Search Results (133)

This study addresses the role of solar energy technologies in supporting sustainable and low-carbon residential energy systems through a comparative assessment of selected system configurations for a single-family house located in Lublin, Poland: photovoltaic–thermal (PVT) collectors, a hybrid system combining photovoltaic (PV) panels and solar thermal (ST) collectors, and a standalone PV installation. The analysis was carried out under the primary assumption of limited rooftop area available for renewable energy systems. The operational performance of each configuration was simulated using POLYSUN v. 2025.1 software, while the environmental impacts over a 25-year lifetime were evaluated using life cycle assessment (LCA) in SimaPro v. 10.3.0.1, including IPCC 2021 Global Warming Potential (GWP100) and ReCiPe 2016 Endpoint indicators. The results indicate that the PV + ST configuration achieved the best energy and environmental performance, providing the highest solar contribution (57.8%), the lowest total energy consumption (5136 kWh/year), and the lowest environmental impacts in both impact assessment methods (64.3 tCO₂ eq. and 3487 Pt). Under the adopted design assumptions, the PVT system exhibited intermediate overall performance between the PV + ST and standalone PV systems. The study demonstrates that combining energy performance analysis with LCA provides a more comprehensive basis for selecting sustainable solar technologies for low-carbon residential buildings than energy indicators alone. Full article

In SRAM-based compute-in-memory (CIM), read-bitline (RBL) charging and discharging depend on the physical bit pattern stored in the memory array, so the energy-relevant code statistic should be defined with respect to the actual read-port polarity. This paper presents a read-polarity-aware row-wise offset-encoding method for W4A8 INT4 weights. In the evaluated Q-sensed 8T topology, the stored logical one is the discharge-active state; hence, the topology-specific read-active density equals the stored-one fraction. Under the exact whole-row INT4-feasibility protocol, a nonzero row offset is accepted only when every translated valid signed-INT4 code remains within [−8, 7]; no clipping, saturation, wraparound, or remapping is permitted, and zero offset remains the fallback. The complete software evaluation covers 286 quantized modules, 579,464 physical 16 × 16 tile positions, and 147,156,296 quantized weights across ResNet-18, MobileNetV3-Small, and SmolLM2-135M. Circuit re-validation uses 300 independent tile-policy samples, 1200 matched baseline-selected bitplane pairs, and 2400 successfully completed transistor-level Spectre simulations. The balanced circuit population yields an aggregate local SRAM readout-energy reduction of 5.03%, with a sample-cluster bootstrap 95% confidence interval of 4.12–6.02%. After four-bitplane aggregation, relative read-active-density reduction and local SRAM readout-energy reduction exhibit Pearson r = 0.81 and Spearman $\rho$ = 0.75, indicating a substantial but imperfect relationship. The directly validated no-offset, positive-offset, and signed-offset policies preserve the corresponding model-level Top-1 accuracy or perplexity. Proposal-specific digital overheads and metadata-storage capacity are quantified separately, whereas representative physical SRAM/ROM metadata-access energy remains uncharacterized. Accordingly, the measured energy benefit is limited to local SRAM readout; a net energy reduction at the complete CIM-macro or system level, robustness across all evaluated PVT conditions, and robustness to process mismatch are not established by the present evidence. Full article

Photovoltaic–thermal (PVT) collectors coupled to thermochemical energy storage (TCES) can turn intermittent low-grade solar heat into a dispatchable service, but solar intermittency poses a closed-loop control problem. A companion study established the feedback-only lower bound: a 937 kJ accumulated exergy-delivery-deficit benchmark under optimally tuned proportional–integral–derivative (PID) flow control. The corresponding upper bound is quantified here by means of a deliberately idealised search-based predictive controller that, at each 10 s step, enumerates 51 candidate pump rates, predicts the reactor-inlet temperature by a single forward-Euler step, and is granted perfect future irradiance. On the experimentally validated shared plant (matched to the companion baseline), against an optimally tuned PID, the perfect-foresight advantage is marginal: +0.96% daily exergy on synthetic days and +0.07–0.24% on two measured Walailak University monsoon days, all controllers tracking within 6–13 K on the measured days. Under tropical-monsoon irradiance, the 95 °C desorption setpoint is rarely sustained, so the delivered exergy is nearly controller-independent: the perfect-foresight upper bound lies just above the feedback-only lower bound, and together the two results bracket the exergy envelope available to any flow-rate controller of this system. A horizon sweep localises the bottleneck to internal-model fidelity, not anticipation depth. The eight-node plant is validated against measured module temperature (root-mean-square error 3.5 °C, coefficient of determination R² = 0.89) and a copper-tube PVT prototype (1.5 °C; peak hot water up to 79 °C). The central contribution is therefore a rigorously defined, experimentally grounded upper bound showing that, at this scale and latitude, deployability rather than anticipation is the effective design lever. Full article

This paper presents a bandgap-referenced current-mode vertical-cavity surface-emitting laser (VCSEL) driver for short-range light detection and ranging (LiDAR) sensor applications. To improve current stability under process, voltage, and temperature (PVT) variations, the proposed driver employs a bandgap-referenced bias generation scheme combined with a current-mode modulation architecture. The driver was implemented in a 0.18 µm CMOS process and occupies a compact core area of 350 × 100 µm². Post-layout simulation results show that the proposed circuit maintains a bias current of approximately 2 mA and a modulation current of approximately 10 mA across PVT corners. The fabricated chip was measured using a 50 Ω termination, and the measured output voltage swing was approximately 495 mV_pp, corresponding to a modulation current of 9.9 mA_pp. Hence, the proposed current-mode VCSEL driver provides a potential solution as a compact, low-power, stable LiDAR sensor transmitter. Full article

Photovoltaic–thermal (PVT) collectors co-generate electricity and heat—natural front ends for thermochemical energy storage (TCES)—provided the heat transfer fluid stays above the reactor’s desorption temperature. Using an eight-node model of a 0.6834 m² collector at 8.64° N whose thermal core is partially validated against measured data from the same tropical–coastal site (rooftop PV module temperature, RMSE 3.8 °C; prototype absorber-to-water heat transfer, RMSE 1.3 °C), flow-regulated to the ≈95 °C SrCl₂/NH₃ desorption threshold, we quantify the energy and exergy delivered for charging under tropical–monsoon intermittency. The 95 °C setpoint operation, the ≈5.3 h charging window, and all reported exergy yields are simulated: the built prototype delivered hot water peaking at 79 °C and did not reach the 95 °C setpoint. On a measured clear-sky day (clearness index K_t = 0.52), the collector yields 1.38 kWh of energy but only 0.43 kWh of exergy (first-law efficiency ≈ 38%; gross exergy efficiency ≈ 13%); across a 30-seed synthetic-intermittency ensemble, the exergy yield is 0.678 kWh at ≈14% gross exergy efficiency (≈52% combined first-law efficiency). In both cases, the thermal stream dominates the energy output while the electrical stream dominates the exergy output—on the sunlit day, the exergy is about 80% electrical—because 95 °C heat carries a Carnot factor (exergetic quality factor, 1 − T_a/T₇, at the instantaneous ambient dead state) of only ≈0.18 and an integrated Bejan/Kotas thermal-exergy quality of only ≈0.09. The controller holds the outlet within 1.4 K of the setpoint for ≈5.3 h, whereas no fixed flow in the 0.5–5.0 L min⁻¹ range ever reaches it: feedback control is a structural enabler, not an optimisation. On overcast days, the threshold is never reached and charging heat collapses to zero, leaving a PV-only generator. Exergy delivery is nonetheless nearly controller-independent: the accumulated exergy delivery deficit after a 50% irradiance drop is 937 kJ, a controller-independent value changing only 1.3% across a systematic 4 × 4 gain sweep (K_p 0.33–2.7×, K_d 0.25–5× of nominal), and predictive control improves it by ≤1%. For PVT–TCES at this scale, the decisive lever is deployability, not control sophistication. Full article

Thin-film solar cells (TFSCs) remain a cornerstone of the global transition toward renewable energy, characterized by consistent reductions in manufacturing costs and steady gains in power conversion efficiency. In addition to electricity generation, TFSCs play an important role in advanced solar thermal cooling, heating, and energy storage systems, where their tunable optical absorption, low thermal mass, and flexibility enable integration with photovoltaic–thermal (PV/T) collectors, thermally driven cooling cycles, and hybrid thermal–electrical storage architectures. This paper provides a comprehensive review of prominent TFSC technologies, including copper indium gallium selenide (CIGS), cadmium telluride (CdTe/CdS), amorphous silicon (a-Si), copper zinc tin sulfide (CZTS), organic photovoltaics (OPVs), and metal halide perovskite solar cells (PSCs), with a focus on their material structures, performance specifications, and current efficiency benchmarks. Compared to state-of-the-art reviews, this article distinguishes itself by addressing next-generation innovations, cross-domain solar thermal–photovoltaic applications, and economic analysis. Specifically, the integration of machine learning and simulation-based material dynamics is examined to accelerate material discovery, process optimization, and the characterization of novel TFPV components relevant to coupled thermal–electrical energy systems. Furthermore, the study explores how additive manufacturing is transforming the industry through the development of high-efficiency electrodes, electrohydrodynamic atomization for thin-film deposition, and the fabrication of flexible solar arrays suitable for thermally integrated and building-scale energy systems, including space applications. By integrating advancements in module efficiency, scalable manufacturing approaches, and techno-economic analysis, this paper positions TFSCs as sustainable, resource-abundant technologies essential for next-generation solar thermal cooling, heating, and energy storage infrastructures. Full article

In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid. Full article

A dual-mode variable gain amplifier (VGA) with a wide-dynamic-range is proposed in this paper. The VGA is designed in a 0.18 μm CMOS process, and it has a body-driven variable load cell and binary gain array structure to implement both the digitally stepped programmable gain amplifier (PGA) mode and the analog-controlled VGA mode. This design removes additional digital conversion modules when integrated into an automatic gain control (AGC) loop, which simplifies the whole system architecture significantly. The design is also able to address several limitations of conventional VGAs, such as a single control mode, low AGC compatibility, and a narrow gain range. The simulation results after post-layout indicate that at PGA mode, the design has an ultra-wide gain band of −0.03 to 126.9 dB with a constant gain step of 1 dB. And in VGA mode, it allows smooth, continuous gain adjustment over a large range of −25.3 dB to 187.4 dB. The bandwidth of −3 dB is more than 45 MHz in both modes. The whole VGA uses 1.026 mW and has a core size of 0.011 mm². The output 1-dB compression point (OP1dB) was −1.57 dBm at minimum gain in the PGA mode and −4.02 dBm in the VGA mode. Besides, PVT analysis, Monte Carlo simulations and AGC system-level verification are evident enough to prove that the suggested VGA has high immunity to PVT (Process, Voltage, Temperature) variations, stable processes and high practicality in engineering applications. Full article

This paper presents a nanowatt-scale operational transconductance amplifier (OTA) and an electronically tunable third-order low-pass filter (LPF) designed for energy-constrained biomedical signal conditioning. The circuits are implemented in a 65 nm CMOS process and verified through comprehensive schematic-level simulations. Biased in the deep subthreshold region at 1 nA, the OTA achieves a 50 dB low-frequency gain, a 225 Hz unity-gain bandwidth at 10 pF load capacitance and an input-referred noise floor of 1.55 μV/√Hz, with a total power consumption of only 1.75 nW. The integrated third-order LPF provides a wide tuning range (37–668 Hz) via bias current modulation, exhibiting excellent linearity with a THD of 0.059% and a 65.3 dB dynamic range. Monte Carlo and PVT corner analyses demonstrate the design’s theoretical robustness against process variations and environmental fluctuations. ECG signal simulations validate the circuit’s effectiveness in suppressing high-frequency artifacts while preserving morphological integrity, providing a proof-of-concept for ultra-low-power wearable healthcare architectures. Full article

The global incidence of skin cancer is rising, making it an increasingly critical public health issue. Malignant skin tumors such as melanoma originate from pathological alterations in skin cells, and their accurate early-stage segmentation is crucial for quantitative analysis, early diagnosis, and effective treatment. However, achieving precise and efficient segmentation remains a major challenge, as existing methods often struggle to capture complex lesion characteristics. To address this challenge, we propose a novel deep learning framework that integrates the PVT v2 backbone with two key modules: the Spatial-Aware Feature Enhancement (SAFE) module and the Multiscale Dual Cross-attention Fusion (MDCF) module. The SAFE module enhances multi-scale encoder features through a dual-branch architecture, which adaptively extracts offset information to integrate fine-grained shallow details with deep semantic information, thereby bridging the feature gap across network depths. The MDCF module establishes bidirectional cross-attention between decoder and encoder features, followed by multi-scale deformable convolutions that capture lesion boundaries and small fragments across heterogeneous receptive fields, thereby enriching semantic details while suppressing background interference. The proposed model was evaluated on two public benchmark datasets (ISIC 2016 and ISIC 2018), achieving Intersection over Union (IoU) scores of 87.33% and 83.67%, respectively. These results demonstrate superior performance compared to current state-of-the-art methods and indicate that our framework significantly enhances skin lesion image analysis, offering a promising tool for improving early detection of skin cancer. Full article

The primary objective of this study is to develop a specialized deep learning framework specifically adapted for the unique physical characteristics of neurovascular Optical Coherence Tomography (OCT) imaging. Although Polyp-PVT, originally designed for polyp segmentation, shows promise for OCT analysis, it faces limitations in neurovascular applications. The default RGB input wastes resources on duplicated grayscale data, while its fixed-scale fusion struggles with vascular curvature variations. Furthermore, the attention mechanism fails to capture radial vessel patterns, and geometric constraints limit thin boundary detection. To address these challenges, we propose Brain-OCT-PVT with key innovations: a single-channel input stem reducing parameters by two-thirds; a Radial Intensity Module (RIM) using polar transforms and angular convolution to model annular structures; and a Deformable Cross-scale Fusion Module (D-CFM) with learnable offsets. The Boundary-aware Attention Module (BAM) combines Laplace edge detection with Swin-Transformer for sub-pixel consistency. A specialized loss function combines Dice Similarity Coefficient (Dice), BoundaryIoU on 2-pixel dilated edges, and Focal Tversky to handle extreme class imbalance. Evaluation on 13 clinical cases achieves a Dice score of 95.06% and an 95% Hausdorff Distance (HD95) of 0.269 mm, demonstrating superior performance compared to existing approaches. Full article

As the mainstream technology solution for deep imaging LiDAR, dToF measurement has been widely applied in emerging fields such as environmental perception and obstacle recognition, 3D terrain reconstruction, real-time motion capture, and drone obstacle avoidance navigation due to its advantages of high resolution, long-range detection capability, and high sensitivity. In order to adapt to functional applications in different scenarios, the resolution of TDC needs to be adjustable and can work normally in different environments. In view of this, this article studies the pixel array and TDC circuit in the chip and locks a voltage-controlled ring oscillator (VCRO) with the same structure as the pixel to a fixed frequency through a PLL structure. Then copy the control voltage of the locked VCRO to the control terminal of the TDC in each pixel. In an ideal situation, this control voltage can make the oscillation frequency of VCRO within the pixel consistent with the locking frequency of VCRO within the PLL, and insensitive to changes in PVT. This study developed a module expandable 16 × 16-pixel array dToF sensor chip based on TDC architecture using CMOS technology. Finally, six configurable 16 × 16-pixel subarrays were integrated and constructed into a 32 × 48 large-scale dToF sensor chip through modular splicing. The top-level layout design was completed using SMIC 180 nm technology, with a layout area of 5285 µm × 3669 µm. Post-simulation verification showed that, under the testing conditions of a 400 MHz system clock and a 33.3 kHz frame rate, the dToF chip system performance indicators were: time measurement resolution of 156 ps, DNL < 1 LSB, INL < 0.85 LSB, and absolute ranging accuracy better than 2.5 cm. Full article

Photovoltaic–thermal (PVT) collectors often experience limited heat extraction under laminar cooling conditions, and the influence of controlled flow pulsation on full-scale PVT performance has not been clearly established. This study experimentally investigates a water-cooled PVT system operated under pulsating flow using an indoor solar simulator to quantify its thermal and electrical response. Flow pulsations were generated using a solenoid valve at frequencies of 0.25, 0.5, 1, and 2 Hz across inlet flow rates of 1–4 L/min, with average irradiance maintained between 700 and 800 W/m². System performance was benchmarked against uncooled and continuous-flow reference cases. Pulsating operation reduced the PVT surface temperature and produced a clear enhancement in thermal performance relative to continuous flow, while electrical efficiency exhibited a smaller but consistent improvement that followed the same thermal trend. A pulsation frequency of 0.5 Hz yielded the most favorable results, achieving thermal efficiencies exceeding 50% at higher flow rates without any measurable increase in average pressure drop. Electrical efficiency stabilized at approximately 9.82%, slightly higher than that obtained under continuous-flow operation. The results indicate that low-frequency pulsating flow can significantly improve thermal energy extraction in PVT systems under controlled conditions, with modest associated electrical gains, and provide a basis for further investigation of flow-modulation strategies for thermally driven PVT applications. Full article

Photovoltaic thermal systems are capable of simultaneously generating electricity and recovering thermal energy from the rear surface of photovoltaic modules. When integrated with an air-source heat pump, the thermal energy recovered from an air-based photovoltaic thermal system can be utilized as an auxiliary heat source, thereby improving heating performance and reducing electricity consumption. In this study, a demonstration-scale performance assessment of an air-based photovoltaic thermal-assisted air-source heat pump system was conducted in a real building located in Asan, South Korea. Performance analysis was based on measured operational data collected over a one-month period in March 2024, corresponding to late-winter to early-spring conditions when heating demand was still present. During the measurement period, the average plane-of-array solar irradiance was approximately 600 W/m², with peak values reaching up to 1000 W/m². Under these conditions, the air-based photovoltaic thermal collector provided average electrical and thermal power outputs of 1.96 kW and 2.2 kW, respectively, while peak outputs reached 3.3 kW for electricity generation and 3.8 kW for thermal energy recovery. The daily thermal energy production remained relatively stable, ranging from 17.8 to 21.7 kWh. Furthermore, approximately 45–60% of the recovered thermal energy was effectively transferred to a buffer tank through an air-to-water heat exchanger, indicating stable solar heat recovery and storage performance. When the recovered thermal energy was supplied to the air-source heat pump during daytime heating operation, a preheating effect was observed, resulting in reduced electricity consumption and improved heating performance. The coefficient of performance increased from 2.24 during nighttime operation to 2.81 under solar-assisted daytime conditions, corresponding to a notable reduction in electricity consumption under solar-assisted daytime operation, compared with nighttime operation without PVT preheating. Overall, the results indicate that, under the tested late-winter to early-spring heating conditions, the integrated air-based photovoltaic thermal and air-source heat pump system can enhance heating performance and reduce electricity consumption, demonstrating its practical feasibility as a solar-assisted heating solution rather than representing generalized annual performance. Full article

Amidst the ongoing global energy crisis, environmental deterioration, and the exacerbation of climate change, the development of renewable energy, particularly solar energy, has become a central topic in the global energy transition. This study investigates a solar photovoltaic thermal (PVT) heat pump system that utilizes an expanded honeycomb-channel PVT module to enhance the comprehensive utilization efficiency of solar energy. A simulation platform for the solar PVT heat pump system was established using Aspen Plus software (V12), and the system’s performance impact mechanisms and engineering applications were researched. The results indicate that solar irradiance and the circulating water temperature within the PVT module are the primary factors affecting system performance: for every 100 W/m² increase in solar irradiance, the coefficient of performance for heating (COP_h) increases by 13.7%, the thermoelectric comprehensive performance coefficient (COP_co) increases by 14.9%, and the electrical efficiency of the PVT array decreases by 0.05%; for every 1 °C increase in circulating water temperature, the COP_h and COP_co increase by 11.8% and 12.3%, respectively, and the electrical efficiency of the PVT array decreases by 0.03%. In practical application, the system achieves an annual heating capacity of 24,000 GJ and electricity generation of 1.1 million kWh, with average annual COP_h and COP_co values of 5.30 and 7.60, respectively. The Life Cycle Cost (LCC) is 13.2% lower than that of the air-source heat pump system, the dynamic investment payback period is 4–6 years, and the annual carbon emissions are reduced by 94.6%, demonstrating significant economic and environmental benefits. This research provides an effective solution for the efficient and comprehensive utilization of solar energy, utilizing the low-global-warming-potential refrigerant R290, and is particularly suitable for combined heat and power applications in regions with high solar irradiance. Full article