Foundation Models (FMs) have shown great promise for multimodal medical image analysis such as Magnetic Resonance Imaging (MRI). However, certain MRI sequences may be unavailable due to various constraints, such as limited scanning time, patient discomfort, or scanner limitations. The absence of certain modalities can hinder the performance of FMs in clinical applications, making effective missing modality imputation crucial for ensuring their applicability. Previous approaches, including generative adversarial networks (GANs), have been employed to synthesize missing modalities in either a one-to-one or many-to-one manner. However, these methods have limitations, as they require training a new model for different missing scenarios and are prone to mode collapse, generating limited diversity in the synthesized images. To address these challenges, we propose DiffM4RI, a diffusion model for many-to-many missing modality imputation in MRI. DiffM4RI innovatively formulates the missing modality imputation as a modality-level inpainting task, enabling it to handle arbitrary missing modality situations without the need for training multiple networks. Experiments on the BraTs datasets demonstrate DiffM4RI can achieve an average SSIM improvement of 0.15 over MustGAN, 0.1 over SynDiff, and 0.02 over VQ-VAE-2. These results highlight the potential of DiffM4RI in enhancing the reliability of FMs in clinical applications. The code is available athttps://github.com/27yw/DiffM4RI.

The immutability of blockchain technology, while fundamental to its trustworthiness, introduces significant challenges for dynamic data governance scenarios such as financial transaction corrections, privacy-preserving healthcare data updates, and GDPR compliance. Existing blockchain rewriting schemes based on chameleon hash techniques alleviate some of these issues but suffer from inherent limitations, including complex key management overhead, lack of regulatory mechanisms, and inflexible access policy enforcement. This article introduces a chameleon hash that supports the identity update, called IDCHU , which is a novel cryptographic primitive integrating identity-based proxy re-encryption and chameleon hash with ephemeral trapdoors to enable secure, traceable and identity-aware data modifications while supporting flexible user identity updates. The proposed regulable blockchain rewriting scheme built upon IDCHU algorithm ensures compliance through three core mechanisms: identity-bound modification authority, time-constrained modification and cryptographic traceability recorded on a regulatory blockchain. We provide formal security proofs for both the IDCHU algorithm and the blockchain rewriting scheme. The experimental evaluations and comparisons validate the scheme’s efficiency, showcasing its practical viability for real-world applications.

Cell-free massive multiple-input multiple-output symbiotic radio (CF-SR) has recently been introduced as a promising solution for the Internet of Things (IoT), offering cost advantages and more uniform coverage performance for user devices. However, most previous studies assume perfect hardware, which is impractical in IoT systems. In this article, we investigate the uplink performance of CF-SR systems in the presence of hardware impairments (HWIs). We adopt two novel schemes for hybrid combining, namely hybrid maximum ratio (HMR) and hybrid local minimum mean square error (HL-MMSE), both of which effectively enhance the spectral efficiency (SE) of the backscattering link. We derive closed-form expressions for the achievable SE of both conventional MR and HMR combining schemes, taking into account the imperfect channel state information (CSI) and HWIs. The simulation results that both the direct and backscattering links are primarily limited by HWIs including multiplicative and additive distortions from the device side. We apply the differential evolution (DE) algorithm for power control to maximize the minimum SE, and the results show that the DE algorithm improves the minimum SE by approximately 52%, avoiding further degradation of the SE of the weakest device due to the impact of HWIs.

Industrial Internet of Things (IIoT) enables highly automated and intelligent cross-domain communication. Due to varying computational capabilities and security requirements, cross-domain IIoT data sharing involves the design of security protocols using different cryptosystems. Attribute-based searchable encryption enables fine-grained access control and ciphertext retrieval, but existing schemes are primarily designed on a singular cryptosystem, lacking support for searches on heterogeneous ciphertext. Moreover, related schemes still suffer from technical challenges, such as the single point bottleneck, caused by a single attribute authority, issues with attribute updates, and forward secrecy attacks. In this article, we propose an attribute-based heterogeneous data privacy sharing (AB-HDPS) scheme for blockchain-assisted IIoT. Attribute-authorized users within a public key infrastructure cryptosystem can search ciphertext from data owners in a certificateless cryptosystem. The scheme utilizes multiple authorities to generate attribute keys, employs a glass-box traceable mechanism to prevent misuse of attribute keys, and implements subset-cover trees for attribute revocation. Specifically, leveraging the immutability of blockchain, the AB-HDPS scheme supports traceability and auditing of user access. Security analysis shows that the AB-HDPS scheme can resist internal keyword guessing and chosen-plaintext attacks, and it satisfies forward security. Comparative experimental simulations demonstrate that the AB-HDPS scheme with outsourced decryption has satisfactory computational performance, and the performance of the blockchain system within the scheme is commendable.

Reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) technology has tremendous potential to revolutionize wireless communications by dynamically adapting wireless channels to boost average rate and energy efficiency (EE) of Internet of Things (IoT) networks for meeting the specifications of ultra-reliable and low-latency communications (URLLC). In this paper, we study the downlink harvested energy (HE), uplink rate, and total EE of the wireless-powered RIS-aided CF-mMIMO communication system with hardware impairments under finite blocklength. IoT devices harvest energy from the energy signals transmitted from access points (APs) during the downlink and use it for the uplink pilot and data transmission. Specifically, based on the unique characteristics of the channel fading model and the RIS deployment location, we propose a novel RIS phase shift design according to the line-of-sight (LoS) components of channels. Furthermore, we derive the average HE and uplink rate in closed form with a two-layer decoding method. We also validate the effectiveness of the proposed RIS phase shift design and the derived closed-form expressions by Monte Carlo simulations. Moreover, it is interesting to find that local minimum mean squared error (L-MMSE) combining is recommended to meet the requirements of URLLC, including communication reliability and delay. More notably, the numerical results show that the RIS-aided system with impaired hardware exhibits even superior performance, compared to the system with ideal hardware and more APs but lacking the assistance of RISs.

With the low latency and high coverage, the low earth orbit (LEO) satellite systems are attracting more and more venture capitals as well as research attentions. Due to their highly dynamic constellation topologies, routing protocols on the ground have to be tailored to efficiently adapt to the regular topology changes. However, for irregular topology changes caused by exceptional link failure/recovery, network-wide route flooding is still necessary for route convergence. But, this will cause significant traffic flooding redundancy due to the high density of constellation topologies. For larger-scale constellations, the redundancy issue will be exacerbated. To lessen the redundancy, this work proposes a lightweight route flooding mechanism by generating a sparse flooding topology that prunes the original full-mesh topology, and only flooding the route information on the sparse topology. By considering the maximum flooding hop as well as the robustness of the flooding topology, we design an algorithm to calculate the optimal topology instead of just applying the minimum spanning tree. The evaluation shows that, for the LEO-96 constellation, the new flooding topology has a 28.1% reduction in the inter-satellite links (ISLs) compared with the original topology, and the new flooding mechanism has a 37.52% reduction in the traffic flooded and a 10.03% reduction in the route convergence time compared with OSPF. Such improvements will be amplified on larger-scale constellations.

A robust scheme is presented for the efficient transmission of packet video over a tandem wireless Internet channel. This channel is assumed to have bit errors (due to noise and fading on the wireless portion of the channel) and packet erasures (due to congestion on the wired portion). First, we propose an algorithm to optimally switch between intracoding and intercoding for a video coder that operates on a packet-switched network with fixed-length packets. Different re-synchronization schemes are considered and compared. This optimal mode selection algorithm is integrated with an efficient channel encoder, a cyclic redundancy check outer coder concatenated with an inner rate-compatible punctured convolutional coder. The system performance is both analyzed and simulated. Last, the framework is extended to operate on a time-varying wireless Internet channel with feedback information from the receiver. Both instantaneous feedback and delayed feedback are evaluated, and an improved method of refined distortion estimation for encoding is presented and simulated for the case of delayed feedback.

We present an adaptive video transmission scheme for use in a code-division multiple-access network, which incorporates efficient bandwidth allocation among source coding, channel coding, and spreading under a fixed total bandwidth constraint. We derive the statistics of the received signal, as well as a theoretical bound on the packet drop rate at the receiver. Based on these results, a bandwidth allocation algorithm is proposed at the packet level, which incorporates the effects of both the changing channel conditions and the dynamics of the source content. Detailed simulations are done to evaluate the performance of the system, and the sensitivity of the system to estimation error is presented.

Summary form only given. A robust scheme for the efficient transmission of packet video over a tandem wireless Internet channel is extended to a time varying scenario with a feedback channel. This channel is assumed to have bit errors (due to noise and fading on the wireless portion of the channel) and packet erasures (due to congestion on the wired portion). Simulation results showed that refined estimation can dramatically improve the performance for varying channel conditions, and that combined feedback of both channel conditions and ACK/NACK information can further improve system performance compared with the feedback of just one type of information.