The Cochlea, a spiral-shaped structure in the inner ear, plays a crucial role in the process of hearing by converting sound waves into electrical signals that the brain can interpret. This study introduces a cost-effective adaptation of 2D artificial Cochlea mathematical modeling using a planar approximation technique. The main novelty and contribution of our work is a method employs surface-based functions and is known as the Surface-Based Approximation Model of Cochlea (SBAMoC). By simplifying complex multiplication processes in nonlinear components, the SBAMoC reduces costs and enhances efficiency, making it suitable for FPGA implementation with minimal hardware requirements. The proposed model is evaluated in scenarios involving two-coupled oscillations and grid-based cochlear networks to better understand its performance. Through hardware synthesis on a Virtex-II board, the SBAMoC demonstrates improved efficiency and reduced computational expenses compared to the original model, achieving faster speeds and greater cost-effectiveness. In practical tests, the SBAMoC exhibits higher operational speeds and increased scalability, outperforming the original model by replicating accurate cochlear behaviors with minimal deviations. Specifically, the single SBAMoC implementation in our model achieves a speed boost of approximately 1.333 times compared to the original model (381.292 MHz vs. 286.029 MHz) and supports a greater number of fitted SBAMoCs (75 vs. 35), showcasing its superior efficiency and performance enhancements. In case of real-world applications, it can be considered for the development of more efficient and cost-effective cochlear implants, leading to improved hearing restoration solutions for individuals with hearing impairments. Also, the findings from this study could also be leveraged to enhance the design and implementation of signal processing systems in various audio and communication devices, paving the way for advanced audio processing technologies with increased efficiency and reduced hardware costs.

Deep learning has made significant advancements and breakthroughs in medical image recognition. However, the clinical reality is complex and multifaceted, with patients often suffering from multiple intertwined diseases, not all of which are equally common, leading to medical datasets that are frequently characterized by multi-labels and a long-tailed distribution. In this paper, we propose a method involving label decoupling and reconstruction (LDRNet) to address these two specific challenges. The label decoupling utilizes the fusion of semantic information from both categories and images to capture the class-aware features across different labels. This process not only integrates semantic information from labels and images to improve the model's ability to recognize diseases, but also captures comprehensive features across various labels to facilitate a deeper understanding of disease characteristics within the dataset. Following this, our label reconstruction method uses the class-aware features to reconstruct the label distribution. This step generates a diverse array of virtual features for tail categories, promoting unbiased learning for the classifier and significantly enhancing the model's generalization ability and robustness. Extensive experiments conducted on three multi-label long-tailed medical image datasets, including the Axial Spondyloarthritis Dataset, NIH Chest X-ray 14 Dataset, and ODIR-5K Dataset, have demonstrated that our approach achieves state-of-the-art performance, showcasing its effectiveness in handling the complexities associated with multi-label and long-tailed distributions in medical image recognition.

Multi-label image recognition aims to predict all objects present in an input image. A common belief is that modeling the correlations between objects is beneficial for multi-label recognition. However, this belief has been recently challenged as label correlations may mislead the classifier in testing, due to the possible contextual bias in training. Accordingly, a few of recent works not only discarded label correlation modeling, but also advocated to remove contextual information for multi-label image recognition. This work explicitly explores label correlations for multi-label image recognition based on a principled causal intervention approach. With causal intervention, we pursue causal label correlations and suppress spurious label correlations, as the former tend to convey useful contextual cues while the later may mislead the classifier. Specifically, we decouple label-specific features with a Transformer decoder attached to the backbone network, and model the confounders which may give rise to spurious correlations by clustering spatial features of all training images. Based on label-specific features and confounders, we employ a cross-attention module to implement causal intervention, quantifying the causal correlations from all object categories to each predicted object category. Finally, we obtain image labels by combining the predictions from decoupled features and causal label correlations. Extensive experiments clearly validate the effectiveness of our approach for multi-label image recognition in both common and cross-dataset settings.

The increasing number of objectives poses a great challenge upon many-objective optimization algorithms (MaOOAs) when solving many-objective optimization problems (MaOOPs), since it is rather difficult to obtain well-distributed solutions with tight convergence. To efficiently improve the ability of solving MaOOPs, this paper proposes a hierarchical clustering-based cooperative multi-population many-objective optimization algorithm (C2MP-MaOOA). Specifically, a hierarchical clustering-based population division strategy is proposed in C2MP-MaOOA, which is able to effectively optimize different regions of the Pareto front (PF) regardless of its shape, so as to maintain population diversity and accelerate convergence. Any single-objective optimizer can be applied in C2MP-MaOOA to optimize a subpopulation. To comprehensively evaluate the performance of C2MP-MaOOA, it was compared with eight state-of-the-art existing algorithms and two variants of C2MP-MaOOA on 63 MaOOPs selected from DTLZ, MaF, and WFG benchmark suites. The results indicate that C2MP-MaOOA has the best overall performance for each benchmark suite, which demonstrates that C2MP-MaOOA is quite competitive in solving MaOOPs.

Biological systems in case of real-time state and also large-scale simulation approach are interesting and challenge-based due to different aspects of nonlinear mathematical modeling that can describe the interactions of biological blocks. Thus, hardware circuit designing of these basic blocks in the Central Nervous System (CNS) can be an important field in case of achieving high performance neuromorphic system emulator. This paper presents a high-speed, low-cost, and efficient digital circuit for emulating the plausible calcium-dynamic-based model of astrocyte which has spontaneous oscillations. The nonlinear high-cost functions of the complex astrocyte model are reformulated using the power-2 based low-cost terms using optimized exhaustive search algorithm. Subsequently, the proposed model is simulated in case of validating the presented model and new optimized functions. Finally, the proposed model is physically realized in hardware case using Virtex 4 FPGA platform to test and validate final circuits. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. The proposed hardware consumes maximum 2% of the all resources of a Virtex 4 board. Additionally, timing analysis and synthesize report represent that the proposed model works in a high frequency of 371.56 MHz. Moreover, to validate the results of implementation, the proposed model is compared with the original model and other similar works in terms of accuracy, speed-up, and maximum number of implemented astrocyte.

With the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous trajectories hidden in the massive data has become one of the research hotspots. Anomalous trajectories should be detected and cleaned before the trajectory data can be effectively used. In this article, a Trajectory Evaluator by Sub-tracks (TES) for detecting VOT-based anomalous trajectory is proposed. Feature of Anomalousness is defined and described as the Eigenvector of classifier to filter Track Lets anomalous trajectory and IDentity Switch anomalous trajectory, which includes Feature of Anomalous Pose and Feature of Anomalous Sub-tracks (FAS). In the comparative experiments, TES achieves better results on different scenes than state-of-the-art methods. Moreover, FAS makes better performance than point flow, least square method fitting and Chebyshev Polynomial Fitting. It is verified that TES is more accurate and effective and is conducive to the sub-tracks trajectory data analysis.

Shadow detection and removal is a classic research in the field of image processing. This work aims to address the problem of shadow detection and removal for licence plate. The shadow detection and removal method based on inherent properties of licence plate is presented in this study. First, an S–V‐channel‐property‐based shadow detection algorithm is investigated to extract the background‐shaded area of licence plate. Afterwards, a shadow edge location algorithm is proposed to detect the complete shadow edge. Then, the shadow is removed on H, S, and V channels with different strategies, respectively. Also, a filter‐based median filtering algorithm is employed to remove the pseudo‐edge. Finally, the proposed method is verified through comparing with other generic shadow detection and removal methods quantitatively and qualitatively.

Vehicle type recognition is a common question in modern intelligent transportation system. Although many methods have been proposed in literatures, how to recognize the vehicle type in the case of small samples is still troubling. To solve the problem mentioned above, this paper presents a method for vehicle type recognition based on a two-stage strategy that includes data preprocessing phrase and training phrase. The main purpose of the first stage is to remove the background of the vehicle image to reduce the interference of redundant information on subsequent stage. In second stage, a concept of multi-scale fusion feature which integrates handcrafted features with learning-based feature is to put forward to describe the characteristics of vehicles. The combined features are input to Support Vector Machine with Racial Basis Function (RBF-SVM) to train the recognition model. The proposed method is tested on MVVTR and the recognition accuracy is up to 90.96%, which is better than other methods. It is verified that strong generalization ability can be achieved in the case of small samples by using the two- stage strategy.