Search Results (208)

We propose a probabilistic shaping (PS) scheme based on a single-layer lookup table (LUT) that employs only one LUT for symbol mapping while achieving favorable system performance. This scheme reduces the average power of the signal by adjusting the symbol distribution using a specialized LUT architecture and a flexible shaping proportion. The simulation results indicate that the proposed PS scheme delivers performance comparable to that of the conventional constant-composition distribution-matching-based probabilistic shaping (CCDM-PS) algorithm. Specifically, it reduces the bit error rate (BER) from 1.2376 $\times {10}^{-4}$ to 6.3256 $\times {10}^{-5}$, corresponding to a 48.89% improvement. The radial basis function neural network (RBFNN) effectively compensates for nonlinear distortions and further enhances transmission performance due to its simple architecture and strong capacity for nonlinear learning. In this work, we combine lookup-table-based probabilistic shaping (LUT-PS) with RBFNN-based nonlinear equalization for the first time, completing the transmission of 16-QAM OFDM signals over a photonic terahertz-over-fiber system operating at 400 GHz. Simulation results show that the proposed approach reduces the BER by 81.45% and achieves a maximum Q-factor improvement of up to 23 dB. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

Neural networks are increasingly attractive for digital predistortion applications due to their demonstrated superior performance. This is mainly attributed to their ability to capture the intrinsic traits of nonlinear systems. This paper presents a novel hybrid predistorter labeled as the look-up table assisted bidirectional long-short term memory (BiLSTM) neural network (LUT-A-BiNN) that combines a neural network cascaded with a look-up table in a manner that both sub-models complement each other. The main motivation in using this two-box arrangement is to eliminate the highly nonlinear static distortions of the PA with the look-up table, allowing the neural network to focus on the compensation of the dynamic distortions. The proposed predistorter is experimentally validated using 5G test signals. The results demonstrate the ability of the proposed predistorter to achieve a 5 dB enhancement in the adjacent channel leakage ratio when compared to its single-box counterpart (BiLSTM neural network predistorter) while maintaining the signal-agnostic performance of the BiLSTM predistorter. Full article

New Zealand’s Emissions Trading Scheme (ETS) enables growers to earn payments by accumulating carbon units as their forests increase in carbon stock. For forests of less than 100 hectares, growers use predefined lookup tables (LUTs) to estimate carbon stock changes based on forest age. Using a combination of growth models and productivity surfaces, underpinned by data from 1360 growth plots, the objective of this study was to provide draft updates for the Exotic Hardwoods LUTs. The updated LUTs were based on growth rates of three Eucalyptus species, E. fastigata, E. regnans, and E. nitens, which comprise a major proportion of the Exotic Hardwoods forest type in New Zealand. Carbon tables were first derived for each species. Then, a draft LUT was generated for New Zealand’s North Island, using a weighted average of the species-specific tables based on the relative importance of the species, while the E. nitens table was used for the South Island where this is the predominant Eucalyptus species. Carbon stock predictions at ages 30 and 50 years were 820 and 1340 tonnes CO₂ ha⁻¹ for the North Island, and slightly higher at 958 and 1609 tonnes CO₂ ha⁻¹ for the South Island. Regional variation was significant, with the highest predicted carbon in Southland (1691 tonnes CO₂ ha⁻¹ at age 50) and lowest in Hawke’s Bay/Southern North Island (1292 tonnes CO₂ ha⁻¹). Predictions closely matched the current Exotic Hardwood LUT to age 20 years but exceeded it by up to 45% at age 35. Growth and carbon sequestration rates were similar to other established Eucalyptus species and slightly higher than Acacia species, though further research is recommended. These findings suggest that the three Eucalyptus species studied here could serve as the default species for a revised Exotic Hardwoods LUT and that the current national tables could be regionalised. However, the government may consider factors other than the technical considerations outlined here when updating the LUTs. Full article

Convolutional neural networks (CNNs) are widely used in geotechnical engineering. Real-time detection in complex geological environments, combined with the strict power constraints of embedded devices, makes Field-Programmable Gate Array (FPGA) platforms ideal for accelerating CNNs. Conventional parallelization strategies in FPGA-based accelerators often result in imbalanced resource utilization and computational inefficiency due to varying kernel sizes. To address this issue, we propose a customized heterogeneous hybrid parallel strategy and refine the bit-splitting approach for Digital Signal Processor (DSP) resources, improving timing performance and reducing Look-Up Table (LUT) consumption. Using this strategy, we deploy the lightweight YOLOv5n network on an FPGA platform, creating a high-speed, low-power subsurface geotechnical defect-detection system. A layer-wise quantization strategy reduces the model size with negligible mean average precision (mAP) loss. Operating at 300 MHz, the system reduces LUT usage by 33%, achieves a peak throughput of 328.25 GOPs in convolutional layers, and an overall throughput of 157.04 GOPs, with a power consumption of 9.4 W and energy efficiency of 16.7 GOPs/W. This implementation demonstrates more balanced performance improvements than existing solutions. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

The increasing demand for real-time multimedia communications has driven the need for highly secure and computationally efficient encryption schemes. In this work, we present a novel chaos-based encryption system that provides remarkable levels of security and performance. It leverages the benefits of applying fast-to-evaluate chaotic maps, along with a 2-Dimensional Look-Up Table approach (2D-LUT), and simple but powerful periodic perturbations. The foundation of our encryption system is a Pseudo-Random Number Generator (PRNG) that consists of a fully connected random graph with M vertices representing chaotic maps that populate the 2D-LUT. In every iteration of the system, one of the M chaotic maps in the graph and the corresponding trajectories are randomly selected from the 2D-LUT using an emulated spin-wheel picker game. This approach exacerbates the complexity in the event of an attack, since the trajectories may come from the same or totally different maps in a non-sequential time order. We additionally perform two levels of perturbation, at the map and trajectory level. The first perturbation (map level) produces new trajectories that are retrieved from the 2D-LUT in non-sequential order and with different initial conditions. The second perturbation applies a p-point crossover scheme to combine a pair of trajectories retrieved from the 2D-LUT and used in the ciphering process, providing higher levels of security. As a final process in our methodology, we implemented a simple packet-based data integrity scheme that detects with high probability if the received information has been modified (for example, by a man-in-the-middle attack). Our results show that our proposed encryption scheme is robust to common cryptanalysis attacks, providing high levels of security and confidentiality while supporting high processing speeds on the order of gigabits per second. To the best of our knowledge, our chaotic cipher implementation is the fastest reported in the literature. Full article

The use of multi-output look-up tables (LUTs) is a widely adopted approach in contemporary commercial field-programmable gate arrays (FPGAs). Larger LUT configurations (e.g., six-input LUTs) can be partitioned into smaller LUTs (e.g., two five-input LUTs, maintaining a total input count of less than six). This capability of generating a second output from a larger LUT is not only crucial for reducing logic cell count and enhancing the utilization efficiency of logic resources—thus conserving area—but also plays a key role in optimizing system-level delays and energy consumption. In this paper, we propose an efficient multi-output LUT mapping technique, incorporating several highly efficient technology mapping algorithms, which focus on optimizing the mapping from an interconnection perspective as alternatives to directly merging smaller LUTs. These algorithms include a side-fanout insertion algorithm, and a runtime multi-output cut generation algorithm. The proposed methods improve mapping efficiency and enhance performance. The benchmarking results demonstrate that the dual-output mapping algorithms achieve LUT area reductions of up to 35% and 6%, compared to the state-of-the-art ABC six-input, single-output LUT mapping technique and previous work focusing on dual-output LUT mapping techniques that optimize cut generation parameters. Moreover, FPGA system-level simulations also show that area, delay, and energy can all be optimized based on this multi-output mapping technique. Full article

Edge-computing applications demand ultra-low-power architectures for both feature extraction and classification tasks. In this manuscript, a Keyword Spotting (KWS) system tailored for energy-constrained portable environments is proposed. A 16-channel analog filter bank is employed for audio feature extraction, followed by a digital Gated Recurrent Unit (GRU) classifier. The filter bank is behaviorally modeled, making use of second-order band-pass transfer functions, simulating the analog front-end (AFE) processing. To enable efficient deployment, the GRU classifier is trained using a Learned Step Size (LSQ) and Look-Up Table (LUT)-aware quantization method. The resulting quantized model, with 4-bit weights and 8-bit activation functions (W4A8), achieves 91.35% accuracy across 12 classes, including 10 keywords from the Google Speech Command Dataset v2 (GSCDv2), with less than 1% degradation compared to its full-precision counterpart. The model is estimated to require only 34.8 kB of memory and 62,400 multiply–accumulate (MAC) operations per inference in real-time settings. Furthermore, the robustness of the AFE against noise and analog impairments is evaluated by injecting Gaussian noise and perturbing the filter parameters (center frequency and quality factor) in the test data, respectively. The obtained results confirm a strong classification performance even under degraded circuit-level conditions, supporting the suitability of the proposed system for ultra-low-power, noise-resilient edge applications. Full article

A new state assignment method focusing on Mealy finite state machines (FSMs) is proposed. The proposed codes are an alternative to composite state codes (CSCs). CSCs are represented as concatenations of group codes and partial state codes. Both group and partial state codes are maximum binary codes. We propose encoding groups using one-hot codes. The main goal of this method is improving the value of the FSM cycle time without a significant degradation of the spatial characteristics. The method can be applied if FSM circuits are implemented using the look-up table (LUT) elements of field-programmable gate arrays (FPGAs). The resulting FSM circuit includes three logic blocks. The first block generates partial input memory functions and FSM outputs depending on maximum binary state codes and one-hot group codes. The partial codes are assigned in a way minimizing the number of arguments in the partial functions. This allows for the generation of most partial functions by single-LUT circuits. The second block generates the final values of the input memory functions and FSM outputs. This block does not require group codes to generate functions, as in CSC-based FSMs. The third block transforms maximum binary group codes into their one-hot equivalents. The proposed approach allows for a reduction in the number of series-connected LUTs in comparison with CSC-based FSMs. Due to this reduction, the temporal characteristics of an FSM circuit are improved. This paper includes an example of FSM synthesis applying the proposed method. The experiments were conducted using standard benchmark FSMs. The results of the experiments show that the proposed method allowed for an improvement in the cycle time of an average of 8.81%. Moreover, in relation to CSC-based FSMs, the LUT counts decreased by an average of 4.00%. Full article

A common challenge in direct digital frequency synthesizers (DDFSs) is obtaining high memory compression while maintaining good output signal purity. To address this challenge, in this paper, we present a 16-bit, quadrature direct digital frequency synthesizer (DDFS) that utilizes the second-order Taylor series polynomial interpolation in the phase-to-amplitude conversion. In this approach, the sinusoidal signal is divided into multiple segments, and for each segment, related values are stored into a look-up table (LUT). The amplitude values for each segment are calculated using the stored LUT values and the second-order Taylor series polynomial interpolation. A Python-based model was created to optimize the number of segments, and the resulting design was coded using register-transfer level VHDL. The design is synthesized and implemented on an AMD Artix 7 FPGA, and the implementation results are presented. We show that the proposed design is capable of reaching a very high memory compression ratio of 5178:1. Additionally, the design generates both sine and cosine with high spectral purity utilizing a low number of FPGA resources compared to previous work. With 107 logic slices and 3 DSP slices, the design reaches a spurious-free dynamic range (SFDR) of −102.9 dBc. Full article

In this paper, we proposed a new state assignment method focusing on Mealy finite state machines (FSMs). The method makes it possible to improve the temporal characteristics of the circuits of FSMs, the internal states of which are encoded by the composite state codes (CSCs). These codes consist of class codes and partial state codes. Both class and partial state codes are maximum binary codes. We propose to encode classes by one-hot codes. The main goal of the method is improving the value of the FSM cycle time without any significant degradation of spatial characteristics. The method can be applied if FSM circuits are implemented using look-up table (LUT) elements of field-programmable gate arrays (FPGAs). The resulting FSM circuit includes two logic blocks. The first block generates partial input memory functions and FSM outputs depending on maximum binary state codes and one-hot class codes. The choice of partial codes allows minimizing the systems of partial functions. This allows generating most partial functions by single-LUT circuits. Some partial functions require using dedicated multiplexers. The second block generates final values of input memory functions and FSM outputs. This block does not require class codes to generate functions, which is the case of CSC-based FSMs. The proposed approach allows reducing the number of series-connected LUTs in comparison with CSC-based FSMs. Due to this reduction, the temporal characteristics are improved. The paper includes an example of FSM synthesis through applying the proposed method. The experiments are conducted using standard benchmark FSMs. The results of experiments show that the proposed method allows improving the temporal characteristics (by an average of 9.15%). In relation to CSC-based FSMs, the number of LUTs increases by an average of 10.03%, and the power consumption increases by an average of 7.63%. Full article

New Zealand’s Emissions Trading Scheme (ETS) allows growers to receive payments through the accumulation of carbon units for increased carbon stock. For forests < 100 ha, growers rely on pre-formulated lookup tables (LUTs) to estimate changes in carbon stock by age. Currently, minor exotic softwood species, which are predominantly redwood and cypresses, are covered by a general Exotic Softwoods LUT. However, this table has been found to significantly underestimate carbon sequestration for these species. Using a combination of growth models and productivity surfaces, the objective of this study was to provide draft updates for the Exotic Softwoods LUT based on redwood, and two key cypresses (Cupressus lusitanica and C. macrocarpa), at different scales (national, Island level, regional), and to identify the most appropriate scale for a revised LUT. For cypress species, carbon predictions were made using C. lusitanica for the North Island and C. macrocarpa for the South Island, as these are the preferred species for each island. Variation in redwood carbon among New Zealand’s nine regions ranged over two-fold at ages 30 (390–847 tonnes CO₂ ha⁻¹) and 50 (926–1956 tonnes CO₂ ha⁻¹) and carbon was much higher within the North Island than the South Island. Predicted carbon for cypresses was higher within the North Island than the South Island at all ages and varied across regions, by 38% at age 30 (610–840 tonnes CO₂ ha⁻¹) and 12% at age 50 (1019–1146 tonnes CO₂ ha⁻¹). These findings suggest that a separate LUT for redwood is warranted, and that cypress species could serve as the default species for a revised Exotic Softwoods LUT. They also suggest that regional tables should be considered for both redwood and cypresses. However, the government may consider factors other than the technical considerations outlined here when updating the LUTs. Full article

Field-programmable gate arrays (FPGAs) are extensively utilized to accelerate virtualized network functions (VNFs) within cloud networks. Imposing rate limits on different flows can enhance the overall bandwidth utilization of the network. Existing hardware token bucket approaches fundamentally trade off resource efficiency against configuration granularity when supporting massive queues (>512). This paper proposes a novel rate-limiting method based on the token bucket algorithm and achieves efficient resource utilization through head packet scheduling and token-to-time conversion. The experimental results show that our method achieves 1.16% lookup-table (LUT) and 2.62% flip flop (FF) resource usage compared to state-of-the-art methods, while supporting 512 queues with <0.4% rate deviation across a 100 Kbps–10 Gbps range (5-decade dynamic range). Full article

Methane is a potent greenhouse gas that significantly contributes to global warming, making the accurate quantification of methane emissions essential for climate change mitigation. The traditional matched filter (MF) algorithm, commonly used to derive methane enhancement from hyperspectral satellite data, is limited by its tendency to underestimate methane plumes, especially at higher concentrations. To address this limitation, we proposed a novel approach—the multi-level matched filter (MLMF)—which incorporates unit absorption spectra matching using a radiance look-up table (LUT) and applies piecewise regressions for concentrations above specific thresholds. This methodology offers a more precise distinction between background and plume pixels, reducing noise interference and mitigating the underestimation of high-concentration emissions. The effectiveness of the MLMF was validated through a series of tests, including simulated data tests and controlled release experiments using satellite observations. These validations demonstrated significant improvements in accuracy: In radiance residual tests, relative errors at high concentrations were reduced from up to −30% to within ±5%, and regression slopes improved from 0.89 to 1.00. In simulated data, the MLMF reduced root mean square error (RMSE) from 1563.63 ppm·m to 337.09 ppm·m, and R² values improved from 0.91 to 0.98 for Gaussian plumes. In controlled release experiments, the MLMF significantly enhanced emission rate estimation, improving $R^2$ from 0.71 to 0.96 and reducing RMSE from 92.32 kg/h to 16.10 kg/h. By improving the accuracy of methane detection and emission quantification, the MLMF presents a significant advancement in methane monitoring technologies. The MLMF’s superior accuracy in detecting high-concentration methane plumes enables better identification and quantification of major emission sources. Its compatibility with other techniques and its potential for integration into real-time operational monitoring systems further extend its applicability in supporting evidence-based climate policy development and mitigation strategies. Full article