Given the increasing number of on-demand delivery, people progressively realize that accurate indoor localization of couriers becomes vital for improving the quality of services. However, existing wireless indoor localization systems suffer from deployment difficulty caused by model migration or extra infrastructure needed, and traditional neural networks fail to get good performance with poor quality data and labels. In this work, we overcome the fundamental challenges in pitiful data to achieve a low-cost indoor localization system using large-scale poor-quality crowdsourcing WiFi data - WiLoc. WiLoc constructs WiFi data into a hypergraph and acquires the topological relationships among APs based on a light-weight graph convolutional network. Then, it utilizes a modified transformer structure to learn the latent global-aware features according to the data quality and task demand. Finally, we implement the prototype of WiLoc in the real on-demand delivery scenario with merchant-level accuracy and evaluate its performance based on real datasets from couriers. Extensive experiments demonstrate that WiLoc achieves an average F1 score of 85.03 % across various shopping malls, outperforming the baseline methods.

mmWave networks offer wide bandwidth for high-speed wireless communication but suffer from limited range and susceptibility to blockage. Existing coverage provisioning solutions not only incur high costs but also require significant expert knowledge and manual efforts. In this paper, we present AutoMS, an automated service framework to optimize mmWave coverage by strategically designing and placing low-cost passive metasurfaces. Our approach consists of three key components: (1) joint optimization of metasurface phase configurations and placement as well as access point beamforming codebooks. (2) a fast 3D ray-tracing simulator for accelerated large-scale metasurface channel modeling. (3) a metasurface design amenable to ultra-low-cost hot stamping fabrication, featuring high reflectivity, near 2π phase control, and wideband support. Simulation and testbed experiments show that AutoMS can increase the median received signal strength by 11 dB in target rooms and over 20 dB at previous blind spots, and improve the median throughput by over 3× in real-world scenarios.

Accurate and quick relative-distance measurement is crucial for supporting various intelligent transparent services, such as multi-device collaboration, screen rotation, and multi-device mirroring. Unfortunately, current methods often rely on single-modality sensing, resulting in various limitations: BLE-based and WiFi-based methods suffer from coarse-grained estimation, and ultrasound-based approaches suffer from limited sensing range. In this work, we aim at designing a distance-measurement system that enjoys long range, high accuracy, and small delay. Our designed system, named BAIR, relies on low-energy Bluetooth (BLE), acoustic sensors, and inertial measurement units (IMU) equipped on commercial smartphones for fine-grained and real-time relative distance estimation. BAIR effectively aligns multiple sensory signals with different sampling rates via the improved Kalman filter technology. To mitigate IMU's integration errors, BAIR calculates the average velocity over a preceding period and uses this, alongside accumulated velocity data from the IMU, significantly improving distance prediction accuracy. We implemented our BAIR system on smartphones and conducted extensive experiments to evaluate its performance. Specifically, in static scenarios, BAIR achieves a mean average error (MAE) of 11 cm. In moving scenarios, the cumulative distribution function (CDF) values for 95%, 80%, and 50% are 31 cm, 13 cm, and 8 cm, respectively. The memory footprint of BAIR is 16.41 MB. We release a video demo on YouTube11https://youtu.be/7Fbmn4ALaI0.

Low Earth Orbit (LEO) satellite communication is essential for wireless communication. While manufacturing and launching LEO satellites have become efficient and cost-effective, ground stations remain expensive due to complex designs for handling severe path losses and precise beam tracking. Hence, it is important to develop low cost and high-performance ground stations for widespread adoption of LEO satellite communication. Towards realizing this goal, we design a passive metasurface-enhanced LEO ground station system, named PMSat, combining metasurface's fine-grained beamforming capability with a small-size phased array's adaptive steering and focusing. For uplink, we jointly optimize the phase array codebook and uplink metasurface phase profile, and realize electronic steering by switching the codeword. We further jointly optimize the downlink metasurface phase profile to improve the focusing performance and enhance the received signal strength (RSS) over a wide range of incident angles. Our PMSat prototype consists of a single passive metasurface with 21 × 21 elements for uplink and 22 × 22 for downlink, along with 1 × 4 receiving and 1 × 4 transmitting phased array antennas. The effectiveness of our proposed PMSat is validated through extensive experiments, and results demonstrate that the optimized metasurface improves the SNR by 8.32 dB and 16.57 dB for uplink and downlink, respectively.

The computational methods of protein-protein interaction sites prediction can effectively avoid the shortcomings of high cost and time in traditional experimental approaches. However, the serious class imbalance between interface and non-interface residues on the protein sequences limits the prediction performance of these methods. This work therefore proposed a new strategy, NearMiss-based under-sampling for unbalancing datasets and Random Forest classification (NM-RF), to predict protein interaction sites. Herein, the residues on protein sequences were represented by the PSSM-derived features, hydropathy index (HI) and relative solvent accessibility (RSA). In order to resolve the class imbalance problem, an under-sampling method based on NearMiss algorithm is adopted to remove some non-interface residues, and then the random forest algorithm is used to perform binary classification on the balanced feature datasets. Experiments show that the accuracy of NM-RF model reaches 87.6% and 84.3% on Dtestset72 and PDBtestset164 respectively, which demonstrate the effectiveness of the proposed NM-RF method in differentiating the interface or non-interface residues.

We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L0sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.

We handle a special type of motion blur considering that cameras move primarily forward or backward. Solving this type of blur is of unique practical importance since nearly all car, traffic and bike-mounted cameras follow out-of-plane translational motion. We start with the study of geometric models and analyze the difficulty of existing methods to deal with them. We also propose a solution accounting for depth variation. Homographies associated with different 3D planes are considered and solved for in an optimization framework. Our method is verified on several natural image examples that cannot be satisfyingly dealt with by previous methods.