Design and Development of an FPGA-based Hardware Accelerator for Corner Feature Extraction and Genetic Algorithm-based SLAM System

Simultaneous Localization and Mapping (SLAM) systems are crucial parts of mobile robots. These systems require a large number of computing units, have significant real-time requirements and are also a vital factor which can determine the stability, operability and power consumption of robots. This thesis aims to improve the calculation speed of a lidar-based SLAM system in domestic scenes, reduce the power consumption of the SLAM algorithm, and reduce the overall cost of the whole platform. Lightweight, low-power and parallel optimization of SLAM algorithms are researched. In the thesis, two SLAM systems are designed and developed with a focus on energy-efficient and fast hardware-level design: a geometric method based on corner extraction and a genetic algorithm-based approach. Finally, an FPGA-based hardware accelerated SLAM is implemented and realized, and compared to a software-based system. As for the front-end SLAM system, a method of using a Corner Feature Extraction (CFE) algorithm on FPGA platforms is first proposed to improve the speed of the feature extraction. Considering building a back-end SLAM system with low power consumption, a SLAM system based on genetic algorithm combined with algorithms such as Extended Kalman Filter (EKF) and FastSLAM to reduce the amount of calculation in the SLAM system is also proposed. Finally, the thesis also proposes and implements an adaptive feature map which can replace a grid point map to reduce the amount of calculation and utilization of hardware resources. In this thesis, the lidar SLAM system with front-end and back-end parts mentioned above is implemented on the Xilinx PYNQ Z2 Platform. The implementation is operated on a mobile robot prototype and evaluated in real scenes. Compared with the implementation on the Raspberry Pi 3B+, the implementation in this thesis can save 86.25% of power consumption. The lidar SLAM system only takes 20 ms for location calculation in each scan which is 5.31 times faster compared with the software implementation with EKF

Application of biosynthesized ZnO nanoparticles on an electrochemical H 2 O 2 biosensor

ZnO nanoparticles (NPs) were synthesized via a green biochemical method using Corymbia citriodora leaf extract as a reducing and stabilizing agent. The biosynthesized ZnO NPs were characterized by SEM and XRD. An electrochemical H2O2 biosensor was fabricated by modification of a glassy carbon electrode using our proposed ZnO NPs. The electrochemical sensor showed excellent detection performance towards trace amounts of H2O2, demonstrating that it could potentially be used in clinical applications

A Homogenous Luminescent Proximity Assay for 14-3-3 Interactions with Both Phosphorylated and Nonphosphorylated Client Peptides

The 14-3-3 proteins are a family of dimeric eukaryotic proteins that mediate both phosphorylation-dependent and -independent protein-protein interactions. Through these interactions, 14-3-3 proteins participate in the regulation of a wide range of cellular processes, including cell proliferation, cell cycle progression, and apoptosis. Because of their fundamental importance, 14-3-3 proteins have also been implicated in a variety of diseases, including cancer and neurodegenerative disorders. In order to monitor 14-3-3/client protein interactions for the discovery of small molecule 14-3-3 modulators, we have designed and optimized 14-3-3 protein binding assays based on the amplified luminescent proximity homogeneous assay (AlphaScreen) technology. Using the interaction of 14-3-3 with a phosphorylated Raf-1 peptide and a nonphosphorylated R18 peptide as model systems, we have established homogenous “add-and-measure” high-throughput screening assays. Both assays achieved robust performance with S/B ratios above 7 and Z’ factors above 0.7. Application of the known antagonistic peptides in our studies further validated the assay for screening of chemical compound libraries to identify small molecules that can modulate 14-3-3 protein-protein interactions

Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks. To obtain time diversity within the channel coherence time, we first put forward a random repetition scheme that randomizes the interference power. Then, we optimize the number of reserved slots and the number of repetitions for each packet to minimize the QoS violation probability, defined as the percentage of users that cannot achieve URLLC. We build a cascaded Random Edge Graph Neural Network (REGNN) to represent the repetition scheme and develop a model-free unsupervised learning method to train it. We analyze the QoS violation probability using stochastic geometry in a symmetric scenario and apply a model-based Exhaustive Search (ES) method to find the optimal solution. Simulation results show that in the symmetric scenario, the QoS violation probabilities achieved by the model-free learning method and the model-based ES method are nearly the same. In more general scenarios, the cascaded REGNN generalizes very well in wireless networks with different scales, network topologies, cell densities, and frequency reuse factors. It outperforms the model-based ES method in the presence of the model mismatch.Comment: Submitted to IEEE journal for possible publicatio

Multilayered Clouds Identification and Retrieval for CERES Using MODIS

Traditionally, analyses of satellite data have been limited to interpreting the radiances in terms of single layer clouds. Generally, this results in significant errors in the retrieved properties for multilayered cloud systems. Two techniques for detecting overlapped clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. The first technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other method uses microwave (MWR) data. The use of BTD, the 11-12 micrometer brightness temperature difference, in conjunction with tau, the retrieved visible optical depth, was suggested by Kawamoto et al. (2001) and used by Pavlonis et al. (2004) as a means to detect multilayered clouds. Combining visible (VIS; 0.65 micrometer) and infrared (IR) retrievals of cloud properties with microwave (MW) retrievals of cloud water temperature Tw and liquid water path LWP retrieved from satellite microwave imagers appears to be a fruitful approach for detecting and retrieving overlapped clouds (Lin et al., 1998, Ho et al., 2003, Huang et al., 2005). The BTD method is limited to optically thin cirrus over low clouds, while the MWR method is limited to ocean areas only. With the availability of VIS and IR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and MW data from the Advanced Microwave Scanning Radiometer EOS (AMSR-E), both on Aqua, it is now possible to examine both approaches simultaneously. This paper explores the use of the BTD method as applied to MODIS and AMSR-E data taken from the Aqua satellite over non-polar ocean surfaces

Comparison of the Effects of Acarbose and TZQ-F, a New Kind of Traditional Chinese Medicine to Treat Diabetes, Chinese Healthy Volunteers

Ethnopharmacological Relevance. TZQ-F has been traditionally used in Traditional Chinese Medicine as a formula for the treatment of diabetes. Aim of the Study. This study aims to compare the pharmacologic effects and gastrointestinal adverse events between TZQ-F and acarbose. Methods. The double-blind randomized placebo-controlled fivefold crossover study was performed in 20 healthy male volunteers. Plasma glucose, plasma IRI, and plasma C-peptide were measured to assess the pharmacologic effects. Flatus and bowel activity were measured to assess the adverse event of gastrointestinal effect. Results. 3 and 4 tablets of TZQ decreased the Cmax of plasma glucose compared with that of the previous day and with placebo. 3 tablets also decreased Cmax of plasma C-peptide compared with placebo. 4 tablets increased Cmax of plasma insulin after breakfast and the AUC of plasma C-peptide after breakfast and dinner. 2 tablets did not decrease plasma glucose and elevated the Cmax and AUC of C-peptide after breakfast and dinner, respectively. Acarbose 50 mg decreased the Cmax of plasma insulin and C-peptide after breakfast and the Cmax of plasma glucose and C-peptide after dinner. The subjects who received TZQ did not report any abdominal adverse events. Conclusions. 3 tablets of TZQ have the same effects as the acarbose

A Dual-Readout F2 Assay That Combines Fluorescence Resonance Energy Transfer and Fluorescence Polarization for Monitoring Bimolecular Interactions

Forster (fluorescence) resonance energy transfer (FRET) and fluorescence polarization (FP) are widely used technologies for monitoring bimolecular interactions and have been extensively used in high-throughput screening (HTS) for probe and drug discovery. Despite their popularity in HTS, it has been recognized that different assay technologies may generate different hit lists for the same biochemical interaction. Due to the high cost of large-scale HTS campaigns, one has to make a critical choice to employee one assay platform for a particular HTS. Here we report the design and development of a dual-readout HTS assay that combines two assay technologies into one system using the Mcl-1 and Noxa BH3 peptide interaction as a model system. In this system, both FP and FRET signals were simultaneously monitored from one reaction, which is termed -Dual-Readout F2 assay- with F2 for FP and FRET. This dual-readout technology has been optimized in a 1,536-well ultra-HTS format for the discovery of Mcl-1 protein inhibitors and achieved a robust performance. This F2 assay was further validated by screening a library of 102,255 compounds. As two assay platforms are utilized for the same target simultaneously, hit information is enriched without increasing the screening cost. This strategy can be generally extended to other FP-based assays and is expected to enrich primary HTS information and enhance the hit quality of HTS campaigns.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90469/1/adt-2E2010-2E0292.pd

Evaluation of the Ribosomal Protein S1 Gene (rpsA) as a Novel Biomarker for Mycobacterium Species Identification

Objectives. To evaluate the resolution and reliability of the rpsA gene, encoding ribosomal protein S1, as a novel biomarker for mycobacteria species identification. Methods. A segment of the rpsA gene (565 bp) was amplified by PCR from 42 mycobacterial reference strains, 172 nontuberculosis mycobacteria clinical isolates, and 16 M. tuberculosis complex clinical isolates. The PCR products were sequenced and aligned by using the multiple alignment algorithm in the MegAlign package (DNASTAR) and the MEGA program. A phylogenetic tree was constructed by the neighbor-joining method. Results. Comparative sequence analysis of the rpsA gene provided the basis for species differentiation within the genus Mycobacterium. Slow-and rapid-growing groups of mycobacteria were clearly separated, and each mycobacterial species was differentiated as a distinct entity in the phylogenetic tree. The sequences discrepancy was obvious between M. kansasii and M. gastri, M. chelonae and M. abscessus, M. avium and M. intracellulare, and M. szulgai and M. malmoense, which cannot be achieved by 16S ribosomal DNA (rDNA) homologue genes comparison. 183 of the 188 (97.3%) clinical isolates, consisting of 8 mycobacterial species, were identified correctly by rpsA gene blast. Conclusions. Our study indicates that rpsA sequencing can be used effectively for mycobacteria species identification as a supplement to 16S rDNA sequence analysis