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To provide service to an abundant number of communication users and to avoid the spectrum scarcity problem, many researchers are fascinated to work towards the convergence of radar sensing and communication systems. In addition, future intelligent systems like autonomous vehicles, Vehicle-to-everything (V2X), Unmanned Aerial Vehicles (UAV), and all smart systems are going to implement both radar and communication systems on the same platform, which motivates the researchers to focus on the development of Joint Radar-Communication Systems (JRCS). Cooperative Radar-Communication System (CRCS) and Dual Functional Radar Communication (DFRC) systems provide an opportunity for communication users to utilize radar resources without disturbing radar operation. Waveform design is essential in the development of new models and designs related to joint radar-sensing and communication systems. A cooperative radar communication system uses separate waveforms for radar and communication systems. The DFRC system uses the same waveform for radar and communication operations. So to model both joint radar communication systems one should have a clear idea regarding waveform design and its approaches. Therefore, this review paper focused on different waveform design approaches for modeling CRCS and DFRC systems. In addition, the prime objective of this review paper is to give a detailed view of the existing cooperative and dual-function waveform design approaches and provide a kick-start for new learners to work on this area.

The major problems in an underwater wireless optical communication (UWOC) link are turbulence induced fading and pointing errors. In this paper, we have investigated the bit error rate (BER) performance of spatially coupled low-density parity-check (SC-LDPC) coded horizontal UWOC link over a strong turbulent channel model with pointing errors. The performance of this link for different channel and code parameters has been studied using simulations. It has been observed that a rate 1/2 ARJA protograph based SC-LDPC code with graph lifting factor of 256 gives a coding gain of 47 dB at a BER of ${10}^{-4}$ for strong turbulence channel model with pointing errors. An analytical BER expression for an uncoded UWOC link under strong turbulence with pointing errors for the On-Off Keying modulation technique has been derived. A multidimensional protograph based extrinsic information transfer algorithm has been developed to obtain the decoding thresholds for different channel parameters and code rates. We have also studied the SC-LDPC coded vertical UWOC link performance for some specific strong turbulence channel parameters with pointing errors and observed that as the link length increases from $20 m$ to $40 m$ , the performance gap between the hypothetical and cascaded channel models increases from $1.1 dB$ to $5.5 dB$ .

The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. The Joint Radar-Communication System (JRCS) provides a framework to simultaneously utilize the allocated radar spectrum for sensing and communication purposes. Generally, a Successive Interference Cancellation (SIC) based receiver is applied to mitigate mutual interference in the JRCS configuration. However, this SIC receiver model introduces a communication residual component. In response to this issue, the article presents a novel measurement model based on communication residual components for various radar waveforms. The radar system's performance within the JRCS framework is then evaluated using the Fisher Information Matrix (FIM). The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. After that, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for different waveforms. Additionally, range and range rate estimation errors are quantified using the Root Mean Square Error (RMSE) metric. Furthermore, the Posterior Cramer-Rao Lower Bound (PCRLB) is derived to validate the estimation accuracy of various waveforms. The simulation results show that the range and range rate estimation errors are within the PCRLB limit at all time instants for all the designated waveforms. The results further reveal that the NLFM pulse waveform provides improved range and range rate error performance compared to all other waveforms.

Artificial Intelligence (AI)-supported healthcare has seen a substantial rise in development in recent years. The health economic impact is a crucial factor in the decision-making process regarding AI adoption. This study aimed to analyze the latest research progress and evidence on the cost-effectiveness and clinical efficiency of healthcare AI software from various perspectives, as well as to identify future opportunities and remaining challenges. A review of global literature was conducted using two key databases, PubMed and Embase, along with other related bibliographic resources. The literature search yielded 1,178 unique articles, of which 31 were included in our analysis. These studies covered a wide variety of clinical use cases and healthcare domains, including disease diagnosis (n=13, 41.9%), risk analysis (n=6, 19.4%), screening or patient triage (n=6, 19.4%), treatment delivery (n=5, 16.1%), and patient engagement or follow-up (n=1, 3.2%). Among the included studies, 24 (77.4%) examined the cost-effectiveness of AI compared to standard human-based practices from the perspectives of patients, healthcare systems, payors, or society. The remaining 7 studies, including 5 clinical trials, concluded that AI can enhance clinical efficiency by shortening labor time or patient journey in the clinic. The findings of this targeted literature review demonstrated that leveraging AI in human decision-making has the potential to improve multilevel health outcomes. However, there is a shortage of prospective health economic studies, particularly long-term evaluations, highlighting the disparity between the rapid progress of AI and its lagging utilization in real-world practices.

The most widely-used active upper-limb (UL) prostheses worldwide are body-powered (BP), which is a two-century-old technology. Despite their affordability and functional benefits to users, these devices are prone to poor outcomes for many patients. Additionally, BP devices have witnessed limited improvements compared to their externally-powered counterparts. Literature indicates a strong need for appropriate prosthetic solutions for children and adolescents. Our previous work introduced a first-of-its-kind breathing-powered UL prosthesis (“Airbender”) that can overcome several limitations of the current BP systems (e.g., restricted operation space, user discomfort caused by the harness to which the cables are attached). Users can regulate their breathing, and this controllable airflow is subsequently used to power a small (purpose-built and optimised) Tesla turbine that can accurately control the opening and closing of the prosthetic hand. The current work explores device usability in children and adolescents with a UL difference (n = 15). Further, we gathered feedback, suggestions, and satisfaction levels from the study participants and their parents on breathing as a modality of controlling a prosthetic device. The collected responses and study observations were subjected to qualitative and statistical analysis. This study showcases real-world testing of a breathing-powered prosthesis and proves that UL-deficient children and adolescents can indeed operate the device (i.e., volitionally open or close) with their breathing input. The perceived level of difficulty in opening or closing the device tended to be on the ‘easy’ side. We report generally favourable feedback obtained from participants and their parents. Additionally, design suggestions and satisfaction levels concerning different device attributes help us involve the key stakeholders in co-creating and proactively developing a robust product development roadmap. This work is aligned with creating a step-change in the potential BP prosthesis options for patients in the future, propelled by a need-led approach.