Collaborative localization (CL) has garnered substantial attention in the field of robotics in recent years. Nonetheless, conventional CL algorithms have faced challenges when dealing with practical issues such as spurious sensor data and limited or discontinued observation and communication in real-world settings. This paper proposes a fault-tolerant practical estimated cross-covariance minimum variance update method (FPECMV) designed to tackle these challenges under limited connectivity. The proposed algorithm uses a CNN-based method to evaluate confidence, along with a fault isolation module to identify faults and manage spurious data in real time. The proposed fault isolation module utilizes relative measurement information that randomly occurs, without requiring high observation and communication prerequisites. Notably, the algorithm takes into account correlations among agents to maintain consistency in localization filters and attain accurate localization despite constraints posed by limited connectivity. To evaluate the performance of the proposed algorithm, experiments were conducted in a collaborative multi-robot environment with spurious sensor data and limited connectivity, using both the BULLET simulation and physical mobile robots. The experimental results indicate that the overall localization performance of the proposed algorithm is improved by 21.0% compared to the state of the art. The experiment results demonstrate the effectiveness of our algorithm in localizing group agents in challenging and intricate scenarios with limited connectivity and spurious sensor data.

We consider a spectrum sharing protocol in which a secondary system can operate on the same spectrum with a primary user. In the protocol, the secondary system helps the primary system achieve its target rate by acting as a relay for the primary system. As a reward, the remaining subcarriers can be used for the secondary transmission. In order to alleviate the disadvantages of amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques, an adaptive relaying scheme is proposed. In this paper, we study energy efficient resource allocation for the cognitive cooperation system. The considered problem is modeled as a non-convex optimization problem which takes into account the total transmit power of the secondary system and the minimum required data rate of the primary system. The optimal set of subcarriers used for cooperation, subcarrier power allocation and relaying technique are derived for maximization of the energy efficiency (EE) of the secondary system by using fractional programming and dual theory. Simulation results demonstrate that significant performance gains can be achieved by the devised scheme.

While many bug prediction algorithms have been developed by academia, they're often only tested and verified in the lab using automated means. We do not have a strong idea about whether such algorithms are useful to guide human developers. We deployed a bug prediction algorithm across Google, and found no identifiable change in developer behavior. Using our experience, we provide several characteristics that bug prediction algorithms need to meet in order to be accepted by human developers and truly change how developers evaluate their code.

This paper proposes an energy-efficient multicast scheme for downlink orthogonal frequency division multiplexing (OFDM) system in which the base station (BS) is equipped with multiple antennas. We employ the multiple description coding multicast (MDCM) model and beamforming technique to maximize the energy efficiency (EE) with the constraint on total transmit power. In MDCM, the transmission rate is not limited by the user with the minimum channel quality any more. And the beamforming technique can enhance the signal strength of the weakest user. In this paper, a two-step suboptimal scheme is studied. Firstly, the beamforming weighted vector (BWV) is obtained by a vector splitting algorithm, and then the power and subcarrier allocation is realized by fractional programming and subgradient method. Numerical results reveal that the proposed scheme can achieve near optimal EE and greatly improve the EE compared with two spectrum-efficient schemes. In addition, the energy-efficient scheme can render a good performance in EE as well as throughput when the total transmit power is small.