Search Results (122)

Cadmium-free buffer layers are pivotal for the sustainable development of thin-film photovoltaics. This work numerically investigates SnS₂ as a high-performance, environmentally benign alternative to CdS for antimony selenosulfide (Sb₂(S,Se)₃) solar cells using AFORS-HET software. The SnS₂/Sb₂(S,Se)₃ heterojunction exhibits a significantly lower conduction band offset (CBO ≈ 0.23 eV) than its CdS counterpart (CBO ≈ 0.49 eV), which is identified as the primary factor for suppressed interface recombination and enhanced electron injection efficiency. A comprehensive optimization strategy is presented: tuning the S content in Sb₂(S,Se)₃ to 40% optimizes the trade-off between band gap widening and hole transport barrier at the ETL/absorber interface; adjusting the absorber thickness to 340 nm balances light absorption and carrier collection efficiency; and elevating the SnS₂ carrier concentration to 10²¹ cm⁻³ strengthens the built-in potential and induces a beneficial hole-blocking “spike” at the front contact. The synergistically optimized device achieves a power conversion efficiency (PCE) of 10.39%, a substantial improvement over the 7.56% efficiency of the CdS-based reference cell in our simulation framework. Full article

Narrowband Internet of Things (NB-IoT) was designed as a key Low-Power Wide-Area Network technology when 5G networks were established. The ideal quadrature demodulation in NB-IoT relies on the fundamental symmetry between the in-phase (I) and quadrature (Q) branches, characterized by a perfect 90-degree phase shift and matched amplitude. However, practical hardware imperfections in mixers, filters, and ADCs break this symmetry, leading to I/Q imbalances. Moreover, I/Q imbalance is coupled with carrier frequency offset (CFO), which arises from asymmetry in the frequency of the transceiver oscillator. In this paper, we propose a model-embedded lightweight network for joint CFO and I/Q imbalance estimation for NB-IoT systems. An I/Q imbalance compensation model is embedded as a layer to connect two subnetworks, I/Q estimation network (IQENET) and CFO estimation network (CFOENET). By embedding the physical model, the network gains the capability to learn the features of coupling effects during the training process, as the image signals caused by I/Q imbalance are removed before CFO estimation. A phased training strategy is also proposed. In the first phase, the two subnetworks are pre-trained independently. In the second phase, they are fine-tuned jointly to deal with the coupling effects. Simulation results show that the proposed network achieves high estimation accuracy while maintaining low complexity. Full article

The rapid development of the Internet of Things (IoT) has imposed increasingly stringent power consumption requirements on receiver design. Unlike phase-locked loops (PLLs), free-running oscillators eliminate power-hungry loop circuitry. However, the inherent frequency offset of free-running oscillators introduces uncertainty in the intermediate frequency (IF), preventing the receiver from aligning with the desired channel. To address this, we present a reconfigurable channel receiver employing a free-running oscillator and frequency estimation for low-power IoT applications. The proposed receiver first captures a specific preamble sequence corresponding to the desired channel through multiple parallel sub-channels implemented in the digital baseband (DBB), which collectively cover the expected IF frequency range. The desired IF frequency is estimated using the proposed preamble-based frequency estimation (PBFE) algorithm. After frequency estimation, the receiver switches to a single-channel mode and tunes its passband center frequency to the estimated IF frequency, enabling high-sensitivity data reception. Measurement results demonstrate that the PBFE algorithm achieves reliable frequency estimation with a minimum IF signal-to-noise ratio (SNR) of 2 dB and an estimation error below 22 kHz. In single-channel mode, with a residual frequency offset of 30 kHz, an 8-point energy accumulation decoding scheme achieves a bit error rate (BER) of 10⁻³ at an IF SNR of 5.2 dB. Compared with the case of the original 50 kHz IF frequency offset, the required SNR is improved by 4.1 dB. Full article

To enhance the battery endurance of unmanned aerial vehicles (UAVs), this article addresses key issues in traditional wireless power transfer (WPT) systems. These issues occur during constant current/constant voltage (CC/CV) switching, such as poor stability, high payload, power loss, and charging instability. Accordingly, a WPT system based on topology switching is proposed. First, a lightweight compensation topology based on LCC-Series compensated topology (LCC-S) is designed. A tuning capacitor is incorporated, and two switches regulate the switching of the compensation capacitor to realize CC/CV mode transition. Meanwhile, the impedance matrix model is built to find optimal compensation component values, maximizing energy transfer. To reduce sensitivity to misalignment, a “+” shaped compensation coil is added to the basic 2 × 2 square coil array. It improves magnetic field uniformity and suppresses flux leakage. Experimental results show that the system achieves stable load-independent output. Within horizontal offset [−150, 150] mm and diagonal offset [−150√2, 150√2] mm, it keeps output power over 150 W and efficiency over 70%, with strong anti-misalignment ability. This system effectively solves key challenges such as endurance bottlenecks, complex CC/CV switching, and weak anti-misalignment. It offers a reliable technical solution for efficient charging of autonomous UAVs. Full article

The paper presents an automatic frequency calibration (AFC) technique for a charge pump-based phase-locked loop (CPPLL) with 5–6 μsec correction time. The architecture detects frequency offset in real time while keeping the loop active and performs a variable gain calibration that increases the correction gain at large frequency offsets to accelerate lock acquisition and gradually reduce the gain near locking frequency to suppress residual oscillation and overshoot. The implemented synthesizer rapidly re-acquires the lock within several adjacent coarse-tuning codes after frequency drift and maintains continuous operation without interruption. It demonstrates that the designed AFC achieves seamless frequency recovery in dynamically varying environments. Fabricated in a 65 nm CMOS process, the prototype fractional-N synthesizer occupies an active area of 0.603 mm² and operates over a 5.3–6.2 GHz tuning range. At 5.8 GHz, the design achieves a phase noise of −107 dBc/Hz at 1 MHz offset and consumes 21.5 mW from a 1.2 V supply. Full article

Optical tracking of unknown space objects requires both spatial accuracy and temporal stability to enable high-resolution identification through narrow field-of-view sensors. Traditional performance indices such as RMS error and persistence time (PT) have been used for controller tuning, but they each capture only a subset of the requirements for successful optical identification. This paper proposes a new composite metric, the Persistence-Weighted Tracking Index (PWTI), which combines spatial precision and segment-level continuity into a single measure. The metric assigns a frame-level score based on positional error and accumulates weighted scores over the longest continuous in-threshold segment. Using PWTI as the optimization objective, a genetic algorithm (GA) is employed to tune the PID gains of a frame-by-frame offset correction controller. Comparative simulations under various observation scenarios demonstrate that the PWTI-based approach outperforms RMS- and PT-based tuning methods in both alignment accuracy and consistency. The results validate the proposed metric as a more suitable performance indicator for optical identification tasks involving unknown or uncataloged targets. Full article

Fermentative processes are considered one of the most important technological developments in the modern transforming industry, due to this, the applied research to reach high performance standards with a crucial focus on system intensification, which is the the analysis, optimization, and control issues, are a cornerstone. The goal of this proposal is to show a novel nonlinear feedback control structure to assure a stable closed-loop operation in a continuous flow enzymatic–fermentative bioreactor with chaotic dynamic behavior. The proposed structure contains an adaptive-type gain, which, coupled with a proportional term of the named control error, can lead the feedback control trajectories of the bioreactor to the required reference point or trajectory. The Lyapunov method is used to present the stability analysis of the system within a closed loop, where an adequate choice of the controller gains assures asymptotic stability. Moreover, analyzing the dynamic equation of the control error, under some properties of boundedness of the system, shows that the control error can be diminished to close to zero. Numerical experiments are carried out, where a well-tuned standard proportional–integral (PI) controller is also implemented for comparison purposes, the satisfactory performance of the proposed control scheme is observed, including the diminishing offsets, overshoots, and settling times in comparison with the PI controller. Full article

Traditional Hopfield neural networks (HNNs) suffer from limitations in generating controllable chaotic dynamics, which are essential for applications in neuromorphic computing and secure communications. Memristors, with their memory-dependent nonlinear characteristics, provide a promising approach to regulate neuronal activities, yet systematic studies on attractor offset behaviors remain scarce. In this study, we propose a fully memristive electromagnetic radiation neural network by incorporating three distinct memristors as external electromagnetic stimuli into an HNN. The parameters of the memristors were tuned to modulate chaotic oscillations, while variations in initial conditions were employed to explore multistability through bifurcation and basin stability analyses. The results demonstrate that the system enables large-scale amplitude control of chaotic signals via memristor parameter adjustments, allowing arbitrary scaling of attractor amplitudes. Various offset behaviors emerge, including parameter-driven symmetric double-scroll relocations in phase space and initial-condition-induced offset boosting that leads to extreme multistability. These dynamics were experimentally validated using an STM32-based electronic circuit, confirming precise amplitude and offset control. Furthermore, a multi-channel pseudo-random number generator (PRNG) was implemented, leveraging the initial-boosted offset to enhance security entropy. This offers a hardware-efficient chaotic solution for encrypted communication systems, demonstrating strong application potential. Full article

Hybrid rule-based and reinforcement-learning (RL) signal control is gaining traction for urban coordination by pairing interpretable cycles, splits, and offsets with adaptive, data-driven updates. However, systematic evidence on their architectures, safeguards, and deployment prerequisites remains scarce, motivating this review that maps current hybrid controller designs under corridor coordination. Searches across major databases and arXiv (2000–2025) followed PRISMA guidance; screening is reported in the flow diagram. Eighteen studies were included, nine with quantitative comparisons, spanning simulation and early field pilots. Designs commonly use rule shields, action masking, and bounded adjustments of offsets or splits; effectiveness is assessed via arrivals on green, Purdue Coordination diagrams, delay, and travel time. Across the 18 studies, the majority reported improvements in arrivals on green, delay, travel time, or related coordination metrics compared to fixed-time or actuated baselines, while only a few showed neutral or mixed effects and very few indicated deterioration. These results indicate that hybrid safeguards are generally associated with positive operational gains, especially under heterogeneous traffic conditions. Evidence specific to Indonesia remains limited; this review addresses that gap and offers guidance transferable to other developing-country contexts with similar sensing, connectivity, and institutional constraints. Practical guidance synthesizes sensing choices and fallbacks, controller interfaces, audit trails, and safety interlocks into a deployment checklist, with a staged roadmap for corridor roll-outs. This paper is not only a systematic review but also develops a practice-oriented framework tailored to Indonesian corridors, ensuring that evidence synthesis and practical recommendations are clearly distinguished. Full article

This paper presents a systematic methodology for identifying optimal scaling regions in segment-based box-counting fractal dimension calculations through a three-phase algorithmic framework combining grid offset optimization, boundary artifact detection, and sliding window optimization. Unlike traditional pixelated approaches that suffer from rasterization artifacts, the method used directly analyzes geometric line segments, providing superior accuracy for mathematical fractals and other computational applications. The three-phase optimization algorithm automatically determines optimal scaling regions and minimizes discretization bias without manual parameter tuning, achieving significant error reduction compared to traditional methods. Validation across the Koch curve, Sierpinski triangle, Minkowski sausage, Hilbert curve, and Dragon curve demonstrates substantial improvements: excellent accuracy for the Koch curve (0.11% error) and significant error reduction for the Hilbert curve. All optimized results achieve $R^2\ge 0.9988$. Iteration analysis establishes minimum requirements for reliable measurement, with convergence by level 6+ for the Koch curve and level 3+ for the Sierpinski triangle. Each fractal type exhibits optimal iteration ranges where authentic scaling behavior emerges before discretization artifacts dominate, challenging the assumption that higher iteration levels imply more accurate results. Application to a Rayleigh–Taylor instability interface (D = 1.835 ± 0.0037) demonstrates effectiveness for physical fractal systems where theoretical dimensions are unknown. This work provides objective, automated fractal dimension measurement with comprehensive validation establishing practical guidelines for mathematical and real-world fractal analysis. The sliding window approach eliminates subjective scaling region selection through systematic evaluation of all possible linear regression windows, enabling measurements suitable for automated analysis workflows. Full article

To address the challenges posed by harmonic distortion and DC offset in the power grid, this paper proposes a novel Phase-Locked Loop (PLL) architecture tailored for single-phase grid-connected systems. The design integrates an Enhanced Adaptive Second-Order Generalized Integrator (EA-SOGI) with a Quasi-Proportional Resonant (QPR) controller. The proposed EA-SOGI extends the conventional SOGI by incorporating an all-pass filter and an additional integrator, which enhance the symmetry of the orthogonal signals and effectively suppress the estimation errors caused by DC offset. In addition, the conventional PI controller is replaced by a QPR controller, whose parameters are tuned using a hybrid Levy-AsyLnCPSO optimization algorithm to improve frequency locking performance and enhance system robustness under steady-state conditions. Simulation and experimental results demonstrate that the proposed PLL achieves a Total Harmonic Distortion (THD) as low as 2.8653% based on Fast Fourier Transform (FFT) analysis, indicating superior adaptability compared to conventional PLL structures and validating its effectiveness in DC offset suppression and harmonic mitigation. Full article

A ring-oscillator based voltage-controlled oscillator (VCO) architecture with reduced frequency drift across temperature and process variations is presented in this paper. The frequency stability is achieved through two dedicated compensation techniques: a temperature compensation circuit that generates a proportional-to-absolute-temperature (PTAT) current to mitigate frequency shifts due to temperature changes, and a process compensation circuit that dynamically adjusts the frequency based on detected process corners. The proposed design is implemented in a 22 nm CMOS technology with a 0.8 V supply voltage and targets a nominal oscillation frequency of 2.5 GHz. The post-layout simulation results demonstrate a significant improvement in frequency stability, reducing temperature-induced frequency drift from 23.9% to a range of 5.4% over the −40 °C to 125 °C temperature range for the typical corner. Combining temperature and process compensation, the frequency drift is improved from 47.3% to better than 7.2%. The VCO also achieves a phase noise value about −80 dBc/Hz at a 1 MHz offset with an average power consumption of 380 µW, including the tuning mechanism and the compensation circuits. Full article

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition model employing a Gabor Convolutional Network (GCN) that integrates convolutional neural networks (CNNs) with Gabor filters. To systematically optimize the network’s architecture and improve both detection accuracy and computational efficiency, the Taguchi method is employed to tune key hyperparameters, including convolutional kernel size, filter number, and Gabor parameters (frequency, orientation, and phase offset). Additionally, image tiling and augmentation techniques are employed to effectively increase the training dataset, thereby enhancing the model’s stability and accuracy. Experiments conducted on the MVTec Anomaly Detection dataset (wood category) demonstrate that the Taguchi-optimized GCN achieves an accuracy of 98.92%, outperforming a baseline Taguchi-optimized CNN by 2.73%. Results confirm that Taguchi-optimized GCNs enhance defect detection performance and computational efficiency, making them valuable for smart manufacturing. Full article

Frequency-hopping multiple access is widely adopted to blunt narrow-band jamming and limit spectral disclosure in cyber–physical systems, yet its practical resilience depends on three sequence-level properties. First, balancedness guarantees that every carrier is occupied equally often, removing spectral peaks that a jammer or energy detector could exploit. Second, a wide gap between successive hops forces any interferer to re-tune after corrupting at most one symbol, thereby containing error bursts. Third, a no-hit zone (NHZ) window with a zero pairwise Hamming correlation eliminates user collisions and self-interference when chip-level timing offsets fall inside the window. This work introduces an algebraic construction that meets the full set of requirements in a single framework. By threading a permutation over an integer ring and partitioning the period into congruent sub-blocks tied to the desired NHZ width, we generate balanced wide gap no-hit zone frequency-hopping (WG-NHZ FH) sequence sets. Analytical proofs show that (i) each sequence achieves the Lempel–Greenberger bound for auto-correlation, (ii) the family and zone sizes satisfy the Ye–Fan bound with equality, (iii) the hop-to-hop distance satisfies a provable WG condition, and (iv) balancedness holds exactly for every carrier frequency. Full article

We present a buffer-pH-modulated electrokinetic concentration strategy in MEMS microchannels that harnesses simple pH shifts to neutralize and charge proteins, reversibly “pausing” them at a planar electric-gate electrode by tuning to their isoelectric point (pI) and mobilizing them with slight pH offsets under an applied field. This synergistic coupling of dynamic pH control and electrode-gated focusing, which requires only buffer composition changes, enables rapid and tunable protein capture and release across diverse channel geometries for lab-on-chip, preparative, and point-of-care diagnostics. Moreover, it dovetails with established MEMS biomedical platforms ranging from diagnostics to drug delivery and microsurgery to gene and cell-manipulation devices. Future work on tailored electrode coatings and optimized channel profiles will further boost selectivity, speed, and integration in sub-100 µm MEMS devices. Full article