Engineering

Abstract: Australia is a signatory to the Paris Agreement, a legally binding international treaty on climate change adopted in December 2015. The accord culminated from two weeks of intensive multilateral negotiations at the United Nations Climate Change Conference (COP21). While endorsed by 194 UN member states alongside the European Union, only Iran, Libya, and Yemen refrained from signing. Although the United States was an initial signatory, it remains the sole nation to have formally exited the pact via an executive order issued by President Trump in January 2025. The primary objective of the Paris Agreement is to mitigate greenhouse gas (GHG) emission to stabilize global temperature increases well below 2 °C relative to pre-industrial baselines. Ultimately, the treaty aims to achieve global net-zero emissions by mid-century to restrict the temperature rise to 1.5 °C. To ensure compliance, the Agreement establishes a robust Transparency Framework for tracking, reviewing, and reporting the climate actions undertaken by signatory nations. Furthermore, developed economies committed to mobilizing a dedicated climate finance fund to assist developing nations in executing their respective mitigation and adaptation strategies (Framework Convention on Climate Change, 2015). To curb GHG emissions, most states in Australia have heavily prioritized the large-scale deployment of residential rooftop photovoltaic (PV) systems. Approximately 31% to 32% of homes in the State of Victoria have implemented rooftop solar panel generation. Official data from Solar Victoria and the Australian Photovoltaic Institute indicates that PV penetration in Victoria has reached this mark, reflecting more than 850,000 rooftop solar systems installed across the state (Solar Victoria, 2026a). This paper examines the performance characteristics of these installations and develops a predictive model to assess their future generation capacity.

Abstract: Organic solar cells provide fascinating solutions for sustainable energy conversion. However, inefficient hole transport layers limit their performance by restricting charge extraction and facilitating interfacial charge recombination. This study provides a comprehensive simulation-based performance analysis of organic photovoltaic cells using PbS, PbSe, and PbS-TBAI quantum dots as alternative HTLs in the AZO/PM6:Y6/QD solar cell architecture. We used numerical simulations to carefully evaluate the effect of QD type, thickness, doping concentration, and defect density on photovoltaic parameters. PbS QDs-based devices surpass PbSe-based (21.23%) and PbS (QD)-TBAI-based devices (19.71%) due to their greater intrinsic material proper-ties, superior interfacial chemistry, better tolerance to defects and thermal stress, and perfect band alignment with the PM6:Y6 active layer, resulting in a notable power conversion efficiency of 22.19%. The PbS-based devices displayed acceptable thermal stability and trap tolerance, keeping 92.5% of their original PCE even at high defect densities (~10²⁰ cm^-3). A thorough band alignment study demonstrated that flat-band conditions at the PbS/PM6:Y6 interface reduce non-radiative recombination, but the insulating TBAI ligand layer in PbS-TBAI devices creates tunnelling barriers and Fer-mi-level pinning. This study presents essential design concepts for choosing and de-veloping QD-based HTLs, establishing PbS QDs as the best choice for high-efficiency, stable, solution-processed OPV cells, with important implications for next-generation photovoltaic technologies.

Abstract: Ocean waves are abundant but low-frequency (0.1–1 Hz) and irregular; electromagnetic generators (EMGs) deliver high current yet weaken at low velocity, whereas triboelectric nanogenerators (TENGs) sustain high voltage at microampere-level current through megohm-level internal impedance. A sea-state-adaptive hybrid harvester in a compact 3D-printed decagon buoy is proposed, where calm-sea rocking drives a freestanding sliding-mode TENG of polytetrafluoroethylene (PTFE) plates over aluminum electrodes, and rough-sea heave drives the EMG's magnetically levitated NdFeB magnet assembly through copper coils. At 0.5 Hz and ±45°, a single TENG layer with two PTFE plates sliding in the same direction produced a screen-measured ≈15.36 V peak-to-peak open-circuit voltage and 1.25 μW peak power at an optimum load of approximately 5 MΩ. The EMG delivered approximately 1.488 mW under horizontal rocking at a 10 Ω optimum load, near its 16 Ω coil resistance, and charged a 1000 μF capacitor to 2.693 V (≈3.6261 mJ) within 120 s of heave. A preliminary hybrid test, applying both motions simultaneously and combining the separately rectified outputs at the same capacitor, raised stored energy to 5.278 mJ, about 45.56% more than the EMG alone stored, confirming the rectify-then-combine synergy. These complementary electrical and sea-state characteristics support future self-powered marine monitoring buoys.

Abstract: Objective and reproducible characterization of visual behavior under ecologically valid conditions remains a methodological challenge in precision sports. This pilot study presents a multimodal engineering framework that integrates progressive virtual reality (VR) visual training, standardized clinical assessment, wearable binocular eye tracking, inertial measurement unit (IMU) sensing, and fully automated Quiet Eye (QE) extraction. Eight competitive archers were recruited; six completed the full protocol. Participants underwent a progressive three-session VR training program targeting smooth pursuit, fusional vergence, and eye–hand coordination, delivered with the Eyesoft EMAA Pro 2 platform. Clinical visual function was assessed before and after the intervention. Following training, gaze behavior during standardized blank-bale shooting was recorded with the Pupil Labs Neon head-mounted binocular eye tracker synchronized with its onboard 9-DoF IMU. An automated analysis pipeline combined fixation detection, IMU-based release detection, and acoustic verification of bowstring release to extract QE duration with minimal observer intervention. The short intervention was associated with a significant increase in the number of successful visuomotor responses (TSM; p = 0.026) and a significant increase in the slowest reaction time (LSM; p = 0.028). Accommodative, binocular, and dynamic visual acuity measures showed no statistically significant changes. Automated analysis revealed substantial inter-individual variability in QE duration (median range 155–4975 ms) together with relatively homogeneous fixation, saccade, and pupil metrics. The principal contribution of this work is a complete, largely automated workflow that enables objective and reproducible assessment of gaze behavior during archery performance under ecologically valid conditions. The framework provides a technical platform for future longitudinal studies, individualized visual training systems, and potential real-time biofeedback applications in precision sports.

Abstract: In this article, a penetration detection technology for small hole machining in electrical discharge machining (EDM) based on Backpropagation (BP) neural network classification algorithm is proposed to improve the machining efficiency of gas film holes in aviation turbine discs. During the penetration period of small hole machining, due to the drastic variations in machining voltage, five parameters, including the number of machining voltage variations(p), pulse width(ON_all), pulse interval(OFF), servo distance(SV), and machining current(PS), are determined as input variables for the BP model features. Based on the above analysis, using the data obtained from model training, the BP neural network classification algorithm model was established in MATLAB. As a result, a small hole machining penetration detection model was obtained in a field programmable gate array (FPGA). At the same time, considering factors such as FPGA on-chip resources and algorithm time consumption, the FPGA program was transplanted on the experimental platform through table lookup, and the feasibility was verified through machining experiments on this method.

Abstract: Variations in grid inductance shift the resonance characteristics of LCL filters and can reduce the stability margins of digitally controlled grid-connected inverters. This paper proposes a hybrid active-damping strategy that combines grid-current feedback for current regulation, inverter-current feedback for active damping, and capacitor-voltage feedforward to modify the closed-loop dynamics. An open-loop model incorporating the LCL network, equivalent grid inductance, and digital control delay is derived to evaluate the resonance behavior and small-signal stability of the system. Frequency-domain analysis is performed using a fixed controller parameter set, and comparative simulations are used to assess the contribution of the auxiliary control paths. The strategy is further evaluated through real-time hardware-in-the-loop tests under representative grid-inductance conditions. The frequency-domain results show suppressed resonance-related gain amplification and positive phase margins at all evaluated grid-inductance values. The HIL results show bounded and periodic three-phase grid currents without resonance-induced oscillation. The individual harmonic components from the second to the fortieth order remain below 1% of the fundamental component, and the grid-current total harmonic distortion remains below 1.2% in all evaluated cases. These results show effective resonance damping and consistent grid-current quality under grid-inductance variation.

Abstract: Through-silicon-carbide vias (TSiCVs) is a key technology for three-dimensional (3D) integration of silicon carbide (SiC) integrated circuits intended for extreme-environment applications such as Venus surface exploration. This work presents an expanded full-wave electromagnetic analysis using a Signal-Ground-Signal (SGS) configuration to evaluate the shielding effectiveness and signal integrity required for high-density 3D SiC packaging based on our previous study of Signal-Ground (SG) pairs. Parametric evaluation of Through-Silicon-Carbide Vias (TSiCVs) in a Signal-Ground-Signal (SGS) differential configuration operating across temperatures from 20°C to 600°C and with a frequency range of 1 GHz to 50 GHz and a systematic sweep of via radius (R=5 µm to 25 µm) is performed. Two distinct geometric scaling methodologies are evaluated for via radii with fixed pitch (P=52 µm) and proportional pitch scaling(P/D=2). Results demonstrate that physical geometry—specifically edge-to-edge spacing (S) dictates capacitive coupling and characteristic impedance matching (Z_D11), whereas elevated temperature primarily drives ohmic attenuation (S_D21) through enhanced conductor resistivity and skin-depth limitations. For the simulated 52 µm-pitch SGS channel, a via radius of R=10 µm provides the optimal wideband signal-integrity performance, achieving an optimal differential return loss (S_D11) of -32.35 dB at 20°C and -39.09 dB at 600°C, and minimal insertion loss (S_D21) of -0.045 dB at 50 GHz. Common-to-differential mode conversion (S_CD21) remains suppressed below -64 dB across all temperature and geometric variations. These insights establish critical physical design trade-offs for high-density, extreme-environment 3D integrated circuits.

Abstract: FPGA-based neural network accelerators require careful balancing of numerical precision, hardware resource utilization, latency, throughput, and power consumption to satisfy the diverse requirements of edge artificial intelligence applications. This paper presents a configurable VHDL-based FPGA accelerator for multilayer perceptron (MLP) inference together with a systematic design-space exploration of these trade-offs. The proposed architecture features parameterized fixed-point arithmetic with saturation and half-up rounding, configurable activation functions, streaming data transfer with back-pressure support, and interchangeable serial and parallel processing-element (PE) architectures that enable scalable performance and resource utilization from a unified RTL framework. Three fixed-point formats (Q8.8, Q12.12, and Q16.16) and three of the five supported activation functions (ReLU, Sigmoid, and Hard-Sigmoid) were synthesized and evaluated, yielding 18 hardware configurations synthesized and implemented under identical conditions on an UltraScale+ FPGA. Functional correctness, numerical accuracy, latency, and streaming behavior were verified using a MATLAB–VHDL co-simulation framework with one million inference samples. Experimental results show that the serial architecture minimizes hardware cost, requiring only 3–12 DSP slices and 0.035–0.090 W of dynamic power. The serial architecture incurs an input-to-output latency of 284 clock cycles per inference, with an initiation interval (II) of 285 cycles, the one-cycle difference reflecting the control overhead required for pipeline re-initialization between consecutive inferences. In contrast, the parallel architecture reduces inference latency to 13 clock cycles and achieves an II of 1, confirming full pipeline utilization and achieving throughputs that exceed 100 MSps for the Q12.12 and Q16.16 precision levels (though the Q8.8 variants operate at ~58 MSps), albeit at the expense of substantially higher resource utilization. Among the evaluated configurations, the Q12.12 parallel implementation provides the best overall trade-off, delivering near-floating-point accuracy (RMSE = 3.8 × 10^-4 and maximum absolute error < 1.2 × 10^-3 relative to a double-precision (float64) reference model), operating frequencies of approximately 103–110 MHz, and dynamic power between 0.407 and 0.446 W. Increasing precision to Q16.16 yields negligible accuracy improvement while significantly increasing DSP, LUT, and power consumption. Furthermore, the study identifies an unexpected timing bottleneck in Q8.8 parallel implementations, where control-logic fan-out, rather than arithmetic complexity, limits the achievable operating frequency. These results provide practical design guidelines for selecting FPGA accelerator configurations according to application-specific accuracy, latency, throughput, power, and resource constraints.

Abstract: Accurate and reliable automated medical image classification is important for computer-aided clinical decision support. However, clinical data are often distributed across institutions and cannot be centrally shared because of privacy, governance, and regulatory constraints. Federated Learning (FL) enables collaborative training while keeping data local, but statistical heterogeneity may cause client drift and performance degradation. We propose FedPBN, a personalized FL approach combining client-specific Batch Normalization (BN) with FedProx-based proximal regularization. BN parameters and running statistics remain local, while non-BN parameters are optimized and aggregated according to client data sizes. FedPBN was evaluated on five clients using PathMNIST and BloodMNIST under IID, Dirichlet (α = 0.5), and pathological Label-Skew distributions with ResNet18, DenseNet121, and MobileNetV2, against FedAvg, FedProx, and FedBN. Personalization and generalization were examined using PFPV, GRV, and CPAV. Results show that preserving client-specific BN is critical under severe Label-Skew, whereas GRV can degrade performance; CPAV recovers much of this loss but requires centralized labeled data. MobileNetV2-FedPBN achieved 97.88% accuracy and 96.79% Macro F1 on PathMNIST. Jetson Orin Nano Super deployment further demonstrated practical hospital-side edge inference, with MobileNetV2 offering the best efficiency–performance balance and DenseNet121 the strongest BloodMNIST Client-0 accuracy.

Abstract: Flexible IC technologies are useful in low-cost applications such as biomedical, wearable sensors and product tracking. This paper presents the design of a fully-differential high gain op amp based on the Pragmatic 600 nm FlexIC thin-film transistor (TFT) technology. This technology allows solely for the implementation of n-type FETs, resistors and capacitors. The proposed op amp implements novel gate-bootstrapping technique at the output stage pull-up and shunt-shunt feedback n-channel FETs resulting in an increased output voltage swing even when sourcing significant output current. The op amp is intended for applications processing periodic signals such as in fast cyclic voltammetry, thus ensuring the repeated charging of the bootstrapping capacitor. It has an open loop gain of 94 dB and a differential output swing of ±3.5 V when operated at a supply voltage of 5 V. Although here a fully-differential op amp is described, the design can be adapted for single-ended applications by removing one of the output stages and the common mode feedback correction circuitry.

Abstract: This paper introduces a unified framework for angular super-resolution in antenna arrays. By integrating linear prediction beyond the physical aperture, entropy variations, and harmonic spatial spectra averaging, we recast classical methods—including Capon, Pisarenko, Maximum Entropy, and MUSIC—into a single structure. We extend this framework to accommodate ESPRIT-type methods by treating the number of simultaneously predicted subarray channels as an additional dimension. Furthermore, we adapt this approach to non-Gaussian signals, establishing a parallel framework for Virtual-ESPRIT-type methods. As a practical application, we propose universal, J orthogonalization-based algorithms that estimate spatial super-resolution spectra within a unified computational architecture. This architecture produces spatial spectrum estimates for multiple super-resolution methods and any intermediate variations.

Abstract: State-plane analysis provides an intuitive geometric description of resonant converters, but the relationship between trajectory geometry and practical operating quantities remains insufficiently established. This paper presents a common state-plane framework for LLC and general CLLC converters. Based on established state-plane relationships, a forward mapping relates operating conditions to the characteristic trajectory radius, while an inverse mapping reconstructs the switching frequency from trajectory information. Simulation results show close agreement between analytical and simulated trajectory radii and validate the inverse reconstruction. Overall, the proposed framework establishes a common analytical connection between operating conditions and trajectory geometry in both forward and inverse directions.

Abstract: Triboelectric nanogenerators (TENGs) have been extensively investigated as both energy harvesters and self-powered sensors. While their application as force sensors has been widely reported, the use of TENGs as stretch sensors remains relatively unexplored. Stretch sensors are important for monitoring human joint and limb movements, with significant potential in rehabilitation applications such as post-stroke therapy and recovery following knee surgery. In this work, we present a preliminary TENG-based stretch sensor comprising a multi-triangular elastomer structure and an arched copper electrode. The device generates an output voltage that is proportional to the bending angle. This behavior arises from the increased contact and friction between the elastomer (Dragon Skin silicone) and copper layers as deformation increases, resulting in enhanced triboelectric charge generation and a higher output voltage. Owing to the inherent capacitive characteristics of the TENG under high-impedance loading conditions, the generated voltage can be retained for several seconds after the bending motion ceases. This retention time is sufficient for signal acquisition and joint-motion detection electronics. Experimental results demonstrate that the proposed TENG-based stretch sensor can reliably measure bending angles under both quasi-static and dynamic operating conditions, highlighting its potential for wearable motion-monitoring and rehabilitation systems.

Abstract: This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32/Wokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.

Abstract: This work presents the functional enhancement of a previously developed miniature robot actuated by 3D-printed piezoelectric resonators through the integration of environmental and spatial sensors and the experimental comparison of proportional–derivative (PD) and sliding mode control (SMC) control strategies. The platform integrates an inertial measurement unit, a time-of-flight distance sensor, and a total volatile organic compound (TVOC) sensor, enabling the robot to execute a predefined route while acquiring environmental and spatial information. Two control strategies, PD and SMC, were experimentally compared using yaw-angle feedback and differential adjustment of the excitation frequencies. For a 0° reference, the PD and SMC controllers achieved mean absolute errors of 0.515° and 0.652°, respectively. During a transition from 0° to 90°, both controllers exhibited similar transient responses; however, during the return transition from 90° to 0°, only the PD controller successfully completed the rotation. In addition, the PD controller achieved a 12.27% higher linear velocity and a 5.44% higher counterclockwise angular velocity. The VL53L0X sensor exhibited a mean absolute error of 0.90 cm and a mean percentage error of 8.22%, while the SGP30 successfully detected the increase in TVOC caused by isopropyl alcohol, with relative differences ranging from 5.30% to 12.94% compared with a low-cost commercial air-quality monitor.

Abstract: The increasing penetration of distributed energy resources (DERs) and new electric loads associated with decarbonization is pushing Distribution System Operators (DSOs) towards more proactive management of Medium Voltage (MV) and Low Voltage (LV) networks. In this context, short-term load forecasting (STLF) at the secondary substation (SS) level is becoming increasingly relevant for network operation and planning. However, conventional approaches typically use exclusively the data at SS level. without exploiting information available across the distribution network. This paper proposes a data-driven hierarchical forecasting framework that combines substation level predictions with aggregated forecasts of the underlying connection points. SS are characterized according to their operating conditions, based on the balance between annually consumed and produced energy, to investigate how these affect predictability. Forecasts from one to five days ahead are obtained using horizon-specific Random Forest (RF) models combining autoregressive, meteorological, and calendar information. Minimum Trace (MinT) reconciliation then ensures coherence between substation level and connection point forecasts. The analysis reveals markedly different forecasting behavior across operating conditions, with consumption-dominated substations proving considerably more predictable than their generation-dominated counterparts. Hierarchical reconciliation follows the same pattern, delivering its most consistent gains for passive substations, with an average day-ahead MAE reduction of 3.4%, reaching up to 6.4%, while its benefit gradually fades as local generation grows. The results provide DSOs with practical indications for adapting forecasting and reconciliation strategies across heterogeneous distribution networks.

Abstract: This article presents MIRAI-EDU-D, a software-free adaptive educational micro-robot designed to support tangible formative assessment in STEAM activities for primary and secondary education. The proposal addresses two current tensions: the expansion of educational robotics as a resource for active learning and computational thinking, and the need for school technologies that are transparent, repairable, inclusive and respectful of children’s privacy. Using an educational-technological design methodology, the system is specified through a discrete-electronics architecture composed of reflectance sensors, LM339 comparators, NE555 timers, CD4510 CMOS up/down counters, CD4028/CD4511 decoders and CD4000 logic gates. The robot reads binary cards placed by students, updates a physical memory of conceptual mastery and activates differentiated feedback: reinforcement, hint, remediation, advanced challenge or teacher alert. The design results show the alignment between electronic operation, formative assessment principles and STEAM competences by integrating science, technology, engineering, arts and mathematics in a manipulable learning experience. A validation protocol is also proposed, including pretest/posttest measures, observation rubrics, technical logs and teacher acceptance analysis. MIRAI-EDU-D is framed as a low-cost, auditable and privacy-preserving educational robotics alternative for schools with limited infrastructure, although classroom effectiveness must be tested through controlled pilot studies before making empirical claims about learning impact and long-term transfer outcomes.

Abstract: Chiplet integration enables designers to assemble dies from multiple vendors and connect them through standardized interconnect protocols, such as universal chiplet interconnect express (UCIe). This multi-vendor model, however, redraws the trust boundary and exposes inter-chiplet links to hardware Trojan insertion. Yet this attack surface remains largely unexplored, especially in FPGA-based chiplet ecosystems. In this work, we present the first hardware Trojan that stealthily bypasses the cyclic redundancy check (CRC)-based integrity mechanism of the UCIe protocol. The Trojan deliberately flips data bits in ways that preserve the original checksum, allowing corrupted inter-chiplet traffic to pass CRC validation undetected. We model the Trojan in a multi-die FPGA AI inference engine and show that these evasive corruptions can induce targeted misclassification, including (i) input-class suppression, (ii) forced-class promotion, and (iii) conditional class redirection. The Trojan incurs an overhead ranging from 18–35 and flip-flops (FFs) ranging from 14–17 when implemented on a Kintex-7 FPGA. Our results expose a critical gap in UCIe’s integrity mechanism. Although CRC remains effective for random error detection, its linear structure enables protocol-aware Trojans to inject checksum-preserving corruptions. The integrity gap identified in our work motivates the need to move beyond error-detection codes to secure UCIe-based chiplet interconnects.

Abstract: Cadmium telluride (CdTe) is a direct-bandgap semiconductor with strong optical absorption and is therefore well suited to thin-film photovoltaic conversion. This study brings together the AFORS-HET modeling work on CdS/CdTe devices, the figures and material-parameter table, and supporting literature supplied with the project. The analysis focuses on three closely connected limitations: formation of a non-ohmic Schottky barrier at the metal back contact, use of a highly doped electron-reflector (ER) region to modify carrier transport near that contact, and surface recombination velocity as an effective model for pinhole- or defect-related losses. The simulations indicate that back-contact and interface conditions influence open-circuit voltage much more strongly than short-circuit current density. Optimized modeled structure using an ER, a doping concentration of 7 × 1018 cm^-3, an ER thickness of 100 nm, and an effective barrier height of approximately 0.1 eV reaches a simulated efficiency of 19.83%, with Voc = 917.6 mV and Jsc = 28.45 mA/cm². The surface-recombination study further shows that severe pinhole conditions can lower Voc to approximately 0.73 V. These results are interpreted together with literature on CdTe doping, interface engineering, minority-carrier lifetime, grain-boundary recombination, and Cu-related back-contact effects [1-7]. The combined picture emphasizes that high optical absorption alone is not sufficient: contact selectivity, interface quality, carrier lifetime, and defect control must be optimized simultaneously.

Abstract: This article presents the results of measuring the permittivity of a powder material. The measurements were performed using a resonance method. The method is based on a volumetric strip-slot. A computer model was developed to demonstrate the fundamental feasibility of using such a device for permittivity measurements. Calculations were made to determine the optimal number of measurements to achieve the specified accuracy. Confidence intervals for the measured parameters were obtained.