Wheel-INS2: Multiple MEMS IMU-based Dead Reckoning System for Wheeled Robots with Evaluation of Different IMU Configurations

A reliable self-contained navigation system is essential for autonomous vehicles. Based on our previous study on Wheel-INS \cite{niu2019}, a wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning (DR) system, in this paper, we propose a multiple IMUs-based DR solution for the wheeled robots. The IMUs are mounted at different places of the wheeled vehicles to acquire various dynamic information. In particular, at least one IMU has to be mounted at the wheel to measure the wheel velocity and take advantages of the rotation modulation. The system is implemented through a distributed extended Kalman filter structure where each subsystem (corresponding to each IMU) retains and updates its own states separately. The relative position constraints between the multiple IMUs are exploited to further limit the error drift and improve the system robustness. Particularly, we present the DR systems using dual Wheel-IMUs, one Wheel-IMU plus one vehicle body-mounted IMU (Body-IMU), and dual Wheel-IMUs plus one Body-IMU as examples for analysis and comparison. Field tests illustrate that the proposed multi-IMU DR system outperforms the single Wheel-INS in terms of both positioning and heading accuracy. By comparing with the centralized filter, the proposed distributed filter shows unimportant accuracy degradation while holds significant computation efficiency. Moreover, among the three multi-IMU configurations, the one Body-IMU plus one Wheel-IMU design obtains the minimum drift rate. The position drift rates of the three configurations are 0.82\% (dual Wheel-IMUs), 0.69\% (one Body-IMU plus one Wheel-IMU), and 0.73\% (dual Wheel-IMUs plus one Body-IMU), respectively.Comment: Accepted to IEEE Transactions on Intelligent Transportation System

A JNK-Dependent Pathway Is Required for TNFα-Induced Apoptosis

AbstractTumor necrosis factor (TNFα) receptor signaling can simultaneously activate caspase 8, the transcription factor, NF-κB and the kinase, JNK. While activation of caspase 8 is required for TNFα-induced apoptosis, and induction of NF-κB inhibits cell death, the precise function of JNK activation in TNFα signaling is not clearly understood. Here, we report that TNFα-mediated caspase 8 cleavage and apoptosis require a sequential pathway involving JNK, Bid, and Smac/DIABLO. Activation of JNK induces caspase 8-independent cleavage of Bid at a distinct site to generate the Bid cleavage product jBid. Translocation of jBid to mitochondria leads to preferential release of Smac/DIABLO, but not cytochrome c. The released Smac/DIABLO then disrupts the TRAF2-cIAP1 complex. We propose that the JNK pathway described here is required to relieve the inhibition imposed by TRAF2-cIAP1 on caspase 8 activation and induction of apoptosis. Further, our findings define a mechanism for crosstalk between intrinsic and extrinsic cell death pathways

Wheel-SLAM: Simultaneous Localization and Terrain Mapping Using One Wheel-mounted IMU

A reliable pose estimator robust to environmental disturbances is desirable for mobile robots. To this end, inertial measurement units (IMUs) play an important role because they can perceive the full motion state of the vehicle independently. However, it suffers from accumulative error due to inherent noise and bias instability, especially for low-cost sensors. In our previous studies on Wheel-INS \cite{niu2021, wu2021}, we proposed to limit the error drift of the pure inertial navigation system (INS) by mounting an IMU to the wheel of the robot to take advantage of rotation modulation. However, Wheel-INS still drifted over a long period of time due to the lack of external correction signals. In this letter, we propose to exploit the environmental perception ability of Wheel-INS to achieve simultaneous localization and mapping (SLAM) with only one IMU. To be specific, we use the road bank angles (mirrored by the robot roll angles estimated by Wheel-INS) as terrain features to enable the loop closure with a Rao-Blackwellized particle filter. The road bank angle is sampled and stored according to the robot position in the grid maps maintained by the particles. The weights of the particles are updated according to the difference between the currently estimated roll sequence and the terrain map. Field experiments suggest the feasibility of the idea to perform SLAM in Wheel-INS using the robot roll angle estimates. In addition, the positioning accuracy is improved significantly (more than 30\%) over Wheel-INS. The source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-SLAM).Comment: Accepted to IEEE Robotics and Automation Letter

Multi-authority attribute-based keyword search over encrypted cloud data

National Research Foundation (NRF) Singapore; AXA Research Fun

A snoRNA modulates mRNA 3' end processing and regulates the expression of a subset of mRNAs.

mRNA 3' end processing is an essential step in gene expression. It is well established that canonical eukaryotic pre-mRNA 3' processing is carried out within a macromolecular machinery consisting of dozens of trans-acting proteins. However, it is unknown whether RNAs play any role in this process. Unexpectedly, we found that a subset of small nucleolar RNAs (snoRNAs) are associated with the mammalian mRNA 3' processing complex. These snoRNAs primarily interact with Fip1, a component of cleavage and polyadenylation specificity factor (CPSF). We have functionally characterized one of these snoRNAs and our results demonstrated that the U/A-rich SNORD50A inhibits mRNA 3' processing by blocking the Fip1-poly(A) site (PAS) interaction. Consistently, SNORD50A depletion altered the Fip1-RNA interaction landscape and changed the alternative polyadenylation (APA) profiles and/or transcript levels of a subset of genes. Taken together, our data revealed a novel function for snoRNAs and provided the first evidence that non-coding RNAs may play an important role in regulating mRNA 3' processing