We propose a novel pinching-antenna-assisted integrated sensing and communication (PAA-ISAC) framework, where the dielectric waveguide enables the downlink transmission and the reflected signal is collected at the base station (BS) for radio sensing. Due to the asymmetrical propagation between the waveguide → sensing target (ST) and the ST → BS, the reflection law is exploited to model the radio sensing propagation. The sensing degree of freedom (SDoF) and Cramér–Rao Bound (CRB) of the estimated angle are first analyzed, where the qualitative relationship between the CRB and the antenna design is revealed. Capitalizing on this finding, a weighted CRB and transmit power minimization problem is formulated subject to dual-functional requirements. A low-complexity alternating optimization algorithm is proposed to jointly optimize the location of the activated pinching antenna (PA) and the transmit power towards the waveguide by invoking the Lagrange duality theory. Our simulation results showcase that: 1) the derived performance approximation is close to the exact CRB; 2) the SDoF is independent of the waveguide deployment; 3) the proposed algorithm outperforms the fixed antenna design in the context of radio sensing performance and the transmit power consumption.

A simultaneously transmitting and reflecting surface (STARS) assisted near-field (NF) integrated sensing and communication (ISAC) framework is proposed, where the radio sensors are installed on the STARS to directly conduct the distance-domain sensing by exploiting the spherical wavefront. A new squared position error bound (SPEB) expression is derived to reveal the dependence on beamforming (BF) design and sensor deployment. To balance the trade-off between the SPEB and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the sensor deployment, the active and passive BF, subject to communication and power consumption constraints. For the sensor deployment optimization, a joint sensor deployment algorithm is proposed by invoking the successive convex approximation. Under a specific relationship between the sensor numbers and BF design, we derive the optimal sensor interval in a closed-form expression. For the joint BF optimization, a penalty-based method is invoked. Simulation results validated that the derived SPEB expression is close to the exact SPEB, which reveals the Fisher Information Matrix of position estimation in NF can be approximated as a diagonal matrix. Furthermore, the proposed algorithms achieve the best SPEB performance compared to the benchmark schemes accompanying the lowest deployment cost.

A hybrid non-orthogonal multiple access (HNOMA) empowered integrated sensing and communications (ISAC) framework is proposed, which adaptively manages the additional sensing-to-communication (S2C) interference to save the transmit power. Two scenarios with different numbers of communication users (CUs) are investigated. For the first scenario where the number of CUs does not exceed the number of transmit antennas, a mixed integer problem is formulated to optimize the beamforming (BF) design and successive interference cancellation (SIC) options. An ideal case is primarily inspected, which unveils an insight into the required number of dedicated sensing beams. Inspired by this insight, the SIC options are determined while the remaining BF design is solved via semidefinite relaxation (SDR). For the second scenario where the number of CUs exceeds the number of transmit antennas, the CUs are further grouped into NOMA clusters to mitigate the communication-to-communication interference. An alternating optimization-based algorithm is developed, where the BF design, SIC options and power allocation are alternatively optimized. Simulation results reveal that: 1) the proposed algorithm achieves power-saving gain compared to the conventional ISAC; 2) the proposed algorithm can further exploit the benefits of NOMA to save transmission power while maintaining the least beampattern mismatch in the second scenario.

A novel reconfigurable intelligent surfaces (RIS) assisted near-field (NF) ISAC system is investigated, where the spherical wave propagation environment is utilized to elevate the radio sensing performance. Except for the conventional RIS, the sensor elements are embedded on the RIS surface to conduct the radios sensing functionality. By exploiting the symmetry property of the steering vector, a new expression of position error bound (PEB) is derived to unveil the impact of the sensor deployment. To balance the radio sensing performance and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the number of sensor elements and the passive beamforming (BF). To solve this non-convex problem, a joint geometric programming element-wise (JGPE) algorithm is proposed. The successive convex approximation for geometric programming is invoked to optimize the number of sensor elements while the element-wise algorithm is adopted to optimize the passive BF. Numerical results demonstrated that the proposed algorithm reach the least PEB and cost function value among the benchmarks.

Nuclear safety technology arouses the concern of nuclear power works when historical nuclear accidents have proven irreversible environmental damage and bodily injury. Significantly, nuclear evacuation technology is one of the critical parts to guarantee public lives, exacerbated by the spatial and temporal uncertainties associated with unfixed routes, radiation distribution, and crowdedness in traffic. At present, large-scale nuclear evacuation falls into three challenges: 1) round-trip, 2) unfixed routes, and 3) dynamic radiation field. This article proposes a multistage vehicle planning model for evacuation time and individual dose optimizations under real-world constraints such as vehicle limits, evacuee demand, and number of evacuation times. The critical contributions include three stages: 1) optimizing the number of vehicles and vehicle schemes for round-trip, guaranteeing the evacuation of all personnel efficiently; 2) the next pick-up point is selected to minimum distance and dose for unfixed routes; and 3) vehicle routing is conducted of emergency response, featuring a dynamic programming by nondominant sorting genetic algorithm II with minimum time and individual dose. Therefore, the proposed model can provide recommended schedules, consisting of the number of vehicles, associated arrival, departure trips, and the exposure dose. With the actual data of the nuclear power plant in China, the case demonstrates the model's efficiency by comparing it with conventional solutions.

A novel non-orthogonal multiple access (NOMA) assisted full space integrated sensing and communication (ISAC) framework is proposed to elevate the radio sensing performance. Exploiting the simultaneously transmitting and reflecting RIS (STAR-RIS) to extend the half-space into full-space ISAC coverage intensifies the competition for wireless resources. To alleviate this fierce competition as well as ensure ISAC performance, the cluster-based NOMA (CB-NOMA) technique is employed to save the joint communication and sensing (C&S) beams. Furthermore, the dedicated sensing beam accompanied by the joint C&S beams supports the radio sensing functionality. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design, subject to communication requirements. To solve this non-convex problem, a block coordinate descent (BCD) based integral matrix algorithm is proposed to reach a suboptimal solution. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed to optimize the coupled variables. For the passive BF block, the penalty-based method is invoked. To further reduce the complexity of the passive BF design, a BCD-based element-wise algorithm is proposed, where the joint phase shift and amplitude coefficients of each STAR-RIS element are optimized one by one. Simulation results verified that our proposed algorithms achieve higher beampattern gain towards the intended targets than the benchmark schemes accompanying less mismatch error.

A simultaneously transmitting and reflecting RIS (STAR-RIS) empowered integrated sensing and communications (ISAC) framework is proposed, where the STAR-RIS establishes an additional link to compensate for the insufficient LoS link. To alleviate the conflicts between the limited wireless resources and the multifunctionality requirements, a cluster-based NOMA transmission scheme is adopted, where the communication functionality is employed by the joint communication and sensing (C&S) beam in a NOMA approach. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design. We propose a block coordinate descent (BCD) based iterative algorithm, which splits the optimization variables into two blocks. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed. For the passive BF block, the penalty-based method is invoked to deal with the non-convex constraints. Simulation results verified that our proposed algorithm achieves higher beampattern gain at the intended targets than the other baselines accompanying the least mismatch error.

Hearing-impaired people and non-native speakers rely on captions for access to video content, yet most videos remain uncaptioned or have machine-generated captions with high error rates. In this paper, we present the design, implementation and evaluation of BandCaption, a system that combines automatic speech recognition with input from crowd workers to provide a cost-efficient captioning solution for accessible online videos. We consider four stakeholder groups as our source of crowd workers: (i) individuals with hearing impairments, (ii) second-language speakers with low proficiency, (iii) second-language speakers with high proficiency, and (iv) native speakers. Each group has different abilities and incentives, which our workflow leverages. Our findings show that BandCaption enables crowd workers who have different needs and strengths to accomplish micro-tasks and make complementary contributions. Based on our results, we outline opportunities for future research and provide design suggestions to deliver cost-efficient captioning solutions.