Face sketch synthesis is a technique aimed at converting face photos into sketches. Existing face sketch synthesis research mainly relies on training with numerous photo–sketch sample pairs from existing datasets. However, these large-scale discriminative learning methods will have to face problems, such as data scarcity and high human labor costs. Once the training data become scarce, their generative performance significantly degrades. In this article, we propose a one-shot face sketch synthesis method based on diffusion models. We optimize text instructions on a diffusion model using face photo–sketch image pairs. Then, the instructions derived through gradient-based optimization are used for inference. To simulate real-world scenarios more accurately and evaluate method effectiveness more comprehensively, we introduce a new benchmark named One-shot Face Sketch Dataset (OS-Sketch). The benchmark consists of 400 pairs of face photo–sketch images, including sketches with different styles and photos with different backgrounds, ages, sexes, expressions, illumination, and so on. For a solid out-of-distribution evaluation, we select only one pair of images for training at each time, with the rest used for inference. Extensive experiments demonstrate that the proposed method can convert various photos into realistic and highly consistent sketches in a one-shot context. Compared to other methods, our approach offers greater convenience and broader applicability. The dataset will be available at: https://github.com/HanWu3125/OS-Sketch .

Identifying the transmission status as line-of-sight (LOS) and non-line-of-sight (NLOS) is of importance for 3D Massive Multiple-Input Multiple-Output (MIMO) systems, which is one of the core technology to improve 5G New Radio (NR) capacity and spectral efficiency. If the identification could be as accurate as possible, the positioning systems and adaptive radio systems like cognitive radios can increase their performances significantly. This paper presents an improved algorithm for LOS/NLOS identification in 3D Massive MIMO systems. By fully considering the characteristics of the 3D MIMO channel, we formulate the identification problem as a binary hypothesis test by exploiting a statistic model based on time-space-frequency channel correlation. Compared to a previous study based on channel correlation, our simulation results show that the performance of the novel method is over 8.7% improved, and the error rate is as low as 3.23%. Besides, the effect of the number of antennas and taps in time domain on the performance of the improved method is discussed.

In multi-user multiple-input multiple-output (MU-MIMO) systems, user grouping methods have been proposed to improve network capacity. Most of the related papers focus on separating users according to their channel conditions and correlations between users. However, the packet size of users also has a significant impact on system performance, especially in Internet of Things (IoT) systems where there are lots of users with small-sized packets, and this is not investigated in those papers. Therefore, we propose a low complexity overlapping user grouping method considering packet size. On the premise of ensuring the rate and delay requirement of small-packet users, we increase the resources occupied by large-packet users to improve the system performance. To analyze the performance in more practical scenarios, we use 3D MIMO channel model in simulations. Compared with existing overlapping user grouping algorithms, our method achieves higher throughput and lower complexity order.

To satisfy the explosive growth of data in large-scale data centers, where redundant arrays of independent disks (RAIDs), especially RAID-5, are widely deployed, effective storage scaling and disk expansion methods are desired. However, a way to reduce the data migration overhead and maintain the reliability of the original RAID are major concerns of storage scaling. To address these problems, we propose a new RAID scaling scheme, H-Scale, to achieve fast RAID scaling via hybrid stripe layouts. H-Scale takes advantage of the loose restriction of stripe structures to choose migrated data and to create hybrid stripe structures. The main advantages of our scheme include: (1) dramatically reducing the data migration overhead and thus speeding up the scaling process, (2) maintaining the original RAID’s reliability, (3) balancing the workload among disks after scaling, and (4) providing a general scaling approach for different RAID levels. Our theoretical analysis show that H-Scale outperforms existing scaling solutions in terms of data migration, I/O overheads, and parity update operations. Evaluation results on a prototype implementation demonstrate that H-Scale speeds up the online scaling process by up to 60% under SPC traces, and similar improvements on scaling time and user response time are also achieved by evaluations using standard benchmarks.