Simultaneous Localization and Mapping (SLAM) based on LIDAR and MEMS IMU is a kind of autonomous integrated navigation technology. It can provide attitude, velocity position for a small UAV in an indoor frame during the outage of GNSS. A method of integrating the measurements from a LIDAR and a MEMS IMU was proposed in the paper. LIDAR measurements are a set of ranges and scan angles. The angle rates and accelerations from MEMS IMU are used to drive the simplified strapdown INS equations. The first step of the method is environment features extracting from the measurements of LIDAR and constructing a feature map. Then, the model of errors of LIDAR measurement due to the change of the scan plane during the attitude manoeuver is established and compensated based on aiding information from MEMS INS and the assumption about the structural indoor environment. The relative position parameters derived from environmental features delay matching algorithm and the differences of measurements of LIDAR at adjacent times are used to estimate the error of MEMS INS and MEMS sensors by a Kalman Filter. A LIDAR/MEMS IMU prototype was designed to verify the practicability of the integrated navigation system of LIDAR and MEMS IMU. Some experiments were carried out in a room and the results demonstrated the potential use of the LIDAR/MEMS IMU integration navigation system. , , E N U v v v δ δ δ : Error vector in INS velocities (3-states) , , L h δ δλ δ : Error vector in INS positions (3-states) , , bx by bz ε ε ε : Gyroscopes random bias vector (3-states) , , bx by bz ∇ ∇ ∇ : Accelerators random bias vector (3-states) The measurement vector of KF is the pose and the position of ( ) , , n n t t t x y ϕ derived from the LIDAR data. Seeing that it is unstable of calculation the height, a MEMS-based barometercan be used in the system or the height should be fixed to an initial value.

In life-critical applications, the real-time detection of faults is very important in Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. A new fault detection method for soft fault detection is developed in this paper with the purpose of improving real-time performance. In general, the innovation information obtained from a Kalman filter is used for test statistic calculations in Autonomous Integrity Monitored Extrapolation (AIME). However, the innovation of the Kalman filter is degraded by error tracking and closed-loop correction effects, leading to time delays in soft fault detection. Therefore, the key issue of improving real-time performance is providing accurate innovation to AIME. In this paper, the proposed algorithm incorporates Least Squares-Support Vector Machine (LS-SVM) regression theory into AIME. Because the LS-SVM has a good regression and prediction performance, the proposed method provides replaced innovation obtained from the LS-SVM driven by real-time observation data. Based on the replaced innovation, the test statistics can follow fault amplitudes more accurately; finally, the real-time performance of soft fault detection can be improved. Theoretical analysis and physical simulations demonstrate that the proposed method can effectively improve the detection instantaneity.

Through C–O–Mn bonding, graphene nanosheets are homogeneously dispersed in porous Mn3O4 to take full advantages of porous Mn3O4 and graphene nanosheets, making the as-formed three-dimensional porous Mn3O4/reduced graphene oxide (rGO) composite exhibit good electrochemical performance. Besides, C–O–Mn bonding is demonstrated to greatly promote the Faradic reactions of the composite, resulting in the enhancement of its real capacity in supercapacitor (SC) electrodes as well as lithium-ion battery (LIB) anodes. By simply fine-tuning the content of graphene (<7 wt %), the composite with 2.8 wt % of rGO delivers a high capacitance of 315 F g–1 at 0.5 A g–1 with a high rate capability of 64.7% at 30 A g–1 and an excellent cycling stability of 105% (5 A g–1, 5000 cycles) as an SC electrode. Also, the one with 6.9 wt % rGO can present a reversible capacity of more than 1500 mAh g–1 at 0.05 A g–1 as the LIB anode, the highest value reported to date, which remains 561 mAh g–1 at 1 A g–1.

With the development of global navigation satellite system (GNSS), the GNSS/inertial navigation system (INS) integrated system offers the users better positioning or navigation performance. This paper proposes an adaptive robust ultratightly coupled GNSS/INS system based on a novel vector tracking strategy for combining both global positioning system (GPS) L1 and BeiDou B1 signals' tracking together. The inherent mechanism of the vector tracking approach has been analysed to describe the relationship between the replica signals and user's dynamic state. Then, an adaptive robust filter is used to gain the accurate estimates of vehicle states when the vehicle is under a weak-signal or large manoeuvring environment. Finally, the experimental platform is set up using a GPS/BeiDou signal simulator and an inertial measurement unit simulator and the test results show that the proposed ultra-tightly coupled system can keep the tracking loops from the high dynamic perturbations, which saves the cost time of signal reacquisition. Moreover, the presented adaptive robust ultratightly coupled system can obtain a higher accuracy than Kalman filtering in a simultaneous weak-signal and large manoeuvring environment.

The development of simple and rapid toxicity detection methods has important practical significance. In this work, a dual-signal method with colorimetric and electrochemical properties for water toxicity detection was proposed for the first time based on a rapid color reaction between Escherichia coli (E. coli) and p-benzoquinone (BQ). Here, E. coli was used as a biocatalyst and BQ was used as a mediator. An IC50 value of 0.75 mg L–1 for Cu2+ was obtained using a two-step electrochemical detection method. Strikingly, toxicity could also be estimated visually by the naked eye, and the minimum detection limit was 3.2 mg L–1 for Cu2+. The dual-signal toxicity detection method extends the function of BQ, and the result is more reliable than the traditional single-signal method. This simple and rapid toxicity detection method shows certain application prospects.

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BackgroundProstate cancer (PCa) is a biologically heterogeneous disease with considerable variation in clinical aggressiveness. In this study, bioinformatics was used to detect the patterns of gene expression alterations of PCa patients.MethodsThe gene expression profile GSE21034 and GSE21036 were downloaded from Gene Expression Omnibus (GEO) database. Significantly changed mRNA transcripts and microRNAs were identified between subtypes with favorable (cluster 2) and unfavorable (cluster 5) prognosis by two-side unequal variances t test. MicroRNAs and their potential target genes were identified by TargetScan and miRTarBase, respectively. Besides, the overlapped genes between the target genes of microRNAs and mRNA transcripts were assessed by Fisher’ exact test (one side). The functional annotation was performed by DAVID, followed by construction of protein-protein interaction (PPI) network.ResultsCompared to cluster 2, 1556 up-regulated and 1288 down-regulated transcripts were identified in cluster 5. Total 28 microRNAs were up-regulated and 30 microRNAs were down-regulated in cluster 5. Besides, 12 microRNAs target transcripts were significantly overlapped with down-regulated transcripts in cluster 5 with none of them was found overlapped with up-regulated transcripts. Functional annotation showed that cell cycle was the most significant function. In the PPI network, BRCA1, CDK1, TK1 and TRAF2 were hub protein of signature genes in cluster 5, and TGFBR1, SMAD2 and SMAD4 were hub proteins of signature gnens in cluster 2.ConclusionsOur findings raise the possibility that genes related with cell cycle and dysregulated miRNA at diagnosis might have clinical utility in distinguishing low- from high-risk PCa patients.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_156

The Doppler frequency changes rapidly due to high dynamics of vehicle, which leads to the loose lock and even the abnormal performance of global positioning system (GPS) receiver. To solve this problem, a federated ultra-tight integration algorithm based on pre-filters is proposed to optimal estimate both receiver tracking control commands and inertial navigation system (INS) navigation solutions. Firstly, the INS error model and GPS receiver tracking loop structure are built to present the fundamental architecture of the proposed ultra-tightly coupled system. Meanwhile, in order to reduce the load of the integrated filter, the pre-filters are incorporated to the ultra-tightly coupled system, and the state variables are fed into the integrated Kalman filter. Secondly, the intrinsic relevance between the phase and frequency biases of replica signals and INS states is analyzed to accomplish the deep fusion of INS and tracking loop. Finally, semi-physical simulations are performed by using a GPS signal simulator to generate signals of two high dynamic trajectories. The experimental results indicate that the proposed ultra-tight integration algorithm can achieve a good performance on reliable positioning and robust tracking in high dynamic environments, compared with the conventional approaches such as tightly coupled integration strategy and third-order phase-locked loops.