Search Results (48)

In this paper, we propose a new synthesis method for LUT-based Moore finite state machines (FSMs) with twofold state assignment (TSA). The method introduces an additional core of partial input memory functions (IMFs), resulting in an architecture with two IMF cores. The first core is based on structural decomposition using additional partial state variables, whereas the second uses maximum binary state codes. Both cores are implemented as single-level circuits. We formulate the conditions under which the proposed method can be applied and show that it improves both the area and timing characteristics of the resulting FSM circuits. The method exploits pseudoequivalent state classes to reduce the number of literals in sum-of-products describing partial IMFs. The developed FSM architecture is organized into three logic stages. At the first stage, two dedicated blocks generate partial IMFs. At the next stage, these intermediate functions are merged and used to form the maximum binary state code. The final stage produces both the output signals and the partial state encoding. The proposed method is illustrated by a synthesis example and validated using standard benchmark FSMs. The obtained results indicate that the method is particularly suitable for larger and more complex Moore FSM implementations. Full article

In digital signal processing, the finite impulse response (FIR) filter is a fundamental tool for processing discrete-time signals. This paper explores the implementation of FIR filters using quantum computation methods. In this study, a quantum circuit for the FIR filter is designed using a normalized filter coefficient vector, orthogonal triangular decomposition commonly known as QR decomposition, and the transpilation tools provided by IBM’s software Qiskit SDK V2.3. Then, each block of the input signal is normalized to a unit-norm vector, loaded into a quantum register, and processed by the FIR filter quantum circuit to produce an output state. Quantum measurement is then performed on the output state to obtain a histogram, from which the first-bin data are scaled to compute the output sample of the filter. Finally, signal filtering experiments using FIR mean filters are conducted to demonstrate the effectiveness of the proposed quantum computation approach. Full article

Financial data analysis remains a significant challenge due to the inherent stochasticity, non-stationarity, and low signal-to-noise ratio of market data. Conventional methods often struggle to disentangle intrinsic trends from noise and frequently overlook the critical influence of investor sentiment on price dynamics. To address these issues, we propose an adaptive trading model named CBAM-BiLSTM-DDQN, which integrates signal decomposition, multi-source feature fusion, and deep reinforcement learning. First, we construct a comprehensive heterogeneous feature set by combining price signals decomposed via Variational Mode Decomposition (VMD) and investor sentiment indices extracted from financial texts. Subsequently, a Genetic Algorithm (GA) is employed to identify the most significant feature subset, effectively reducing dimensionality and redundancy. Finally, these optimized features are input into a Double Deep Q-Network (DDQN) agent equipped with a Convolutional Block Attention Module (CBAM) and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture complex spatiotemporal dependencies. We evaluated this approach through simulated trading on three major Chinese stock indices—the Shanghai Stock Exchange Composite (SSEC), the Shenzhen Stock Exchange Component (SZSE), and the China Securities 300 (CSI 300). Experimental results demonstrate the superiority of our method over traditional strategies and standard baselines; specifically, the trading agent achieved robust cumulative returns across the SSEC and CSI 300 indices, confirming the model’s exceptional capability in balancing profitability and risk aversion in complex financial environments. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Full article

Realistic atomistic modeling of mineral and soil systems requires chemically meaningful representations of organic matter (OM). Bulk ¹³C nuclear magnetic resonance (NMR) data have been proposed as compositional inputs for stochastic generation of OM structures, and prior studies using nonnegative multivariate curve resolution (MCR) suggested that bulk ¹³C NMR spectra of OM may be represented as mixtures of only a few components. However, these studies typically relied on single-block decompositions and did not explicitly assess decomposition uniqueness. The objective of this work was to examine whether a quantitative and chemically interpretable nonnegative MCR decomposition of OM can be obtained while explicitly evaluating (1) residual rotational ambiguity controlling the uniqueness of components, and (2) the variance captured by the decomposition. Using a dataset of International Humic Substances Society (IHSS) humic acids, fulvic acids, and aquatic OM, we applied single- and multi-block nonnegative MCR–alternating least squares (ALS) analyses integrating ¹³C NMR spectra, elemental composition (C, H, O, N, S), and titratable carboxylic and phenolic group contents. The multi-block approach effectively narrowed the feasible solution space and enriched the chemical characterization of the resulting MCR components. Across all analytical blocks, two chemically distinct components, an aromatic-rich and an aliphatic-rich motifs, consistently emerged, together explaining ~97–98% of the total variance and exhibiting near-zero residual rotational ambiguity. These findings support that diverse OM types can be represented quantitatively as mixtures of a small set of unique recurring compositional motifs. These motifs serve as ensemble-level averages whose underlying molecular diversity may vary substantially across materials. They provide quantitative, chemically justified inputs for molecular modeling of mineral–OM systems, which could contribute to chemical interpretability of modeling and provide better mechanistic insights into OM variation across diverse sample series. Full article

In this paper, a new method of structural decomposition is proposed. The method focuses on FPGA-based Moore finite state machines (FSMs). The method makes it possible to improve both spatial and temporal characteristics of the FSM circuits. Each internal state is represented by two codes. One of them is a partial state code representing a state as an element of some class of compatibility. The second code is represented by a concatenation of two codes: a code of output collection and a code of identifier. The method can be applied if FSM circuits are implemented using look-up table (LUT) elements of field-programmable gate arrays. The resulting FSM circuit includes three logic blocks. The first block generates partial Boolean functions representing partial output collections and identifiers. These functions depend on partial state codes. The partial codes are assigned in a way minimizing the number of arguments in partial functions. This allows generating all partial functions by single-LUT circuits. The second block generates codes of output collections and identifiers. The third block transforms them into FSM outputs and partial state codes. The paper includes an example of FSM synthesis by applying the proposed method. The experiments are conducted using standard benchmark FSMs. The experiments show that the proposed approach can be used for complex FSMs where the total number of FSM inputs and state variables are at least twice the number of inputs of the base LUT. The results of experiments show that the proposed method allows improving both the spatial and temporal characteristics for complex FSMs compared with their counterparts based on methods used by the Vivado tool. Full article

The interaction between organic and inorganic nutrients, bacterial communities, and soil fertility has been well documented over time. Conventional agricultural systems heavily utilize both inorganic and organic fertilizers, each exerting distinct effects on soil microbial dynamics and plant growth. The objective of our experiments was to identify the most effective fertilization strategy for improving the biological quality of a microbiologically impoverished and low-productivity soil. To this end, four fertilization strategies were evaluated: (i) organic fertilizers characterized by a high content of organic carbon (Fertil 4-5-7—variant 1); (ii) organic fertilizers with 12% organic nitrogen from proteins (Bio Ostara N—variant 2) (iii) combined inorganic–organic fertilizers (P35 Bio—variant 3) and (iv) mineral (inorganic) fertilizers (BioAktiv—variant V4). This study aimed to assess the short-term effects of fertilizers with varying chemical compositions on the density of cultivable heterotrophic bacteria and their associated dehydrogenase (DH) activity in a petrocalcic chernozem soil containing pedogenic carbonates. Soil sampling was conducted according to a randomized block design, comprising four replicates per treatment (control plus four fertilizer types). The enumeration of cultivable bacteria was performed using Nutrient Agar and A2R Agar media, whereas dehydrogenase activity (DHA) was quantified based on the reduction of 2,3,5-triphenyl-2H-tetrazolium chloride (TTC) to 1,3,5-triphenyl-tetrazolium formazan (TPF) by bacterial dehydrogenase enzymes. Marked differences were observed in both parameters between the plots amended with inorganic fertilizers and those treated with organic fertilizers, as well as among the organic fertilizer treatments of varying composition. The most pronounced increases in both bacterial density and dehydrogenase activity (DHA) were recorded in the plots receiving the fertilizer with a high organic nitrogen content. In this treatment, the maximum bacterial population density reached 6.25 log₁₀ CFU g⁻¹ dry soil after approximately two months (May), followed by a significant decline starting in July. In contrast, DHA exhibited a more rapid response, reaching its peak in April (42.75 µg TPF g⁻¹ soil), indicating an earlier DHA activation of microbial metabolism. This temporal lag between the two parameters suggests that enzymatic activity responded more swiftly to the nutrient inputs than did microbial biomass proliferation. For the other two organic fertilizer variants, bacterial population dynamics were broadly similar, with peak densities recorded in June, ranging from 5.98 log₁₀ CFU g⁻¹ soil (V3) to 6.03 log₁₀ CFU g⁻¹ soil (V1). A comparable trend was observed in DHA: in V3, maximum DHA was attained in June (30 µg TPF g⁻¹ soil), after which it remained relatively stable, whereas in V1, it peaked in June (24.05 µg TPF g⁻¹ soil) and subsequently declined slightly toward the end of the experimental period. Overall, the temporal dynamics of bacterial density and DHA demonstrated a strong dependence on the quality and biodegradability of the organic matter supplied by each fertilizer. Both parameters were consistently lower under inorganic fertilization compared with organic treatments, suggesting that the observed increases in microbial density and activity were primarily mediated by the enhanced availability of organic substrates. The relationship between the density of culturable heterotrophic bacteria and dehydrogenase (DH) activity was strongly positive (r = 0.79), indicating a close functional linkage between bacterial density and oxidative enzyme activity. This connection suggests that the culturable fraction of the heterotrophic microbial community plays a key role in the early stages of organic matter mineralization derived from the applied fertilizers, particularly in the decomposition of easily degradable substrates. Full article

Karst springs play a critical strategic role in regional economic and ecological sustainability, yet their spatiotemporal heterogeneity and hydrological complexity pose substantial challenges for flow prediction. This study proposes FMD-mGTO-BiGRU-KAN, a four-stage hybrid deep learning architecture for daily spring flow prediction that integrates multi-feature signal decomposition, meta-heuristic optimization, and interpretable neural network design: constructing an Feature Mode Decomposition (FMD) decomposition layer to mitigate modal aliasing in meteorological signals; employing the improved Gorilla Troops Optimizer (mGTO) optimization algorithm to enable autonomous hyperparameter evolution, overcoming the limitations of traditional grid search; designing a Bidirectional Gated Recurrent Unit (BiGRU) network to capture long-term historical dependencies in spring flow sequences through bidirectional recurrent mechanisms; introducing Kolmogorov–Arnold Networks (KAN) to replace the fully connected layer, and improving the model interpretability through differentiable symbolic operations; Additionally, residual modules and dropout blocks are incorporated to enhance generalization capability, reduce overfitting risks. By integrating multiple deep learning algorithms, this hybrid model leverages their respective strengths to adeptly accommodate intricate meteorological conditions, thereby enhancing its capacity to discern the underlying patterns within complex and dynamic input features. Comparative results against benchmark models (LSTM, GRU, and Transformer) show that the proposed framework achieves 82.47% and 50.15% reductions in MSE and RMSE, respectively, with the NSE increasing by 8.01% to 0.9862. The prediction errors are more tightly distributed, and the proposed model surpasses the benchmark model in overall performance, validating its superiority. The model’s exceptional prediction ability offers a novel high-precision solution for spring flow prediction in complex hydrological systems. Full article

This paper presents new fast algorithms for the fourth type discrete Hartley transform (DHT-IV) for input data sequences of lengths from 3 to 8. Fast algorithms for small-sized trigonometric transforms can be used as building blocks for synthesizing algorithms for large-sized transforms. Additionally, they can be utilized to process small data blocks in various digital signal processing applications, thereby reducing overall system latency and computational complexity. The existing polynomial algebraic approach and the approach based on decomposing the transform matrix into cyclic convolution submatrices involve rather complicated housekeeping and a large number of additions. To avoid the noted drawback, this paper uses a structural approach to synthesize new algorithms. The starting point for constructing fast algorithms was to represent DHT-IV as a matrix–vector product. The next step was to bring the block structure of the DHT-IV matrix to one of the matrix patterns following the structural approach. In this case, if the block structure of the DHT-IV matrix did not match one of the existing patterns, its rows and columns were reordered, and, if necessary, the signs of some entries were changed. If this did not help, the DHT-IV matrix was represented as the sum of two or more matrices, and then each matrix was analyzed separately, if necessary, subjecting the matrices obtained by decomposition to the above transformations. As a result, the factorizations of matrix components were obtained, which led to a reduction in the arithmetic complexity of the developed algorithms. To illustrate the space–time structures of computational processes described by the developed algorithms, their data flow graphs are presented, which, if necessary, can be directly mapped onto the VLSI structure. The obtained DHT-IV algorithms can reduce the number of multiplications by an average of 75% compared with the direct calculation of matrix–vector products. However, the number of additions has increased by an average of 4%. Full article

Traditional disease detection methods typically depend on visual assessments conducted by human experts, which are time-consuming and subjective. Thus, there is an urgent demand for automated and efficient approaches to accurately detect and classify tea diseases. This study presents an enhanced Real-Time Detection Transformer (RT-DETR), tailored for the accurate and efficient identification of tea diseases in natural environments. The proposed method integrates three novel components: Faster-LTNet, CG Attention Module, and RMT Spatial Prior Block, to significantly improve computational efficiency, feature representation, and detection capabilities. Faster-LTNet employs partial convolution and hierarchical design to optimize computational resources, while the CG Attention Module enhances multi-head self-attention by introducing grouped feature inputs and cascading operations to reduce redundancy and increase attention diversity. The RMT Spatial Prior Block integrates a Manhattan distance-based spatial decay matrix and linear decomposition strategy to improve global and local context modeling, reducing attention complexity. The enhanced RT-DETR model achieves a detection precision of 89.20% and a processing speed of 346.40 FPS. While the precision improves, the FPS value also increases by 109, which is superior to the traditional model in terms of precision and real-time processing. Additionally, compared to the baseline model, the FLOPs are reduced by 50%, and the overall model size and parameter size are decreased by approximately 50%. These findings indicate that the proposed algorithm is well-suited for efficient, real-time, and lightweight agricultural disease detection. Full article

Few-shot learning (FSL) is an effective solution for cross-domain hyperspectral image (HSI) classification, which could address the limited labeled samples of the target domain. Current FSL methods mostly utilize the 3D-CNN to transform the spatial and spectral information into a single feature to model an HSI, which means that spatial and spectral information are treated equally in the feature-modeling process. However, spectral information is considered to be more domain-invariant than spatial information. Treating the spatial and spectral information equally may result in parameter redundancy and undesirable cross-domain classification performance. In this paper, we attempt to use tensor mathematics for modeling the HSI and propose a novel few-shot learning method, called tensor-based few-shot Learning (TFSL) for cross-domain HSI classification, which aims to guide the model to focus on the extraction of domain-invariant spectral dependencies. Specifically, we first propose a spatial–spectral tensor decomposition (SSTD) model to provide a mathematical explanation of the input HSI, representing the local spatial–spectral information as 1D and 2D local tensors to reduce the data redundancy. Additionally, a tensor-based hybrid two-stream (THT) model is proposed for extracting the domain-invariant spatial–spectral tensor feature by using 1D-CNN and 2D-CNN. Furthermore, we adopt a 1D-CNN tensor feature enhancement block to enhance the spectral feature of hybrid two-stream tensors and guide the THT model to concentrate on the modeling of spectral dependencies. Finally, the proposed TFSL is evaluated on four public HSI datasets, and the extensive experimental results demonstrate that the proposed TFSL significantly outperforms other advanced FSL methods. Full article

Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, previous research primarily focuses on local spatial dependencies, struggling to capture implicit global information. We propose a spatial modeling module that leverages a dynamic global attention network (DGAN) to capture dynamic global information from all-pair interactions, intricately fusing prior knowledge from the input graph with a graph convolutional network. In the temporal dimension, we design a temporal modeling module tailored to navigate the challenges of both long-term and recent-term temporal passenger flow patterns. This module consists of series decomposition blocks and locality-aware sparse attention (LSA) blocks to incorporate multiple local contexts and reduce computational complexities in long sequence modeling. Experiments conducted on both simulated and real-world datasets validate the exceptional predictive performance of our proposed model. Full article

As artificial intelligence technology progresses, deep learning models are increasingly utilized for machine fault classification. However, a significant drawback of current state-of-the-art models is their high computational complexity, rendering them unsuitable for deployment in portable devices. This paper presents a compact fault diagnosis model that integrates a self-attention SqueezeNet architecture with a hybrid texture representation technique utilizing empirical mode decomposition (EMD) and a gammatone spectrogram (GS) filter. In the model, the dominant signal is first isolated from the audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD, and subsequently, the dominant signals are transformed into 2D texture maps using the GS filter. These generated texture maps feed as input into the modified self-attention SqueezeNet classifier, featuring reduced model width and depth, for training and validation. Different attention modules were tested in the paper, including the self-attention, channel attention, spatial attention, and convolutional block attention module (CBAM). The models were tested on the MIMII and ToyADMOS datasets. The experimental results demonstrated that the self-attention mechanism with SqueezeNet achieved an accuracy of 97% on the previously unseen MIMII and ToyADMOS datasets. Furthermore, the proposed model outperformed the SqueezeNet attention model with other attention mechanisms and state-of-the-art deep architectures, exhibiting a higher precision, recall, and F1-score. Lastly, t-SNE is applied to visualize the features of the self-attention SqueezeNet for different fault classes of both MIMII and ToyADMOS. Full article

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT). This method employs input block decomposition and a composite zero-padding approach to streamline memory bandwidth and computational complexity via optimized frequency-domain convolution and image reconstruction. By capitalizing on FFT’s inherent periodicity to augment frequency resolution, Split_ Composite facilitates weight sharing, curtailing both memory access and computational demands. Our experiments, conducted using the OpenSARShip-4 dataset, confirm that the Split_ Composite method upholds high recognition precision while markedly enhancing inference velocity, especially in the realm of large-scale data processing, thereby exhibiting exceptional scalability and efficiency. When juxtaposed with state-of-the-art convolution optimization technologies such as Winograd and TensorRT, Split_ Composite has demonstrated a significant lead in inference speed without compromising the precision of recognition. Full article

Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall efficiency of power resource utilization. However, existing models cannot accurately capture the nonlinear features of electricity data, leading to a decline in the forecasting performance. To relieve this issue, this paper designs an innovative load forecasting method, named Prophet–CEEMDAN–ARBiLSTM, which consists of Prophet, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the residual Bidirectional Long Short-Term Memory (BiLSTM) network. Specifically, this paper firstly employs the Prophet method to learn cyclic and trend features from input data, aiming to discern the influence of these features on the short-term electricity load. Then, the paper adopts CEEMDAN to decompose the residual series and yield components with distinct modalities. In the end, this paper designs the advanced residual BiLSTM (ARBiLSTM) block as the input of the above extracted features to obtain the forecasting results. By conducting multiple experiments on the New England public dataset, it demonstrates that the Prophet–CEEMDAN–ARBiLSTM method can achieve better performance compared with the existing Prophet-based ones. Full article

Methods for reducing power consumption in circuits of finite state machines (FSMs) are discussed in this review. The review outlines the main approaches to solving this problem that have been developed over the last 40 years. The main sources of power dissipation in CMOS circuits are shown; the static and dynamic components of this phenomenon are analyzed. The power consumption saving can be achieved by using coarse-grained methods common to all digital systems. These methods are based on voltage or/and clock frequency scaling. The review shows the main structural diagrams generated by the use of these methods when optimizing the power characteristics of FSM circuits. Also, there are various known fine-grained methods taking into account the specifics of both FSMs and logic elements used. Three groups of the fine-grained methods targeting FPGA-based FSM circuits are analyzed. These groups include clock gating, state assignment, and replacing look-up table (LUT) elements by embedded memory blocks (EMBs). The clock gating involves a separate or joint use of such approaches as the (1) decomposition of FSM inputs and (2) disabling FSM inputs. The aim of the power-saving state assignment is to reduce the switching activity of a resulting FSM circuit. The replacement of LUTs by EMBs allows a reduction in the power consumption due to a decrease in the number of FSM circuit elements and their interconnections. We hope that the review will help experts to use known methods and develop new ones for reducing power consumption. We think that a good knowledge and understanding of existing methods of reducing power consumption is a prerequisite for the development of new, more effective methods to solve this very important problem. Although the methods considered are mainly aimed at FPGA-based FSMs, they can be modified, if necessary, and used for the power consumption optimization of FSM circuits implemented with other logic elements. Full article