Single-angle plane wave (SAPW) ultrasound imaging has gained significant attention in ultrafast and wearable ultrasound systems due to its high frame rate and hardware efficiency. However, the lack of transmit diversity leads to severe degradation in image quality, limiting its clinical utility. In contrast, Coherent Plane Wave Compounding (CPWC) improves spatial resolution and contrast by aggregating data from multiple transmission angles, but at the expense of a substantially reduced frame rate. To overcome this trade-off, we propose TF-Fusion, a novel Time-Frequency Domain Feature Fusion framework that enhances SAPW imaging by jointly exploiting both time- and frequency-domain features extracted from raw in-phase/quadrature (IQ) data. Specifically, our model utilizes a dual-branch encoder to learn compact and complementary representations from each domain, followed by a learnable fusion module that adaptively integrates the multi-domain information. This design facilitates effective noise and artifact suppression while preserving fine anatomical structures. We evaluate TF-Fusion on two public benchmarks—PICMUS and CUBDL—and demonstrate that our method significantly improves image resolution and contrast, achieving performance comparable to multi-angle CPWC methods. Notably, TF-Fusion maintains the high temporal resolution of SAPW, making it well-suited for real-time and resource-constrained ultrasound imaging scenarios.

Transformers have gained widespread usage in multivariate time series (MTS) forecasting, delivering impressive performance. Nonetheless, these existing transformer-based methods often neglect an essential aspect: the incorporation of uncertainty into the predicted series, which holds significant value in decision-making. In this paper, we introduce a Transformer-Modulated Diffusion Model (TMDM), uniting conditional diffusion generative process with transformers into a unified framework to enable precise distribution forecasting for MTS. TMDM harnesses the power of transformers to extract essential insights from historical time series data. This information is then utilized as prior knowledge, capturing covariate-dependence in both the forward and reverse processes within the diffusion model. Furthermore, we seamlessly integrate well-designed transformer-based forecasting methods into TMDM to enhance its overall performance. Additionally, we introduce two novel metrics for evaluating uncertainty estimation performance. Through extensive experiments on six datasets using four evaluation metrics, we establish the effectiveness of TMDM in probabilistic MTS forecasting.

Multi-class unsupervised anomaly detection aims to create a unified model for identifying anomalies in objects from multiple classes when only normal data is available. In such a challenging setting, widely used reconstruction-based networks persistently grapple with the "identical shortcut" problem, wherein the infiltration of abnormal information from the condition biases the output towards an anomalous distribution. In response to this critical challenge, we introduce a Vague Prototype-Oriented Diffusion Model (VPDM) that extracts only fundamental information from the condition to prevent the occurrence of the "identical shortcut" problem from the input layer. This model leverages prototypes that contain only vague information about the target as the initial condition. Subsequently, a novel conditional diffusion model is introduced to incrementally enhance details based on vague conditions. Finally, a Vague Prototype-Oriented Optimal Transport (VPOT) method is proposed to provide more accurate information about conditions. All these components are seamlessly integrated into a unified optimization objective. The effectiveness of our approach is demonstrated across diverse datasets, including the MVTec, VisA, and MPDD benchmarks, achieving state-of-the-art results.

Network coding (NC) is an information transmission technology that encodes and transmits data packets on a network. NC can be applied to the Internet of Things (IoT) and can effectively improve the data transmission quality and throughput of the IoT. The complexity of data packets encoding and decoding is an important problem in NC applications. The cubic permutation polynomial (CPP) can better optimize the data packets participating in NC, reduce the computational complexity of data packet encoding/decoding, and improve the data packet transmission bandwidth and data throughput of the IoT. This article proposes a CPP-based sliding window NC algorithm in IoT (CPP-SWNC). In this article, first, the fusion theory of CPP and sliding window (SW) was discussed. Second, the theory that optimize the size of SWs was demonstrated. Third, an analysis was conducted from the perspective of mathematical theory. Finally, simulation experiments were conducted to evaluate the performance of the CPP-SWNC algorithm using network simulation (NS) software, mainly by comparing and analyzing the network throughput, average packet encoding overhead, and packet decoding delay. The simulation results show that the CPP-SWNC algorithm can improve the performance and data packet transmission quality of the IoT, and can further improve the application field of the IoT.

Network coding (NC) consists of intelligently aggregating data packets by means of binary or linear combinations. NC has been considered as one of the possible solutions to the current low throughput, energy consumption, packet loss, non-connectivity and mobility support problems in MANETs. Sliding-window Network Coding is a variation of NC that is an addition to TCP/IP and improves the throughput of TCP on wireless networks. In this paper, we propose a Variable Length Sliding Window-based Network Coding algorithm in MANETs (VLSW-NC). The performance of this VLSW-NC is studied using NS2 and evaluated in terms of the throughput, packet loss probability, and decoding delay when packet is transmitted. The simulations results shows that the VLSW-NC achieved with our proposition can significantly improve the network performance and reliability.

Due to the ignorance of the relation between different collisions, traditional medium access control protocols cant achieve their anticipated collision resolution results. This paper proposes a novel way to distribute MAC accesses in MANETs based on collision classification approach and modified backoff schemes. The idea is to avoid cross collisions through dynamically allocated elementary windows which prevent intersection of spaces in which the backoff counters are decremented. It develops a policy to select an appropriate backoff value as a function of retransmission attempts, observed number of collisions etc. to minimize repeated collisions, and thus improves throughput, delay jitter, as well as fairness. The performance improvement is demonstrated convincingly using a wide range of simulations.

Traditional backoff algorithms in distributed contention-based MAC protocols adopt contention window scheme for collision resolution. Collided nodes are redistributed in extended windows to avoid further collision. However, collision may still occur due to intersecting window ranges. In this paper, we propose an effective backoff algorithm featuring collision-free collision classification resolution (CF-CCR) based on collision classification model to resolve different collisions with different manners. Under the schedule of CF-CCR, the network will ultimately converges to a collision free state, in which nodes access channel sequentially with no collision occurred. The simulation results show that CF-CCR outperforms IEEE 802.11 DCF in collision times, throughput, delay and fairness.

Backoff algorithms in traditional distributed contention-based MAC protocols concentrate on adjusting contention window size to resolve collision. However, collision may still occur as long as these windows intersect with each other. In order to solve this problem, we put forward a collision classification model to classify collisions into cross collision and intra collision and suggest to solve them with different manners. Based on this model, we propose collision classification resolution (CCR) algorithm to dynamically allocate discrete distribution windows for collided nodes to avoid cross collision, and the intra collision is resolved with an appropriate distribution window size to achieve a tradeoff between delay and collision probability. The extensive simulations performed demonstrate that compared to IEEE 802.11 DCF, CCR algorithm consistently excels, in terms of collision times, throughput, delay and fairness.

A Mobile Ad hoc NETwork (MANET) is an autonomous system of mobile nodes connected by wireless links. There is no static infrastructure such as base station as that was in cell mobile communication. Due to the dynamic nature of the network topology and restricted resources, quality of service (QoS) and multicast routing in MANET is a challenging task. In this paper, we present an entropy-based model to support QoS multicast routing optimization algorithm in mobile ad hoc networks (EQMOA). The basic motivations of the proposed modeling approach stem from the commonality observed in the location uncertainty in mobile ad hoc wireless networks and the concept of entropy. The simulation results demonstrate that the proposed approach and parameters provide an accurate and efficient method of estimating and evaluating the route stability in dynamic mobile networks.