This paper presents approximate multipliers which are efficiently deployed on Field Programmable Gate Arrays (FPGAs) by using newly proposed approximate logic compressors at different levels of accuracy. Our approximate multiplier designs offer higher gains of power-delay-area products (PDAP) than those of the state-of-the-art works at comparable accuracies. Furthermore, in terms of delay, occupied area, and dynamic power dissipation, our designs are much better than Lookup Table based multiplier Intellectual Properties that are available on an FPGA. Particularly, our proposed 8-, 16-, and 32-bit multipliers can deliver PDAP gains up to 7.1×, 8.3×, and 5.0×, respectively. The effectiveness and applicability of our designs are also demonstrated by image processing applications such as image multiplication and sharpening. The experiments show that for the image sharpening, our 8×8 multipliers can deliver a good peak signal-to-noise ratio (PSNR) of 46.81 dB, a structural similarity index metric (SSIM) of 0.9989, and a dynamic power saving of up to 36.7% with regard to the exact multiplier. For the image multiplication, approximate 16×16 multipliers can offer a high PSNR of 80.25 dB, an SSIM of 1.0, and a dynamic power saving of up to 58.15%. From these demonstrations, the proposed multipliers are expected to be appropriate with high-performance and low-power error-resilient applications.

Deep neural networks (DNNs) have proven explicit contributions in making autonomous driving cars and related tasks such as semantic segmentation, motion tracking, object detection, sensor fusion, and planning. However, in challenging situations, DNNs are not generalizable because of the inherent domain shift due to the nature of training under the i.i.d. assumption. The goal of semantic segmentation is to preserve information from a given image into multiple meaningful categories for visual understanding. Particularly for semantic segmentation, pixel-wise annotation is extremely costly and not always feasible. Domain generalization for semantic segmentation aims to learn pixel-level semantic labels from multiple source domains and generalize to predict pixel-level semantic labels on multiple unseen target domains. In this survey, for the first time, we present a comprehensive review of DG for semantic segmentation. we present a comprehensive summary of recent works related to domain generalization in semantic segmentation, which establishes the importance of generalizing to new environments of segmentation models. Although domain adaptation has gained more attention in segmentation tasks than domain generalization, it is still worth unveiling new trends that are adopted from domain generalization methods in semantic segmentation. We cover most of the recent and dominant DG methods in the context of semantic segmentation and also provide some other related applications. We conclude this survey by highlighting the future directions in this area.

Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include a requirement for a patient to stay one or more nights in the lab wearing uncomfortable sensors and wires. To avoid the inconvenience caused by the multiple channels, we aimed to develop a deep learning model for use in clinical decision support systems (CDSSs) and combined convolutional neural networks and a transformer for the supervised learning of three classes of sleep stages only with single-channel EEG data (C4-M1). The data for training, validation, and test were derived from 1590, 341, and 343 polysomnography recordings, respectively. The developed model yielded an overall accuracy of 91.4%, comparable with that of human experts. Based on the severity of obstructive sleep apnea, the model’s accuracy was 94.3%, 91.9%, 91.9%, and 90.6% in normal, mild, moderate, and severe cases, respectively. Our deep learning model enables accurate and rapid delineation of three-class sleep staging and could be useful as a CDSS for application in real-world clinical practice.

Study Objectives: Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments. Methods: All individually exported European data format files containing raw signals were converted into images with an annotation file, which contained the demographics, diagnoses, and sleep statistics. An image-based DL model for automatic sleep staging was developed, compared with a signal-based model, and validated in an external dataset Results: We constructed 10253 image-based PSG datasets using a standardized format. Among these, 7745 diagnostic PSG data were used to develop our DL model. The DL model using the image dataset showed similar performance to the signal-based dataset for the same subject. The overall DL accuracy was greater than 80%, even with severe obstructive sleep apnea. Moreover, for the first time, we showed explainable DL in the field of sleep medicine as visualized key inference regions using Eigen-class activation maps. Furthermore, when a DL model for sleep scoring performs external validation, we achieved a relatively good performance. Conclusions: Our main contribution demonstrates the availability of a standardized image-based dataset, and highlights that changing the data sampling rate or number of sensors may not require retraining, although performance decreases slightly as the number of sensors decreases.

Face presentation attack detection (PAD) is essential for ensuring the security and reliability of face recognition systems by preventing unauthorized access through spoofing attempts. Attackers can exploit various methods, such as printed photos, video replays, paper masks, 3D masks, or makeup, to imitate a legitimate user’s biometric traits. In this paper, we propose an enhanced face PAD solution that leverages the deformable convolutional layer within the MobileNetV2 architecture to improve detection accuracy. By replacing the standard convolution layer with a Deformable ConvNets V2, the proposed model adapts dynamically to spatial distortions, capturing more detailed and robust features for effective face PAD. Extensive experiments on the Replay-Attack, Replay-Mobile, ROSE-Youtu, OULU-NPU, and SiW-Mv2 datasets validate the superiority of the proposed approach. The method achieves a half total error rate (HTER) of 0.0% on both the Replay-Attack and Replay-Mobile datasets, 1.26% on ROSE-Youtu, 4.88% on SiW-Mv2, and an ACER of 0.208% on OULU-NPU, outperforming several existing methods. These results highlight the robustness and effectiveness of our approach in safeguarding face recognition systems against presentation attacks.