In recent years, joint radar and communication (JRC) systems have garnered significant attention due to their enhanced equipment utilization and high spectrum efficiency. This paper investigates a JRC-enabled multi-UAV cooperative system, where multiple UAVs concurrently execute communication tasks for communication users (CUs) and perception tasks for sensed targets (STs) distributed across a specified region. To strike the trade-off between the communication performance and sensing accuracy, we formulate a weighted performance optimization problem aimed at simultaneously maximizing the data transmission for CUs and minimizing the squared position error bound (SPEB) for STs, by jointly optimizing user association and channel assignment, power allocation, as well as UAV deployment. To effectively address this challenging problem, we initially recast the non-differentiable objective function into a more tractable and interpretable form with the aid of smooth approximation techniques. Subsequently, by virtue of the specific problem structure, we decompose the original joint optimization problem and develop an iterative method to optimize each subproblem sequentially. Extensive simulations demonstrate the significant performance gains of the proposed design compared to other benchmark schemes.

Coronary artery disease (CAD) is a type of heart disease, where echocardiography can be used in the diagnosis. Due to the time-consuming and non-reproducibility of manual assessment, automatic evaluation methods are increasingly required where the regional wall segmentation is a crucial step. Currently, most studies prioritize designing sophisticated networks, yet overlooking the fact that the poor segmentation performance comes from the inherent fuzziness and low contrast of echocardiography. In this paper, a framework named MGS-EP is proposed. Inspired by clinical annotation practices where experts infer missing wall structures using anatomical knowledge, this expert intuition is formalized as the Expert Prior (EP). The proposed MGS-EP integrates EP constraints to compensate for image degradation, which consists of a pre-decoupler and a mask-guided segmentation (MGS) network. Where the pre-decoupler first models the EP, generating pseudo masks that encapsulate both complete topology and approximate spatial localization of the regional walls. These pseudo masks are subsequently concatenated with raw echocardiography to form a composite input for the MGS network, thereby enabling regional wall segmentation with topological integrity. The concatenation of pseudo masks with original echocardiography as input to the MGS network serves two purposes: mitigating regional walls' contour degradation due to fuzziness while embedding EP topological constraints. Experimental results demonstrate that, compared to the baseline nnU-Net, the MGS-EP enables topologically continuous segmentation, and can achieve an average improvement of 7.76% in Dice and a reduction of$\mathbf{1 1. 6 1}$pixels in Hausdorff Distance.

Battery free Internet of Things (BF IoT), which is realized by harvesting ambient energy to power the IoT devices, has many advantages such as low power, low cost, free-maintenance, easy deployment, and environmental protection. As the two key enabling technologies of BF IoT, the integration of energy harvesting, management and backscatter communication (BackCom) is expected to meet practical applications. However, several fundamental issues need to be addressed to fully develop the potential of such integration. In this paper, we propose an ultra-low power energy harvesting and management scheme that effectively reduces the startup and quiescent power consumption. In the case of extremely limited energy harvested, the Long Range (LoRa) BackCom digital waveform generation algorithm with ultra-low power consumption and low complexity is studied, which can support low-power and low-cost micro-controllers (MCUs). Then, a radio frequency (RF) front-end scheme that is compatible with RF energy harvesting (RFEH), BackCom, and On-Off-Keying (OOK) receiving is proposed. On this basis, we design and implement a fully functional BF LoRa Tag, which integrates tag antennas, LoRa BackCom, low-power OOK receiving, multi-parameter real-time sensing, RFEH and management, at a cost of only 6. Our test results show that the BF LoRa Tag can be self-powered by harvesting the RF energy as low as –19 dBm, with the cold-start power as low as 280 nW, the quiescent power as low as 49 nW, the OOK receiving power consumption as low as$1.74~\mu $W, the LoRa BackCom power consumption of only$251~\mu $W, and the communication distance up to 445 meters. Compared with the prototypes in existing literature, the BF LoRa Tag not only has lower power consumption, wider coverage, and the lowest cost, but also has complete system functions. Finally, we discuss the reciprocity between stations and receivers, as well as the BF LoRa cellular communication network, which provides useful references for the practical application and large-scale deployment of BF IoT in the future.

Radio-frequency (RF)-powered backscattering communication (BC) has many advantages, such as low-power consumption, battery-free, small size, low cost, full sealing, manual maintenance-free, and easy mass deployment. However, the weak RF energy harvested and the short BC distance have become the two critical obstacles in the real-world implementation of this potential technology. In this article, we propose a high-sensitivity RF energy harvesting (RFEH) and long-distance BC method, and for the first time, we demonstrate the RF-powered self-sustainable long-distance BC device named RFEH LoRa. In order to improve RFEH sensitivity, an ultralow-power (ULP) consumption voltage monitoring and startup control circuit topology is designed to reduce the power consumption to 245 nanowatt (nW) during the accumulation of RF energy. While in order to increase the BC distance, a direct digital frequency synthesis technology is used to generate a linear frequency modulation square wave sequence conforming to the LoRa specifications based on the ULP microcontroller unit, and the square wave is used to drive an RF switch to achieve long-distance BC of hundreds of meters. Test results show that the instantaneous BC current is as low as 130 microampere ( μA), and the standby current is only 52 nanoampere (nA). RFEH LoRa is implemented with commercial off-the-shelf components. It can self-startup by harvesting RF energy as low as -22.5 dBm, and send its sensing data to the receiver of 381-m away.

In this study, an irregular low‐density parity‐check (iLDPC) coded rate compatible modulation (iLDPC‐RCM) is proposed. A particularly designed interleaver is inserted between LDPC and RCM such that a fast convergence and a low bit‐error rate (BER) can be achieved. The combination of strong error correction capability of irregular LDPC code and the seamless adaptation of RCM enables the proposed scheme to achieve a robust and spectrally efficient transmission over time‐varying channels. In addition, a joint belief propagation algorithm is proposed to lower the BER of iLDPC‐RCM and speed up the convergence of iterative decoding. Simulation results show that the proposed iLDPC‐RCM improves the BER and throughput performance while maintaining an acceptable computational complexity at the same time, validating its advantages over the regular LDPC coded RCM with the same coding rate.