Acoustic wave detection techniques like ultrasound (US) and photoacoustic (PA) tomography are widely used in biomedical imaging but often require expensive transducer arrays and complex setups for 3D imaging. To overcome these challenges, recent research has explored using an ergodic relay (ER) with a single-element transducer. This approach improves imaging speed at low cost and reduces complexity. This study presents a dual-mode system that utilizes an ER for single-shot, 3D PA and US imaging with a single-element detector. Each ultrasonic or optical excitation pulse generates a 1D signal, which is then used to reconstruct a 3D image in US or PA mode. While the PA mode provides optical absorption-based contrast, the US mode offers complementary acoustic scattering-based contrast. We demonstrate the system's capabilities through in vivo imaging of blood vessels and skin structure in human hands. The system is non-invasive, label-free, and ultrafast, enabling 4D imaging with simplified hardware requirements.

Optical-resolution photoacoustic microscopy (OR-PAM) can visualize cellular-level wavelength-dependent optical absorption with high resolution and sensitivity. However, the imaging speed of OR-PAM has been limited by the laser repetition rate due to the point-by-point scanning of a focused laser beam. To overcome this limitation, we propose multifocal optical-resolution photoacoustic microscopy (MOR-PAM) with a single-element ultrasonic transducer, leveraging the diffractive optical element (DOE) and a custom-designed encoding acoustic mask. The DOE generates 8 focal spots of $3~\mu $ m diameter. The acoustic mask was designed to encode photoacoustic signals from different focal spots. MOR-PAM achieved an 8-fold increase in imaging speed compared to conventional OR-PAM with the same laser repetition rate. We demonstrated the MOR-PAM using a 266 nm laser at 10 KHz, providing solutions for rapid OR-PAM beyond the laser repetition rate in a cost-effective way. The proposed method can be applied to versatile OR-PAM configurations and enable new applications where high-speed imaging is critical.

We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at $256\times 256$, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-experts policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We release all the code, models, and results at: https://4kagent.github.io.

Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise fluorescence microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first real microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.

The property of changing resistance according to applied currents of memristors makes them candidates for emulating synapses in artificial neural networks. In this paper, we introduce a memristive synapse design into neural network circuits. Combined with modified integrate-and-fire (I&F) complementary metal-oxide-semiconducter (CMOS) neurons, the memristive neural network shows similarities to its biological counterpart, in respect of biologically realistic, current-controlled spikes and adaptive synaptic plasticity. Then, the spike-rate-dependent plasticity (SRDP) of the synapse, an extended protocol of the Hebbian learning rule, is originally implemented by the circuit. And some advanced neural activities including learning, associative memory and forgetting are realized based on the SRDP rule. These activities are comprehensively validated on a neural network circuit inspired by famous Pavlov's dog-experiment with simulations and quantitative analyses.

This paper investigates the problem of exponential lag synchronization control of memristive neural networks (MNNs) via the fuzzy method and applications in pseudorandom number generators. Based on the knowledge of memristor and recurrent neural networks, the model of MNNs is established. Then, considering the state-dependent properties of memristor, a fuzzy model of MNNs is employed to provide a new way of analyzing the complicated MNNs with only two subsystems, and update laws for the connection weights of slave systems and controller gain are designed to make the slave systems exponentially lag synchronized with the master systems. Two examples about synchronization problems are presented to show the effectiveness of the obtained results, and an application of the obtained theory is also given in the pseudorandom number generator.

Deployment of relay nodes (RNs) in cellular orthogonal frequency division multiple access (OFDMA) systems provides an effective solution to increase high data rate coverage and improve cell throughput. However, a challenging issue is that additional interferences caused by RNs may substantially compromise the performance gain if no measure is taken. In this study, the authors address the problem of interference coordination in relay-aided cellular OFDMA systems, aiming at exploiting the benefits of RNs while minimising the negative effects of interferences introduced. The authors first analyse the possible interference scenarios in multi-cell systems. Based on the insights into their analysis, the authors propose resource allocation with interference coordination (RAIC) scheme for cellular OFDMA systems. RAIC selectively perform one of three resource allocation algorithms according to the offered traffic load in the system, to mitigate interferences and thus enhance system throughput. The authors conduct intensive simulation experiments based on the model with realistic broadband channel propagation conditions. Numerical results demonstrate that their proposed RAIC can effectively improve system throughput compared with the resource allocation schemes without appropriate interference coordination.

This paper addresses the problem of interference coordination in relay-aided cellular OFDMA systems, aiming at exploiting the benefits of RNs while minimizing the negative effects of interferences introduced. We first analyze the possible interference scenarios in multi-cell OFDMA systems. Based on the insights in our analysis, we propose an Integrated Interference Coordination Scheme (IICS) for cellular OFDMA systems. IICS consists of two phases, each performing a resource allocation algorithm, to mitigate interferences and thus enhance system throughput. We conduct simulation experiments based on the model with realistic broadband channel propagation conditions. Numerical results show that our proposed IICS can effectively improve system throughput compared with the resource allocation schemes without adequate interference coordination.

Link duration is a main factor in determining the transmission capacity between two encounter nodes in Delay Tolerant Networks (DTNs). Existing research on DTN transmission capacity usually assumes that a node is able to immediately discover the nodes which move into its transmission range. Under this assumption, the link duration in DTNs is determined by the moving speed and transmission distance of encounter nodes. However, this assumption could be invalid in realistic DTNs where the nodes use a contact probing protocol to detect their neighbors. In this paper, we investigate the impact of contact probing mechanisms on link duration, and thus the transmission capacity of DTNs. We first derive the probability distribution of link duration, and analyze the impact of contact probing mechanisms on the total link durations. Based on that, we derive the theoretical node throughput and energy consumption, and explore their tradeoff. In addition, we provide an approach to compute the optimal contact probing frequency under energy limitation and adjust the probing frequency according to the node encountering rate. Simulation experiments based on simulator The ONE validates the correctness and the accuracy of our analytical model.

Relay is used in LTE-Advanced cellular networks to assist eNB(evolved Node Base-station) for coverage extension and throughput enhancement. Compared to eNB, relay is advantageous on both equipment cost and site cost. How to deploy relays to maximize the performance gain with a reasonable cost subject to a number of system constraints such as interference caused and backhaul subframe allocation is an important yet challenging issue. In this paper, we provide a solution for cost-efficient deployment of relays in LTE-A cellular networks. We establish a mathematical model to analyze the tradeoff between the deployment cost and the cell performance gain, and the tradeoff between overall cell performance and satisfaction level for individual users. Our interesting findings in this paper include: (1) the best cost-efficient number of relays in a cell is limited to a reasonable range between 7 and 11 for static relay deployment; (2) when progressive deployment is considered, the optimal number of relays decreases to 4 to achieve tradeoff gain between overall cell capacity and average user satisfaction level.

Due to the mass-market of file sharing, majority of existing P2P systems are based on unstructured overlay networks, where P2P search still remains challenging issues. In this paper, we propose a small-world based semantic search architecture in P2P Networks, called CommuSearch, which has three distinguished features: 1) grouping peers into hierarchical class by exploiting heterogeneity; 2) constructing overlapped semantic communities according to small-world characteristics; and 3) parallel and hierarchical routing based on results caching. We conduct extensive simulation experiments and the numerical results show that the performance of CommuSearch substantially outperforms existing architectures in terms of average search latency, search success ratio and recall ratio, while the overhead incurred for maintaining the community is reasonably low.

Cooperative relay has been recognized as a promising approach for improving transmission efficiency and error performance in wireless networks. A number of realizations of cooperative relay communications, such as Amplify-and-Forward (AF), Decode-and-Forward (DF), have been proposed and the capacity gain of these replay approaches has been widely investigated. In this paper, we consider a cooperative relay approach using fountain codes, called Fountain-Coding-and-Forward (FCF) and derive its upper bound of the information theoretic capacity in a general 3-node relay channel model. Numerical results reveal that the upper bound of FCF is higher than that of AF, DF and conventional direct transmission. This result suggests that cooperative relay in conjunction with fountain codes can potentially provides considerable benefits in terms of transmission capacity for reliable data transmission in wireless networks.

The increasing capabilities of mobile devices, and advances in wireless networking technologies enable mobile computing systems to introduce more intelligent behaviors to support their services such as spatial navigation for drivers. Mobile applications should be able to be aware of, and adapt to environment changes to provide more customized services to their users. Building mobile applications by specifying built-in adaptation mechanisms would be inefficient, error-prone, and lack of flexibility. This research proposes a context adaptation layer (CAL) which incorporates current environment contexts and user goal contexts, to generate a holistic adaptation view at CAL and therefore provide a deliberative guide to the service adaption in the separate service adaptation layer (SAL). The system model is validated through a dynamic spatial routing application in urban area.

The integration of Wireless mesh networks (WMNs) with other networks (e.g. Internet, cellular, IEEE 802.11, sensor networks, etc.) requires an effective and robust congestion control algorithm to maintain network stability and achieve a high network resource utilization. In this paper, we address the problem of congestion control for WMNs with multi-channel MAC. We formulate the design of congestion control mechanism in such a system as a optimization problem of utilization maximization subject to time constraint. We first derive an end-to-end algorithm as the solution. Based on that, we develop a distributed adaptive algorithm with hop-by-hop congestion information feedback and prove its convergence. The numerical results based on ns-2 simulations validate the stability and demonstrate that the hop-by-hop congestion control algorithm significantly outperforms the end-to-end algorithm in terms of queue length.

In this paper we address the issue of cross-layer design in transport layer enhancement mechanisms, where the data-link and transport layer information is shared with each other, in order to provide improved system throughput of the wireless cellular network. We propose a Cross-layer Transport Layer Enhancement mechanism with name of XST (Cross-layer Snoop protocol for TCP), which is based on Snoop protocol combined with the forced duplicate acknowledgement congestion control strategy in wireless cellular networks. XST employs the cross-layer design, working at the data-link layer but using the transport layer information as operating criteria. We conduct a comparative study between the XST mechanism and Active Queue Management (AQM) mechanism in the literature, such as Random Early Detection (RED) in wireless environments with varying link characteristics. Simulation results demonstrate that the proposed XST significantly outperforms RED in wireless cellular network, especially under high-error-rate which is a major characteristic of wireless links.

On the uplink of a DS-CDMA system, successive interference cancellation (SIC) technique can be employed to reduce multiple access interference and improve system capacity. In such a system with K active users, there are K possible decoding orders of SIC and not every decoding order is feasible due to some constraints. It is highly time-consuming to examine the system feasibility directly by using the exhaustive search method (ESM) for a system with even a moderate number of users. In this paper, we propose an efficient approach for examining the feasibility of DS-CDMA systems with imperfect SIC. The proposed approach has significantly less computational complexity than that of ESM, thus benefits the quick decision of admission control and/or scheduling in DS-CDMA systems. Furthermore, under the decoding order obtained by the proposed approach, we prove that the system is able to achieve the lowest outage probability among all possible decoding orders. Simulation experiments and numerical results validate our analysis and demonstrate the effectiveness of our approach.