The development of deep learning has achieved great success in object detection, but small object detection is still a difficult and challenging task in computer vision. To address the problem, we propose an improved single-shot multibox detector (SSD) using enhanced feature map blocks (SSD-EMB). The enhanced feature map block (EMB) consists of attention stream and feature map concatenation stream. The attention stream allows the proposed model to focus on the object regions rather than background owing to channel averaging and the effectiveness of the normalization. The feature map concatenation stream provides additional semantic information to the model without degrading the detection speed. By combining the output of these two streams, the enhanced feature map, which improves the detection of a small object, is generated. Experimental results show that the proposed model has high accuracy in small object detection. The proposed model not only achieves good detection accuracy, but also has a good detection speed. The SSD-EMB achieved a mean average precision (mAP) of 80.4% on the PASCAL VOC 2007 dataset at 30 frames per second on an RTX 2080Ti graphics processing unit, an mAP of 79.9% on the VOC 2012 dataset, and an mAP of 26.6% on the MS COCO dataset.

Background: Deep learning has significantly advanced medical image analysis, particularly in semantic segmentation, which is essential for clinical decisions. However, existing 3D segmentation models, like the traditional 3D UNet, face challenges in balancing computational efficiency and accuracy when processing volumetric medical data. This study aims to develop an improved architecture for 3D medical image segmentation with enhanced learning strategies to improve accuracy and address challenges related to limited training data. Methods: We propose ES-UNet, a 3D segmentation architecture that achieves superior segmentation performance while offering competitive efficiency across multiple computational metrics, including memory usage, inference time, and parameter count. The model builds upon the full-scale skip connection design of UNet3+ by integrating channel attention modules into each encoder-to-decoder path and incorporating full-scale deep supervision to enhance multi-resolution feature learning. We further introduce Region Specific Scaling (RSS), a data augmentation method that adaptively applies geometric transformations to annotated regions, and a Dynamically Weighted Dice (DWD) loss to improve the balance between precision and recall. The model was evaluated on the MICCAI HECKTOR dataset, and additional validation was conducted on selected tasks from the Medical Segmentation Decathlon (MSD). Results: On the HECKTOR dataset, ES-UNet achieved a Dice Similarity Coefficient (DSC) of 76.87%, outperforming baseline models including 3D UNet, 3D UNet 3+, nnUNet, and Swin UNETR. Ablation studies showed that RSS and DWD contributed up to 1.22% and 1.06% improvement in DSC, respectively. A sensitivity analysis demonstrated that the chosen scaling range in RSS offered a favorable trade-off between deformation and anatomical plausibility. Cross-dataset evaluation on MSD Heart and Spleen tasks also indicated strong generalization. Computational analysis revealed that ES-UNet achieves superior segmentation performance with moderate computational demands. Specifically, the enhanced skip connection design with lightweight channel attention modules integrated throughout the network architecture enables this favorable balance between high segmentation accuracy and computational efficiency. Conclusion: ES-UNet integrates architectural and algorithmic improvements to achieve robust 3D medical image segmentation. While the framework incorporates established components, its core contributions lie in the optimized skip connection strategy and supporting techniques like RSS and DWD. Future work will explore adaptive scaling strategies and broader validation across diverse imaging modalities.

This brief proposes a new signal detection method called QR ordered successive interference cancellation (OSIC) with candidates (QOC) method for spatially multiplexed multiple-input-multiple-output (MIMO) systems. By using the OSIC algorithm and the maximum-likelihood (ML) metric, the proposed method achieves near-ML performance without requiring a large number of candidates. Although the proposed method can be used for both hard and soft decoding systems, it is particularly useful for soft decoding systems since the log-likelihood-ratio (LLR) values for all the bits can efficiently be computed without using LLR estimation. The proposed method is also suitable for very large scale integration (VLSI) implementation since it leads to a fixed throughput system. A QOC detector for a 4 times4 16-quadrature-amplitude-modulation (16-QAM) MIMO system has been designed and synthesized with a 0.13-mum complimentary metal-oxide-semiconductor (CMOS) technology. The implementation results show that the proposed detector reduces the hardware complexity by 73% relative to a K-best detector.

In this paper, we consider a $2\times 2$ space-time line coded (STLC) system having two-transmit and two-receive antennas. To improve the secrecy rate of the STLC system, in which an illegitimate receiver eavesdrops the information delivered from the STLC transmitter to the STLC receiver, we propose an artificial noise (AN) injection method. By exploiting the STLC structure, a novel AN for the STLC is designed and its optimal power loading factor is derived. Numerical results verify that the proposed secure STLC systems with the designed AN injection and the power control method can significantly improve the secrecy rate compared to the conventional STLC systems. It is observed that the proposed method is more effective if there is a significant gap between the main-channel and the eavesdropper-channel gains.

The generative adversarial network (GAN) has demonstrated superb performance in generating synthetic images in recent studies. However, in the conventional framework of GAN, the maximum resolution of generated images is limited to the resolution of real images that are used as the training set. In this paper, in order to address this limitation, we propose a novel GAN framework using a pre-trained network called evaluator. The proposed model, higher resolution GAN (HRGAN), employs additional up-sampling convolutional layers to generate higher resolution. Then, using the evaluator, an additional target for the training of the generator is introduced to calibrate the generated images to have realistic features. In experiments with the CIFAR-10 and CIFAR-100 datasets, HRGAN successfully generates images of 64 × 64 and 128 × 128 resolutions, while the training sets consist of images of 32 × 32 resolution. In addition, HRGAN outperforms other existing models in terms of the Inception score, one of the conventional methods to evaluate GANs. For instance, in the experiment with CIFAR-10, a HRGAN generating 128 × 128 resolution demonstrates an Inception score of 12.32, outperforming an existing model by 28.6%. Thus, the proposed HRGAN demonstrates the possibility of generating higher resolution than training images.

When imaging brain vasculature with optical coherence tomography angiography (OCTA), volumetric analysis of cortical vascular networks in OCTA datasets is frequently challenging due to the presence of artifacts, which appear as multiple-scattering tails beneath superficial large vessels in OCTA images. These tails shadow underlying small vessels, making the assessment of vascular morphology in the deep cortex difficult. In this work, we introduce an image processing technique based on mean subtraction of the depth profile that can effectively reduce these tails to better reveal small hidden vessels compared to the current tail removal approach. With the improved vascular image quality, we demonstrate that this simple method can provide better visualization of three-dimensional vascular network topology for quantitative cerebrovascular studies.