAW-based chipless RFID tags are primarily worked by piezoelectricity, in which Interdigital Transducer (IDT) on the surface of piezoelectric materials are used to convert the electromagnetic wave into the acoustic wave. The acoustic waves generate the identification code while propagating through the metallic surfaces of the tag separated by a certain distance and stored on the tag [21], [22]. The SAW RFID tags are commercially available but some important issues such as the reduction in the size, increase in the read range and the data capacity needs to address 15. Besides, the FSS based frequency domain chipless RFID technique is quite promising, in which an incident electromagnetic wave is completely or partially reflected from the surface of the resonators and matches with the resonance frequency, based on the nature and the shape of the elements. The chipless RFID tags are known as passive tags as no additional power is required to operate, which make these tags promising for general IoT applications.

Passive chipless RFID tags have gained much importance as a good candidate for IoT applications because of no additional circuitry, small size, increased lifetime and the cost-effectiveness [23]. As compared to conventional RFID tags, passive tags have many significant advantages such as they are extremely cheap and can be printed over a piece of paper simple as barcodes. Moreover, the wearable applications in which washing and heating will affect the energy source, the absence of the battery and the electrical circuits make these tags promising [24].

The general structure of a chipless RFID system is an adaptive version of the Ultra-Wide Band (UWB) frequency radar [25], in which an incident electromagnetic wave from a transmitter strikes at the surface of the tag and reflect toward the reader and decoder, see Figure 1. When an incident wave Eⁱ on the surface of the conductive element, a current is induced at the resonant frequency, 9.2GHz in the case of chipless Bow-Tie RFID tag, see Figure 1.

In this paper, a 4-bit Bow-tie shaped chipless RFID tag is designed and the transient behaviour by the application of ElectroMagnetic (EM) wave is explained. In this design, the polarization feature of non-periodic bow-tie shaped cells is taken into account to get destructive and constructive interferences. The projection of the incidents wave and reflected field and decoded in terms of bits assigned to Radar Cross Sections (RCS) [26]. The RCS is a parameter to describe the level of scattering of a target  object, in this case, a tag is the target object.

Figure 1: Structural diagram of Bow-Tie chip-less RFID system

This paper employs a simple equivalent model of the tag by using SEM-based circuit modelling. Furthermore, the coupling coefficients and the induced currents over the rings are evaluated and transient response of the Bow-Tie RFID tag is analysed. Finally, the maximum read range is calculated which is 1.8m for 0dBm and extendable to 2.14m for higher incident power, which is maximum to our knowledge for high frequencies of the range 8 to 18GHz.

The rest of the paper is organised as follows: Section II and Section III presents the tag design and its working principle, the material characterisation and optimised designs. Section IV presents the RCS measurements, which further describes the experimental verification of codes, on body RCS measurements, bending analysis and discussion. Finally, the conclusion is presented in Section V.

2. Design and Operation of Bow-Tie Chipless RFID Tag

The Bow-Tie shaped chipless RFID tag proposed in [1] is based on conventional Bow-tie shape which is opted as it has the most intense current flow on the edges of the resonating elements [27], hence cause a more intense backscattered signal. Another reason to opt this design is the fact that it provides more flexibility in designing at higher frequencies unlike octagonal [26], square [28], circular [29]  and triangular [30] designs. Moreover, in Bow-tie chipless RFID tag the asymmetrical shape of the tag in which the distance between the resonators is not constant, it diminished the mutual coupling as compared to the symmetrical surfaces, placed very close to each other [31].  In this paper, a 4-bit Bow-tie chipless RFID tag is proposed and the transient behaviour of the tag is presented. The robust tag is comprised of a periodic pattern of six Bow-tie shaped cells applicable for general IoT and wearable applications.

2.1. Working Principle

The resonant frequency of the tag depends on the inductance and the capacitance between the concentric metallic bow-tie shaped elements [32]. Therefore, when an EM wave strikes at the surface of the tag it induces currents and the corresponding capacitance and the inductances are changed. The extent of the reflected or transmitted wave after falling on the surface, depends upon the placement and the geometry of the rings. Aa results, surface currents (J_in) and (J_out) are induced on the inner and outer bow-tie shaped rings, respectively, as shown in Figure 2.

     In this case, the whole structure including all the surrounding bow-tie rings, interact with each other and cause the creation of the standing waves. The standing waves produced the same polarization with an odd multiple of the difference of π phase by these surface currents which are approximately equal but opposite in phase. Hence, destructive interference results in backscatter wave (Es) and create a specific frequency response. Figure 2. represents the surface current distribution over the elementary cell and all periodic bow-tie shaped rings which tends to be minimum at the inner side of the tag, at the base and ceiling while maximum and almost constant in all vicinities of the Bow-Tie tag. The area of the tag showing minimum concentration correspond to capacitive behaviour and the area where the surface current is intensive corresponds to the inductive behaviour of the tag, see Figure 2.

Figure 2: E-Field distribution and the directions of induced surface current (J)

3. Electromagnetic Behaviour of the Bow-Tie RFID Tag

The circuit model of the Bow-Tie chipless RFID tag provides insight into the electromagnetic response of the circuit when an incident EM wave strike at the tag and the induced current re-radiate the scattered fields. The current induced on the tag can be evaluated in different ways. One method is based on Singularity Expansion Method (SEM) in which the current induced on the tag is expanded in terms of the singularity poles of the tag. The concept of SEM has been widely used in circuit theory for a long time. Later on, this technique was used in evaluating EM response and designing of chipless RFID tags [33],[34],[35].

3.1. SEM-Based Equivalent Circuit of Chipless RFID Tag

In this paper, the SEM-based equivalent circuit of the octagonal chipless RFID tag is presented where the solution is evaluated as a collection of poles (Sn), coupling coefficient (Rn) and the entire function (Fe) in the complex frequency domain. The scattering analysis is performed by representing the chipless RFID tag with an equivalent circuit representation, see Fig. For electromagnetic response, the RFID scatterer assumed to be Perfectly Electric Conductor (PEC) in free space. Let’s N be a total number of scatterers and an incident electric field Eⁱ strike at the surface of the tag and E^s are scattered electric field with polarization vectors â_i and â_s, respectively. The Eⁱ causes an induced current (J_n) on the surface and the global resonances are demonstrated in the time domain as damped sinusoidal analogous to the Complex Natural Response (CNR) of the scatterer with some weighting residue as coupling coefficients (R_N). The distance between the two scatterers is optimised in such a way that the coupling between one and the alternative scatterer is very small to be negligible. Hence, the coupling between the two scatterers only at a time and ignored the effects of the others on them.

The SEM-based circuit equivalent model of the octagonal chipless RFID tag is presented in Figure 3. where, R₁, R₂…..R_N, L₁, L­₂,…..L_N  define the resistances and the inductances and C₁, C­₂,… C_N is the inductance between two consecutive scatterers for N number of resonators.

Figure 3: SEM-based equivalent circuit of Bow-Tie RFID Tag with N number of resonators

3.2. Transient Response of Bow-Tie Chipless RFID Tag

The Eⁱ coupled to CNR’s by coupling coefficients Rⁱ_1, Rⁱ_2,….. Rⁱ_n which depends on the direction and the polarization of incident electric filed, whereas   Rⁱ_1, Rⁱ_2,….. Rⁱ_n denotes the coupling coefficients corresponding to E^s. The CNR of the model is presented by a parallel RLC circuit in series with delay line (TL) which designate the turn-on time of CNR, and Fe shows the early time response of the tag. Whereas, the input Vi and output voltages Vs are defined at the transmitting and receiving antennas ports, respectively.

Figure 4. shows the geometry of the Octagonal tag, when Eⁱ strikes at the surface of the tag and E^s electric field is scattered with polarization vectors â_i and â_s assuming the incident electric field as

Figure 4: The geometry and the internal circuit modelling of the Bow-Tie RFID Tag Illuminated by an Incident Field Ei

a Dirac-delta function then the transfer function of the tag in the complex s-plane is defined as [15]

After some mathematical manipulation, the transfer function in terms of early-time response H_e(s) and late time response H₁(s) with CNR’s can be written as [15]

where Q represents quality factor which is kept below 0.5 and the RFID first resonator resonates at f₁=ω/2π.

3.3. Evaluation of Surface Current Distribution (Jn) and the Coupling Coefficients (Rn).

The current distribution over the first resonator, the outermost octagonal ring is I₁ and V₁ is the induced voltage due to incident fields corresponding to the impedance Z₁ of the resonator then the current distribution over the octagonal scatterers can be evaluated by discretizing the length of the ring into N number of segments [15]  as

Figure 5: The induced voltages and currents distribution over the surface of the tag

where  corresponds to N×N matrix referred to as system impedance matrix, and are the response vector of N×1corressponding to the incident field, see Figure 5.

The CNR’s for the first scatterer is calculated from

where s₀ is the initial guess of resonant frequency, by repeating this procedure more accurate values can be evaluated for the first scatterer. For Bow-Tie design, the first resonance was calculated at approximately 9.2 GHz.

The equation (5) is modified to get current distribution for the first resonator of octagonal chipless RFID tag as

where R_n the residue of nth pole and J_n is the surface current distribution over the surface of the scatterer, which is evaluated in the next section. Also, The time-domain response can be obtained by applying inverse Laplace transform to (9).

To study the mechanism of scattering by a 4-bit octagonal chipless RFID tag, the surrounding medium is supposed as a free space with permittivity ε₀ and permittivity μ₀, the scatterer is PEC and the thickness of the tag is very small hence considered to be zero. Consider an incident field Eⁱ strikes and the induced current over the surface of the tag is (J_n) then the scattered field E^s from the tag is either obtained from Electric Field Integral Equation (EFIE) or Magnetic Field Integral Equation (MFIE) [36]. The EFIE is used which is represented in Laplace form [37].

where A represents the surface area of the octagonal chipless RFID tag, , k represents the propagation constant k=s/c in the complex frequency domain and G₀ defines the scalar Green’s function in the free space.

Figure 6: The variation of the distances between consecutive Bow-Tie shaped rings

      The evaluation of coupling coefficients (R₁ to R₄) was not easy in the case of chipless Bow-tie RFID. This is because the distance between the two consecutive bow-tie shaped rings is not constant and hence mutual coupling varies, see Figure 6. Therefore, coupling coefficients are evaluated for the values of the maximum and the minimum distances between two consecutive rings at corresponding resonance,  see Table 1. The values of coupling coefficients between the first two consecutive rings are -0.010 and -0.021 calculated for the maximum and the minimum spacing of 1.2mm and 0.23mm, respectively for 9.2GHz frequency, see Table 1. Similarly, all values are calculated for corresponding resonant frequencies of 11.3, 14 and 16.9 GHz. The maximum value of R_n from the table is -0.021 which is a sufficiently low value that assures reliable reading of the coded information. To reduce the mutual coupling the average spacing between the resonators can be increased, therefore the size of the tag would be significantly increased.

Table 1: Coupling Coefficients for Corresponding Resonances

4. Flexibility and Bending Analysis

      The Bow-Tie tag is designed on 13.44×11.56mm2 PET substrate of 70µm thickness with conductive gold rings varied in thickness [1].

Figure 7: Chipless Bow-Tie RFID tags a) Experimental set-up in chamber b) Bending aspects of fabricated Chip-less RFID Tags at 27mm and 14mm

4.1. Experimental Setup and RCS Results

The chipless Bow-tie RFID tag is printed on a PET substrate by using Universal  Laser Systems   (ULS). The   measurements of

Figure 8: |RCS| measurements for the Flat, bending radius at 27mm and bending radius at 14mm

the RCS obtained by the reflections from the tag are analysed by Vector Network Analyser (VNA).  For this purpose, Keysigth VNA series E5063A is used with two similar horn antennas operating at 4-18GHz frequencies. To conduct the measurement, the testing is performed in the FRANKONIA anechoic chamber, having a frequency range of 30 MHz to 40 GHz. The compact Bow-Tie RFID tag design is highly flexible and the bit codes are successfully obtained up to the bending radius of 14mm [38]. The Bow-Tie RFID tag undergoes bending for the following cases of curvatures, flat, 14mm and 27mm, see Figure 7 (b).

The Passive Bow-tie RFID tag is placed in the horizontal (H) position to perform the bending analysis for the following cases flat, 14mm and 27mm. All the testing was performed in the Frankonia anechoic chamber. One of the impacts of bending is the signal strength degradation of the backscattered waves and the significant shift in the resonant frequency. In other words |RCS| is lowered while tag undergoes bending. In this case, since the distance of the tag from the reader, is kept very small about 12cm from the transmitter, so a very small reduction in the signal strength is obtained at 0dBm power. However, the percentage shift in the resonant frequency is quite prominent especially at a higher level of bending at 14mm, see Figure 8.

Figure 8. depicts the impact of bending on the RCS obtained in the chamber. A shift of 4% from the resonant frequency is obtained, when a PET substrate-based chipless Bow-tie RFID tag undergoes bending curvature 27mm for the lower range of the frequencies 8-14 GHz, whereas, more than 10% shift is observed for higher range of frequency up to 18GHz. Hereafter, there is no noticeable reduction of the signal strength is observed because of the small distance of tag from the reader. Hence, the experimental results depict that the RCS obtained and encode in frequency signature, from the bent chip-less RFID tag fabricated on the flexible PET substrate.

5. Maximum Read Range Estimation

          This section provides the maximum read range of Bow-Tie Chipless RFID tag. The complex RCS feature ( ) of octagonal chipless RFID designs can be obtained by the following equations which are described in [39].

Where  is associated with the measurement taken in the empty chamber,  corresponds to reference value taken from define setup which could be any conductive surface,  In this case, a copper square of 50mm x 50mm is taken.  is associated with the simulation results for the same reference. The results achieved for all the tags show a good agreement between the measured and simulated designs.

The radar equation is used [40] to evaluate the maximum read range of the RFID tag. For this, numerically calculated RCS are considered by using the equation is follows:

Where and  are the gains of the transmitting and receiving horn antennas,  is the power transmitted by VNA to measure RCS, λ is the wavelength.  is the minimum power receive by receiving antenna and  is the experimental values of RCS obtained from (14). For maximum read range calculations  is taken for 1111bit codes, see Figure 8. flat case. The average RCS is obtained from a series of experiments [1] is around 0.02 m². The maximum read range is evaluated for -10, -3 and 0dBm transmitted power from VNA and the maximum range observed is 1.80m and extendable to 2.15m for 3dBm power. Hence, a robust Bow-tie chipless  RFID tag with transient analysis is successfully designed fabricated and tested, which is applicable for the IoT applications and the read range of the tag and bit capacity can be increased in future by using the asymmetrical property of the tag.

Table 2: Maximum Read Range Estimation

6. Conclusion

      This paper is an extension of Novel Bow-Tie Chipless RFID tag which studies SEM-based circuit modelling of the bow-tie-shaped tag. The electromagnetic response of the tag is presented and coupling coefficients are obtained by transient analysis. The behaviour of the RFID tag is completely studied and experimentally verified for different patterns of bitstreams measured in terms of RCS. The coupling coefficients measured in this analysis evaluated for the values of the maximum and the minimum distances between two consecutive rings at corresponding resonance of 9.2, 11.3, 14 and 16.9 GHz. The maximum value of R_n from the table is -0.021 which is a adequately low value that would not impact the reliable coding of the information. Furthermore, the bending on Flexible Chip-less Bow-Tie RFID tag operating within the range of 8-18GHz are described up to the maximum bend of 14mm and the information stored on a bent chip-less RFID tag made of PET is successfully recovered. Finally, the maximum read range for the RFID tag is evaluated which is 1.8m for 0dBm power but extendable to 2.15m for higher powers. This range is maximum to our knowledge for such a high-frequency range of 8-18GHz.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgement

I would like to acknowledge the great contribution of my research colleague Panagiotis Ioannis Theoharis, for helping me in the measurements and the fabrications of the tags.

1. M.U.A. Khan, R. Raad, J. Foroughi, F.E. Tubbal, J. Xi, “Novel Bow-Tie Chip-less RFID Tag for Wearable Applications,” in 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE: 10–13, 2019, https://doi.org/10.1109/iscit.2019.8905208.
2. M. Younan, E.H. Houssein, M. Elhoseny, A.A. Ali, “Challenges and recommended technologies for the industrial internet of things: A comprehensive review,” Measurement, 151, 107198, 2020, https://doi.org/10.1016/j.measurement.2019.107198.
3. A. Čolaković, M. Hadžialić, “Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues,” Computer Networks, 144, 17-39, 2018, https://doi.org/10.1016/j.comnet.2018.07.017
4. E. Jovanov, “Wearables Meet IoT: Synergistic Personal Area Networks (SPANs),” Sensors, 19(19), 4295, 2019, https://doi.org/10.3390/s19194295
5. T.M. Fernández-Caramés, P. Fraga-Lamas, “Towards the Internet of smart clothing: A review on IoT wearables and garments for creating intelligent connected e-textiles,” Electronics, 7(12), 405, 2018, 2018. https://doi.org/10.3390/electronics7120405
6. Y. Leterrier, “Mechanics of curvature and strain in flexible organic electronic devices,” Handbook of Flexible Organic Electronics: Materials, Manufacturing and Applications, 1, 2014, https://doi.org/10.1016/b978-1-78242-035-4.00001-4
7. N.L. Pira, “Smart integrated systems and circuits using flexible organic electronics: Automotive applications,” Handbook of Flexible Organic Electronics: Materials, Manufacturing and Applications, 345, 2014, https://doi.org/10.1016/b978-1-78242-035-4.00014-2
8. R. Anwar, L. Mao, H. Ning, R. Atakan, H.A. Tufan, S. uz Zaman, C. Cochrane, S.K. Bahadir, V. Koncar, F. Kalaoglu, B.A. Auld, C.E. Baum, D. Betancourt, K. Haase, A. Hübler, F. Ellinger, A.T. Blischak, M. Manteghi, A.Čolaković, L. Corchia, G. Monti, E. De Benedetto, A. Cataldo, L. Angrisani, P. Arpaia, L. Tarricone, F. Costa, S. Genovesi, A. Monorchio, et al., “The internet of things: How the next evolution of the internet is changing everything,” IEEE Transactions on Antennas and Propagation, 59(12), 1–4, 2014, https://doi.org/10.7551/mitpress/10277.003.0004
9. L. Corchia, G. Monti, E. De Benedetto, A. Cataldo, L. Angrisani, P. Arpaia, L. Tarricone, “Fully-Textile, Wearable Chipless Tags for Identification and Tracking Applications,” Sensors, 20(2), 429, 2020, https://doi.org/10.3390/s20020429
10. L. Corchia, G. Monti, L. Tarricone, “Durability of wearable antennas based on nonwoven conductive fabrics: Experimental study on resistance to washing and ironing,” International Journal of Antennas and Propagation, 2018, 2018, https://doi.org/10.1155/2018/2340293
11. R. Atakan, H.A. Tufan, S. uz Zaman, C. Cochrane, S.K. Bahadir, V. Koncar, F. Kalaoglu, “Protocol to assess the quality of transmission lines within smart textile structures,” Measurement, 152, 107194, 2020, https://doi.org/10.1016/j.measurement.2019.107194
12. Y. Wang, L. Li, B. Wang, L. Wang, “A body sensor network platform for in-home health monitoring application,” in Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications, IEEE: 1–5, 2009, https://doi.org/10.1109/icut.2009.5405731
13. K. Finkenzeller, RFID handbook: fundamentals and applications in contactless smart cards, radio frequency identification and near-field communication, John Wiley & Sons, 2010, https://doi.org/10.1002/9780470665121
14. D.K. Klair, K.-W. Chin, R. Raad, “A survey and tutorial of RFID anti-collision protocols,” IEEE Communications Surveys & Tutorials, 12(3), 400–421, 2010, https://doi.org/10.1109/surv.2010.031810.00037
15. R. Rezaiesarlak, M. Manteghi, CHIPLESS RFID, Springer, 2016, https://doi.org/10.1007/978-3-319-10169-9_3
16. J.P. Vinding, Interrogator-responder identification system, 1969.
17. O.E. Rittenbach, Communication by radar beams, 1969, https://doi.org/10.1002/sce.3730530262
18. R.M. Richardson, Remotely actuated radio frequency powered devices, 1963.
19. R.F. Harrington, “Field measurements using active scatterers (correspondence),” IEEE Transactions on Microwave Theory and Techniques, 11(5), 454–455, 1963, .https://doi.org/10.1109/tmtt.1963.1125707
20. H. Khaleel, Innovation in wearable and flexible antennas, Wit Press, 2014, https://doi.org/10.2495/978-1-84564-986-9/008
21. D.P. Morgan, “History of SAW devices,” in Proceedings of the 1998 IEEE International Frequency Control Symposium (Cat. No. 98CH36165), IEEE: 439–460, 1998, https://doi.org/10.1109/freq.1998.717937
22. B.A. Auld, Acoustic fields and waves in solids, Рипол Классик, 1973.
23. V.D. Hunt, A. Puglia, M. Puglia, RFID: a guide to radio frequency identification, John Wiley & Sons, 2007.
24. J. Foroughi, T. Mitew, P. Ogunbona, R. Raad, F. Safaei, “Smart Fabrics and Networked Clothing: Recent developments in CNT-based fibers and their continual refinement,” IEEE Consumer Electronics Magazine, 5(4), 105–111, 2016, https://doi.org/10.1109/mce.2016.2590220
25. N.C. Karmakar, R. Koswatta, P. Kalansuriya, E. Rubayet, Chipless RFID reader architecture, Artech House, 2013.
26. D. Betancourt, K. Haase, A. Hübler, F. Ellinger, “Bending and folding effect study of flexible fully printed and late-stage codified octagonal chipless RFID tags,” IEEE Transactions on Antennas and Propagation, 64(7), 2815–2823, 2016, https://doi.org/10.1109/tap.2016.2559522
27. A.C. Durgun, C.A. Balanis, C.R. Birtcher, D.R. Allee, “Design, simulation, fabrication and testing of flexible bow-tie antennas,” IEEE Transactions on Antennas and Propagation, 59(12), 4425–4435, 2011, https://doi.org/10.1109/tap.2011.2165511
28. F. Costa, S. Genovesi, A. Monorchio, “A chipless RFID based on multiresonant high-impedance surfaces,” IEEE Transactions on Microwave Theory and Techniques, 61(1), 146–153, 2013, https://doi.org/10.1109/tmtt.2012.2227777
29. M. Martinez, D. van der Weide, “Compact slot-based chipless RFID tag,” in 2014 IEEE RFID Technology and Applications Conference (RFID-TA), IEEE: 233–236, 2014, https://doi.org/10.1109/rfid-ta.2014.6934234
30. S. Rauf, M.A. Riaz, H. Shahid, M.S. Iqbal, Y. Amin, H. Tenhunen, “Triangular loop resonator based compact chipless RFID tag,” IEICE Electronics Express, 14.20161262, 2017, https://doi.org/10.1587/elex.14.20161262
31. M.A. Riaz, H. Shahid, S.Z. Aslam, Y. Amin, A. Akram, H. Tenhunen, “Novel T-shaped resonator based chipless RFID tag,” IEICE Electronics Express, 14.20170728, 2017, https://doi.org/10.1587/elex.14.20170728
32. R. Anwar, L. Mao, H. Ning, “Frequency selective surfaces: a review,” Applied Sciences, 8(9), 1689, 2018, https://doi.org/10.3390/app8091689
33. A. Blischak, M. Manteghi, “Pole residue techniques for chipless RFID detection,” in 2009 IEEE Antennas and Propagation Society International Symposium, IEEE: 1–4, 2009, https://doi.org/10.1109/aps.2009.5172097
34. A.T. Blischak, M. Manteghi, “Embedded singularity chipless RFID tags,” IEEE Transactions on Antennas and Propagation, 59(11), 3961–3968, 2011, https://doi.org/10.1109/tap.2011.2164191
35. R. Rezaiesarlak, M. Manteghi, “Complex-natural-resonance-based design of chipless RFID tag for high-density data,” IEEE Transactions on Antennas and Propagation, 62(2), 898–904, 2013, https://doi.org/10.1109/tap.2013.2290998
36. C.E. Baum, The singularity expansion method, Springer: 129–179, 1976, https://doi.org/10.1007/3540075534_8
37. J.-M. Jin, Theory and computation of electromagnetic fields, John Wiley & Sons, 2011, https://doi.org/10.1002/9780470874257
38. M.U.A. Khan, R. Raad, J. Foroughi, F. Tubbal, P.I. Theoharis, M.S. Raheel, “Effects of Bending Bow-Tie Chipless RFID Tag for Different Polymer Substrates,” in 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE: 1–4, 2019, https://doi.org/10.1109/icspcs47537.2019.9008594
39. A. Vena, E. Perret, S. Tedjini, “High-capacity chipless RFID tag insensitive to the polarization,” IEEE Transactions on Antennas and Propagation, 60(10), 4509–4515, 2012, https://doi.org/10.1109/tap.2012.2207347
40. I.Y. Immoreev, “-Signal Waveform Variations in Ultrawideband Wireless Systems: Causes and Aftereffects,” Ultrawideband Radar: Applications and Design, 71–104, 2012, https://doi.org/10.1201/b12356-4

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2. Tinofirei Museba, Koenraad Vanhoof, "An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 01–11, 2024. doi: 10.25046/aj090301
3. Asrar Hussain Alderham, Emad Sami Jaha, "Improved Candidate-Career Matching Using Comparative Semantic Resume Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 1, pp. 15–32, 2024. doi: 10.25046/aj090103
4. John Tsiligaridis, "Tree-Based Ensemble Models, Algorithms and Performance Measures for Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 19–25, 2023. doi: 10.25046/aj080603
5. Zobeda Hatif Naji Al-azzwi, Alexey N. Nazarov, "MRI Semantic Segmentation based on Optimize V-net with 2D Attention", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 73–80, 2023. doi: 10.25046/aj080409
6. Younes Fakhradine El Bahi, Latifa Ezzine, Zineb Aman, Imane Moussaoui, Miloud Rahmoune, Haj El Moussami, "Distribution Management Problem: Heuristic Solution for Vehicle Routing Problem with Time Windows (VRPTW) in the Moroccan Petroleum Sector", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 66–72, 2023. doi: 10.25046/aj080408
7. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, Thiagarajan Murugasamy, "An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 84–94, 2023. doi: 10.25046/aj080110
8. Anh-Thu Mai, Duc-Huy Nguyen, Thanh-Tin Dang, "Transfer and Ensemble Learning in Real-time Accurate Age and Age-group Estimation", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 262–268, 2022. doi: 10.25046/aj070630
9. Vanamala H R, Samriddha Shukla, Vijaya krishna A, "Ensemble Extreme Learning Algorithms for Alzheimer’s Disease Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 204–211, 2022. doi: 10.25046/aj070622
10. Jabrane Slimani, Abdeslam Kadrani, Imad EL Harraki, El hadj Ezzahid, "Long-term Bottom-up Modeling of Renewable Energy Development in Morocco", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 129–145, 2022. doi: 10.25046/aj070515
11. Diad Ahmad Diad, Domra Kana Janvier, Abdelhakim Boukar, Valentin Oyoa, "The use of Integrated Geophysical Methods to Assess the Petroleum Reservoir in Doba Basin, Chad", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 34–41, 2022. doi: 10.25046/aj070406
12. Mohamed Boumaza, Yacine Boumaza, "Hole-Confined Polar Optical Phonon Interaction in \(\mathrm{Al_{0.35}Ga_{0.65}As/GaAs/Al_{0.25}Ga_{0.75}As}\) Quantum Wells", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 82–86, 2022. doi: 10.25046/aj070309
13. Inam Abousaber, "Digital Competencies of Saudi University Graduates Towards Digital Society: The Case of The University of Tabuk", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 171–178, 2022. doi: 10.25046/aj070217
14. Marcos Crescencio González Domínguez, Pedro Guillermo Reyes Romero, Aarón Gómez Díaz, Horacio Martínez Valencia, Víctor Hugo Castrejón Sanchez, "Synthesis and Characterization of CN Thin Films Produced by DC-Pulsed Sputtering in an \(CH_3CH_2OH-N_2\) Atmosphere", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 1, pp. 53–59, 2022. doi: 10.25046/aj070106
15. Sepideh Amirpour, Torbjörn Thiringer, Yasin Sharifi, Marco Majid Kabiri Samani, "Improving of Heat Spreading in a SiC Propulsion Inverter using Graphene Assembled Films", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 6, pp. 98–111, 2021. doi: 10.25046/aj060614
16. Seok-Jun Bu, Hae-Jung Kim, "Ensemble Learning of Deep URL Features based on Convolutional Neural Network for Phishing Attack Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 291–296, 2021. doi: 10.25046/aj060532
17. Natasia, Sani Muhamad Isa, "Cyberbullying Detection by Including Emotion Model using Stacking Ensemble Method", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 229–236, 2021. doi: 10.25046/aj060525
18. Mabrouki Mariem, Gharsallah Ali, "Designs of Frequency Reconfigurable Planar Bow-tie Antenna Integrated with PIN, varactor diodes and Parasitic Elements", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 320–326, 2021. doi: 10.25046/aj060435
19. Jorge Joo-Nagata, Fernando Martínez-Abad, "Evaluation of Information Competencies in the School Setting in Santiago de Chile", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 298–305, 2021. doi: 10.25046/aj060433
20. Montaño-Arango Oscar, Ortega-Reyes Antonio Oswaldo, Corona-Armenta José Ramón, Rivera-Gómez Héctor, Martínez-Muñoz Enrique, Robles-Acosta Carlos, "Multidisciplinary Systemic Methodology, for the Development of Middle-sized Cities. Case: Metropolitan Zone of Pachuca, Mexico", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 80–90, 2021. doi: 10.25046/aj060410
21. Mark Gourary, Sergey Rusakov, "Technique to Simulate an Oscillator Ensemble Represented by the Kuramoto Model", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 311–318, 2021. doi: 10.25046/aj060335
22. Natalia Yevtushenko, Nataliia Kuzminska, Tetiana Kovalova, "Dependence of the Knowledge Structure of the Company Employees on a Set of the Competencies", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 699–708, 2021. doi: 10.25046/aj060281
23. Khadija Alaoui, Mohamed Bahaj, "Categorization of RDF Data Management Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 221–233, 2021. doi: 10.25046/aj060225
24. Rajani Narayan, Anjanappa Sreenivasa Murthy, "Texture Based Image Retrieval Using Semivariogram and Various Distance Measures", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1369–1377, 2021. doi: 10.25046/aj0601156
25. Samuel Batambock, Ndoh Mbue Innocent, Dieudonné Bitondo, Augusto Francisco Nguemtue Waffo, "Auditing the Siting of Petrol Stations in the City of Douala, Cameroon: Do they Fulfil the Necessary Regulatory Requirements?", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 493–500, 2021. doi: 10.25046/aj060154
26. Dionisius Saviordo Thenuardi, Benfano Soewito, "Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 242–249, 2021. doi: 10.25046/aj060127
27. Maritza Cabana-Caceres, Cristian Castro-Vargas, Laberiano Andrade-Arenas, Monica Romero-Valencia, Haydee Castro-Vargas, "Learning strategies and Academic Goals to Strengthen Competencies in Electronics and Digital Circuits in Engineering Students", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 87–98, 2021. doi: 10.25046/aj060110
28. Yaswanthkumar S K, Keerthana M, Vishnu Prasath M S, "A Machine Vision Approach for Underwater Remote Operated Vehicle to Detect Drowning Humans", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1734–1740, 2020. doi: 10.25046/aj0506207
29. Jinwon Cheon, Sunwoong Choi, "Hand Gesture Classification using Inaudible Sound with Ensemble Method", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 967–971, 2020. doi: 10.25046/aj0506115
30. Ismail Ktata, Naoufel Kharroubi, "A Model-Driven Approach for Reconfigurable Systems Development", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 801–810, 2020. doi: 10.25046/aj050695
31. Alan Guadalupe Ochoa Navarro, Juan De Dios Cota Apodaca, Dario Fuentes Guevara, "Strategic Plan for the Achievement of the Competitiveness of Small Companies with Respect to Large Ones", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 584–587, 2020. doi: 10.25046/aj050671
32. Felix Fernandez-Pena, Alex Maigua-Quinteros, Pilar Urrutia-Urrutia, Diana Coello-Fiallos, "Ontology-based Data Management Tool for Studying Radon Concentration", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 577–583, 2020. doi: 10.25046/aj050670
33. Mikhail Vladimirovich Vartanov, Trung Ta Tran, Van Dung Nguyen, "Research of the Effect of Rotation and Low-Frequency Vibration on the Robotic Assembly Process", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 160–168, 2020. doi: 10.25046/aj050619
34. Youssef El Alaoui, Larbi Alaoui, "Qualitative Properties of a Cell Proliferating Model with Multi-phase Transition and Age Structure", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 01–08, 2020. doi: 10.25046/aj050601
35. Mamoun Abu Helou, "Evaluating the Impact of Semantic Gaps on Estimating the Similarity using Arabic Wordnet", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1315–1328, 2020. doi: 10.25046/aj0505158
36. Luluk Wulandari, Yuniar Farida, Aris Fanani, Nurissaidah Ulinnuha, Putroue Keumala Intan, "Evaluation of Disadvantaged Regions in East Java Based-on the 33 Indicators of the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration Using the Ensemble ROCK (Robust Clustering Using Link) Method", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 193–200, 2020. doi: 10.25046/aj050524
37. Khalid Chkara, Hamid Seghiouer, "Criteria to Implement a Supervision System in the Petroleum Industry: A Case Study in a Terminal Storage Facility", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 29–38, 2020. doi: 10.25046/aj050505
38. Zakaria Mighouar, Laidi Zahiri, Hamza Khatib, Khalifa Mansouri, "Damage Accumulation Model for Cracked Pipes Subjected to Water Hammer", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 523–530, 2020. doi: 10.25046/aj050462
39. Bulat Kubekov, Anar Utegenova, Leonid Bobrov, Vitaliy Naumenko, Aibek Ibraimkulov, "Ontologic Design of Software Engineering Knowledge Area Knowledge Components", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 30–34, 2020. doi: 10.25046/aj050404
40. Redouane Kanazy, Samir Chafik, Eric Niel, "Prognosis of Failure Events Based on Labeled Temporal Petri Nets", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 432–441, 2020. doi: 10.25046/aj050354
41. Rabeb Faleh, Souhir Bedoui, Abdennaceur Kachouri, "Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 739–747, 2020. doi: 10.25046/aj050292
42. Suharjito, Atria Dika Puspita, "Face Recognition on Low Resolution Face Image With TBE-CNN Architecture", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 730–738, 2020. doi: 10.25046/aj050291
43. Ola Surakhi, Sami Serhan, Imad Salah, "On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 512–526, 2020. doi: 10.25046/aj050265
44. Marwa Daghari, Hedi Sakli, "Antenna Radiation Performance Enhancement Using Metamaterial Filter for Vehicle to Vehicle Communications Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 344–350, 2020. doi: 10.25046/aj050245
45. ?ahin Aydin, Mehmet Nafiz Aydin, "A Sustainable Multi-layered Open Data Processing Model for Agriculture: IoT Based Case Study Using Semantic Web for Hazelnut Fields", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 309–319, 2020. doi: 10.25046/aj050241
46. Jorge Castro-Bedriñana, Doris Chirinos-Peinado, Felipe Zenteno-Vigo, Gianfranco Castro-Chirinos, "Satisfaction of Old Graduates of Zootechnical Engineering for Improvement of Educational Quality at the UNCP", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 166–173, 2020. doi: 10.25046/aj050221
47. Bzar Khidir Hussan, "Comparative Study of Semantic and Keyword Based Search Engines", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 106–111, 2020. doi: 10.25046/aj050114
48. El Mustapha Iftissane, Moulay Driss Belrhiti, Seddik Bri, Jaouad Foshi, Nawfal Jebbor, "Design and Analysis of Frequency Reconfigurable Antenna Embedding Varactor Diodes", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 371–376, 2019. doi: 10.25046/aj040647
49. Evan Hurwitz, Chigozie Orji, "Multi Biometric Thermal Face Recognition Using FWT and LDA Feature Extraction Methods with RBM DBN and FFNN Classifier Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 67–90, 2019. doi: 10.25046/aj040609
50. Olena Basalkevych, Olexandr Basalkevych, "Fuzzy Simulation of Historical Associative Thesaurus", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 224–233, 2019. doi: 10.25046/aj040528
51. Omar Freddy Chamorro Atalaya, Teodoro Neri Díaz Leyva, Dora Yvonne Arce Santillan, Jorge Isaac Castro Bedriñana, Denisse Marie Barrientos Pichilingue, Avid Roman-Gonzalez, "Satisfaction of the Graduate for the Continuous Improvement of Educational Quality in UNTELS", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 151–157, 2019. doi: 10.25046/aj040520
52. Rowaida Khalil Ibrahim, Subhi Rafeeq Mohammed Zeebaree, Karwan Fahmi Sami Jacksi, "Survey on Semantic Similarity Based on Document Clustering", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 115–122, 2019. doi: 10.25046/aj040515
53. Nadia Mountaj, El-Mehdi Hamzaoui, Mohamed Majid Himmi, Mireille Besson, "Development of Wavelet-Based Tools for Event Related Potentials’ N400 Detection: Application to Visual and Auditory Vowelling and Semantic Priming in Arabic Language", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 414–420, 2019. doi: 10.25046/aj040450
54. Anatolii Petrenko, Bogdan Bulakh, "Automatic Service Orchestration for e-Health Application", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 244–250, 2019. doi: 10.25046/aj040430
55. Anna Tatsiopoulou, Christos Tatsiopoulos, Basillis Boutsinas, "Providing Underlying Process Mining in Gamified Applications – An Intelligent Knowledge Tool for Analyzing Game Player’s Actions", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 212–220, 2019. doi: 10.25046/aj040426
56. Nindhia Hutagaol, Suharjito, "Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 206–211, 2019. doi: 10.25046/aj040425
57. Windra Priatna Humang, Sigit Pranowo Hadiwardoyo, Nahry, "Factors Influencing the Integration of Freight Distribution Networks in the Indonesian Archipelago: A Structural Equation Modeling Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 278–286, 2019. doi: 10.25046/aj040335
58. Youssef Rhazi, Outman El Bakkali, Youssef El merabet, Mustpaha Ait lafkih, Seddik Bri, Mohamed Nabil Srifi, "Novel Design of Multiband Microstrip Patch Antenna for Wireless Communication", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 63–68, 2019. doi: 10.25046/aj040310
59. Ghanmi Helmi, Khenchaf Ali, Pouliguen Philippe, Leye Papa Oussmane, "Experimental Results and Numerical Simulation of the Target RCS using Gaussian Beam Summation Method", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 1–6, 2018. doi: 10.25046/aj030301
60. Daniel Ekpenyong Asuquo, Patience Usoro Usip, "Ontology Modeling of Social Roles of Users in Mobile Computing Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 319–328, 2018. doi: 10.25046/aj030235
61. Touria Karite, Ali Boutoulout, "Boundary gradient exact enlarged controllability of semilinear parabolic problems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 5, pp. 167–172, 2017. doi: 10.25046/aj020524
62. Akihiro Yamada, Susumu Ishihara, "Data Exchange Strategies for Aggregating Geographical Distribution of Demands for Location-Dependent Information Using Soft-State Sketches in VANETs", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 175–185, 2017. doi: 10.25046/aj020622
63. Nacer E. El Kadri E, Abdelhakim Chillali, "Petrov-Galerkin formulation for compressible Euler and Navier-Stokes equations", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 5, pp. 63–69, 2017. doi: 10.25046/aj020511
64. Badis Hammi, Houda Labiod, Gérard Segarra, Alain Servel, Jean Philippe Monteuuis, "CAM-Infrastructure: a novel solution for service advertisement in Cooperative Intelligent Transportation Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1422–1431, 2017. doi: 10.25046/aj0203178
65. Herman Jair Gómez Palacios, Robinson Andrés Jiménez Toledo, Giovanni Albeiro Hernández Pantoja, Álvaro Alexander Martínez Navarro, "A comparative between CRISP-DM and SEMMA through the construction of a MODIS repository for studies of land use and cover change", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 598–604, 2017. doi: 10.25046/aj020376
66. Carlos Guevara, Jose Aguilar, Alexandra González-Eras, "The Model of Adaptive Learning Objects for virtual environments instanced by the competencies", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 345–355, 2017. doi: 10.25046/aj020344
67. Alexander Nuñez, Arturo Sanchez, "Design of Petri Net Supervisor with 1-monitor place for a Class of Behavioral Constraints", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 4, pp. 32–43, 2017. doi: 10.25046/aj020405
68. Lubna s. Ben Taher, "Evaluation of Three Evaporation Estimation Techniques In A Semi-Arid Region (Omar El Mukhtar Reservoir Sluge, Libya- As a case Study)", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 2, pp. 19–29, 2017. doi: 10.25046/aj020204
69. Lucila Romero, Milagros Gutierrez, Laura Caliusco, "Semantic modeling of portfolio assessment in e-learning environment", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 149–156, 2017. doi: 10.25046/aj020117