This paper presents a selective fixed-filter active noise control (ANC) system incorporating a compensation filter, based on a modified error filtered-x discrete cosine transform least mean square (Fx-DCT-LMS) algorithm. In the proposed framework, a compensation filter is cascaded with a fixed noise control filter, which is selected using a convolutional neural network (CNN). Adaptive signal processing is then applied to compensate for the discrepancy between the fixed filter and the optimal solution. The sliding discrete cosine transform (SDCT) enables filter selection using spectrograms generated from arbitrary-length sample windows, which serve as input to the CNN. Notably, since the proposed method utilizes the DCT to compute the spectrogram, it significantly reduces computational complexity compared to conventional DFT-based spectrogram generation. Moreover, by employing the DCT-LMS algorithm, the compensation filter achieves faster convergence than the conventional filtered-x normalized LMS (Fx-NLMS) algorithm. Simulation results using real-world impulse responses demonstrate that the proposed system can achieve up to 10 dB of noise reduction under varying noise conditions.

Recommender systems have become more crucial for informed service consumption, product selection, and decision-making in the era of overabundant information and the digitized economy. Session-based systems have emerged in recent years as a new paradigm for recommender systems. Although session-based recommenders have been extensively investigated, there are currently no unified issue statements for them nor detailed explanations of their characteristics and difficulties. In this paper, deep learning with optimal feature selection approaches are used for effective feature selection and classification. Different data related to product reviews and movie reviews are considered as input for this suggested approach. Initially, these datasets are given to the count vectorizer for converting the “message” column’s text into numbers. These converted raw data’s are pre-processed utilizing similarity based data filling, min-max normalization and fuzzy c-means clustering to fill the absent values, standardization and to reduce the redundant data present in the dataset. Then, the features from pre-processed data are extracted. Aquila Optimization based approach is employed in the suggested method to decrease the number of resources required to describe a large set of data. Finally, a hybrid LSTM-SVM classifier is utilized for classification purpose. In this model, the softmax unit of the LSTM is substituted through SVM to predict the five different classes based on the customers reviews. The valuation outcomes shows that the suggested approach achieves 95%, 96%, 97% of accuracy, 90%, 91%, 93% of precision, 95%, 88%, 95% of specificity rate for three various datasets. As a result, the recommended strategy is the greatest option for a successful recommendation system.

The commutative property has been an essential characteristic in the development of public-key cryptosystems. There are essentially only two kinds of commutative polynomials: monomials and Chebyshev polynomials. By leveraging the commutative property of them, efficiently implementable public-key cryptosystems over the residue class ring ℤ_2^k have been introduced; unfortunately, however, they can be broken. Although commutative polynomials with two variables could be potential candidates for a public-key cryptosystem over the ring, the characteristics of these polynomials should be rigorously investigated. In this study, we analyzed several properties of commutative polynomials with two variables over ℤ²_2^k. More precisely, the degree period and the condition for permutation polynomials are discussed theoretically and verified experimentally. Based on the derived properties, a security analysis of a key exchange protocol using the commutative polynomials is also discussed.

In the case of distributed power source fault crossing, traditional methods have limited negative sequence voltage suppression effect and are difficult to reduce the negative sequence voltage to the ideal level, which affects the stability and safety of microgrid operation. Therefore, an improved method for on-site feeder automation considering distributed power source fault crossing is proposed. This method deeply analyzes the impact of distributed power access on feeder automation, sets anti islanding protection values for fault crossing situations, and preliminarily improves the feeder automation structure. To further optimize the effect, an improvement strategy based on regional numbering is proposed, which divides the range of power grid areas connected to distributed power sources, assigns logical numbering and adjusts logical operation time, and achieves the coordinated optimization of power grid fault timing coordination logic and anti islanding protection setting strategy. The experimental results show that this method can effectively suppress the negative sequence voltage connected to the distributed power microgrid, and approach the ideal level of negative sequence voltage; At the same time, the ratio error of the distance between the fault point and the protected end to the total length of the line is less than 0.01, the total exit time is controlled within 40 ms, and the risk of fault rejection/misoperation is less than 0.5%. Significantly improved the stability and fault handling capability of the power grid, providing reliable technical support for the intelligent management of the power system.

Greedy-based approaches for solving nonoverlapping, unordered, type-2 square jigsaw puzzles in the literature, primarily consider single-side compatibility, which increases the number of false-positive candidate neighbors. In the assembly phase, these solvers generally resolve the issue by considering all possible combinatorial combinations of the candidate neighbors to determine an optimal match, increasing the solver’s computational complexity. To address the same, this paper proposes an efficient greedy asymmetric assembly strategy with two-side verification to solve square jigsaw puzzles. More precisely, the proposed greedy strategy constructs nonoverlapping blocks via unambiguous jigsaw neighbors, such that at least two sides are always connected to the same block. The idea is to reconstruct the puzzle by optimally growing the subset of the local optimal solutions. The implementation leverages a disjoint set data structure to grow and merge blocks efficiently, achieving a space complexity of O(N) and an amortized time complexity of O(N log₂N). Extensive experimental evaluations validate the solver’s effectiveness across standard datasets via direct, neighbor, and perfect compatibility matrices. Furthermore, the algorithm demonstrates its versatility in handling diverse type-2 scenarios, including small puzzle sizes, missing pieces, and mixed puzzles. Our method achieves near-perfect precision in the construction of initial jigsaw blocks, with more than 74% sides of the total jigsaw pieces, as demonstrated by experimental analysis.

Balance control and high-speed motion present significant challenges for unicycle robots, particularly when implementing stable control under low-power actuator constraints. This paper investigates balance and locomotion problems for unicycle robots with low-power flywheels in uncertain environments. Considering the limitations of existing control methods in disturbance rejection, operational speed, and steering stability, we propose a robust control framework with high uncertainty-handling ability. The system overcomes low-power constraints and enhances disturbance rejection performance by introducing center-of-mass velocity estimation, strategic under-compensation techniques, and a priority-based power allocation algorithm. Then, by decoupling the robot dynamics into three axes, we propose a comprehensive control scheme integrating delay compensation and lightweight torque control without current detection. Experiments under various conditions confirm the effectiveness of our approach in achieving stable operation at speeds up to 1 m/s and diverse movement capabilities. We won the first prize in China’s National College Student Intelligent Vehicle Competition. See video on https://youtu.be/8UNbX0LAHjY.

Frequency-hopping multiple access (FHMA) systems provide advantages in reducing interference and enhancing security, but multi-access interference (MAI) remains a challenge. This letter presents new theoretical bounds for the maximum periodic Hamming correlation (PHC) and maximum periodic partial Hamming correlation (PPHC) within the low-hit-zone (LHZ) of multi-timeslot wide-gap frequency-hopping sequence (MTWGFHS) sets. Our results extend LHZ-FHS theory and address gaps in designing interference-resilient FHMA systems, contributing to more secure and robust frequency-hopping communication.