Mixed-signal chips, serving as critical interfaces between analog and digital domains, are playing an increasingly important role in modern electronic systems. To accelerate manufacturing tests, adaptive testing has emerged as a promising approach that dynamically adjusts the test sequence, prunes redundant items, and detects defects at minimal cost. Existing studies predict the performance of devices under test (DUTs) from low-cost test items in current and prior stages with different predictors. However, a systematic analysis for selecting appropriate prediction pairs is still lacking. Consequently, a large number of DUTs are required for training, which increases the overall test time and reduces efficiency. To overcome the limitations, this study introduces a correlation-driven method for test-item selection, enabling accurate DUT classification in adaptive testing. Test items are recategorized into “leader,” “follower,” and “uncorrelated” groups based on their extra information gain cost (EIGC), and a backpropagation neural network (BPNN) regression model is used to predict the performance of the “follower” set. The nondominated sorting genetic algorithm is implemented for threshold setting and test-item categorization, considering both test time cost and accuracy. Experimental validation on a commercial mixed-signal chip demonstrates the effectiveness of the proposed method, achieving a defect escape rate as low as 1% with a 40% reduction in test time.

Performance testing of analog integrated circuits (ICs) is essential before deployment and real-world use. The conventional method for testing ICs’ specifications relied on complex test circuits and diverse stimuli with high time and hardware costs. To reduce costs, we propose a two-stage testing framework of analog ICs that employs ensemble learning technology. In the first stage, we explore the mapping of a single response, generated by the test circuit upon stimulation, to multiple target specifications for testing. In particular, the framework could include multiple types of responses to ensure the testing accuracy of each specification. In the second stage, we investigate the strategy of combining the different predictions to the final test results. The results of the simulation experiments indicate that, within the framework of indirect testing, the proposed method can fuse the predicted specifications from multiple responses to enhance the testing accuracy of each specification.

In this brief, we propose a deep learning-based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus. First, we apply a deep neural network (DNN) to establish the mapping from the response of the circuit under test (CUT) in each module to all specifications to be tested. Then, the required test modules are selected by solving a 0–1 integer programming problem. Finally, the predictions from the selected test modules are combined by a DNN to form the specification estimations. The simulation results validate the proposed approach in terms of testing accuracy and cost.

March algorithms are essential for detecting functional memory faults, characterized by their linear complexity and adaptability to emerging technologies. However, the increasing complexity of fault types presents significant challenges to existing fault detection models regarding analytical efficiency and adaptability. This article introduces the test primitive (TP), a unified notation that characterizes March test sequences through a novel methodology that decouples fault detection operations from sensitization states. The proposed TP achieves platform independence and seamless integration of fault models, supported by rigorous theoretical proofs. These proofs establish the fundamental properties of the TP in terms of completeness, uniqueness, and conciseness, providing a theoretical foundation that ensures the decoupling method reduces the computational complexity of March algorithm analysis to$O(1)$. This reduction is analogous to Karnaugh map simplification in digital logic while enabling millisecond-level automated analysis. Experimental results demonstrate that the proposed method significantly enhances both analyzable fault coverage (FC) and detection accuracy, thereby addressing critical limitations of existing fault detection models.

Ship detection plays a critical role in intelligent maritime applications including port management, marine monitoring and so on. Most existing ship detectors are trained on high-quality conventional-sized ship images under normal lighting conditions. However,low-quality images with poor lighting conditions often exist, where the features are difficult to be distinguished. Additionally, small ships with fewer pixels exhibit minimal appearance information and weak contour characteristics in night scenes, which are harmful to the multi-scale feature fusion. To address the above challenges, we propose an effective multi-scale feature fusion network for small ship detection in night scenes named MFF-YOLO. Specifically, we first design a night-friendly enhanced channel attention module, to better represent channel-dimensional features of small ships. In addition, we construct a multi-scale feature fusion architecture based on space and channel, to obtain richer semantic information of small ships in poor lighting conditions and further enhance the feature distinguishability. Finally, a series of experiments are implemented and corresponding results demonstrate the effectiveness and feasibility of our proposed method.

Traditional digital twin systems have focused on offering valuable insights into real-world systems through current ("what now") and forecast ("what next") interactive visualizations. The full potential for scenario exploration ("what if") remains challenging, especially when such scenarios require high performance computing (HPC) resources. In the case where such HPC resources are not dedicated or capacity is limited, bursting such compute to the cloud is an invaluable tool despite the many challenges involved. Such challenges include provisioning cloud hardware with the appropriate topology, network fabric, etc.; monitoring and controlling cost; and optimizing performance. This paper proposes a novel approach by integrating cloud-enabled workflows into digital twins using the Parallel Works HPC platform, enabling users to actively test "what if" scenarios to visualize and optimize outcomes. This work was done as part of NOAA’s Earth Observation Digital Twin (EO-DT) prototype effort.

With the rapid development of green wireless communication, the research on the energy efficiency characteristics of the downlink (broadcast channels, BC) is significant for a single-cell massive MIMO system. Based on the definition of energy efficiency (in bits/Joule), an optimized energy efficiency scheme is proposed in this paper. Before that, we derive the closed form lower capacity bounds of the achievable downlink sum-rate with the maximum-ratio transmission precoding. Also, we establish a power consumption model by taking into account the circuit power consumption, signal processing power consumption, cooling loss, in the practical situation. Under above scene, the optimization problem that maximizes energy efficiency is formulated and analyzed. We further give the global optimal closed form solution of the number of transmit antennas, for a given transmit power. The simulation results evaluate the performance of the energy efficiency as the number of transmit antennas increases, and reveal that the optimal number of transmit antennas can improve energy efficiency.

Due to adoption of a large amount of multipliers, the standard Farrow filter structure in digital resampling design has been the performance bottleneck of wideband signal demodulation. To improve demodulation performance, this paper proposes a full parallel resampling filter structure based on iterated short convolution algorithm (ISCA). The proposed design can effectively reduce the consumption of multipliers, by appropriately adding adders and delay elements. We implement the proposed design in a FPGA chip and test it by using a signal stream at a rate of 1.44 Giga-symbols per second (Gbps) under a four-parallel structure. The experimental results show that the consumption of multipliers and delay elements can be significantly reduced by about 36.8% and 57.2%, respectively, compared with conventional structure.

The feasibility of compressed sensing (CS) based waveform-reconstruction for data sampled from random equivalent sampling (RES) method is investigated. A novel measurement matrix motivated by the Whittaker-Shannon interpolation formula is proposed for this purpose. Experiments indicate that, for spectrally-sparse signal, the CS reconstructed waveform exhibits significantly higher signal-to-noise ratio (SNR) than that using the traditional time alignment method. A prototype realization of this proposed CS-RES method has been developed using off-the-shelf components. It is able to capture analog waveform at an equivalent sampling rate of 25 GHz while sampled at 100 MHz physically.

As WiMAX achieves increasing deployment, the large overhead in its uplink scheduling when providing real-time services has become a major challenge. In this study, the authors present effective, low-overhead scheduling algorithms for WiMAX uplink scheduling. The authors adaptively predict users' load and select a small set of active users to be served. This addresses the major source of overhead in WiMAX uplink scheduling: the Markovian arrival process information elements (IEs) and media access control layer (MAC) service data unit (SDUs) subheader overheads grow with the number of active users. The authors introduce additional novel techniques, including piggybacking, to reduce MAC overhead. The authors implement their algorithms and conduct extensive evaluations. The results show that their algorithms not only provide quality-of-service guarantees, but also substantially reduce the scheduling overhead compared with existing schemes.

This letter presents an adaptive denoising method based on the singular value decomposition (SVD). By incorporating a global subspace analysis into the scheme of local basis selection, the problems of previous adaptive methods are effectively tackled. Experimental results show that the proposed method achieves outstanding preservation of image details, and at high noise levels it provides improvements in both objective and subjective quality of the denoised image when compared to the state-of-the-art methods.

Recent research has shown that multiple-input and multiple-output (MIMO) techniques can be used with orthogonal frequency division multiplexing (OFDM) for wideband transmission to mitigate intersymbol interference and enhance system capacity. In this paper, we present training sequence design and channel estimation for MIMO-OFDM systems. The effectiveness of the developed approaches are demonstrated by computer simulation examples.