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Poly(3-hydroxybutyrate) (PHB)-a renewable and biodegradable polymer-is a promising alternative to nonbiodegradable synthetic plastics that are derived from petrochemicals. The methods currently employed for PHB production are costly, in part, due to the expensive cultivation feedstocks and the need to sterilize the culture medium, which is energy-intensive. This study investigates the feasibility of nonsterile PHB production from several saline organic wastes using a salt-tolerant strain, Zobellella denitrificans ZD1 (referred to as strain ZD1). Factors such as the pH, salinity, carbon/nitrogen (C/N) ratio, nitrogen source, and electron acceptor that might affect the growth of strain ZD1 and its PHB production were determined. Our results showed successful nonsterile PHB production by growing the strain ZD1 on nonsterile synthetic crude glycerol, high-strength saline wastewater, and real municipal wastewater-activated sludge under saline conditions. The PHB production was significantly enhanced when the levels of salts and nitrate-nitrogen in the culture medium were increased. This study suggested a promising low-cost nonsterile PHB production strategy from organic wastes using strain ZD1.

Microbial and emerging chemical contaminants are unwanted constituents in reclaimed wastewater, due to the health concerns of using the water for agricultural irrigation, aquifer recharges, and potable water. Removal of these contaminants is required but it is currently challenging, given that there is no simple treatment technology to effectively remove the mixture of these contaminants. This study examined the effectiveness of ZnO-assisted photocatalytic degradation of several constituents, including 1,4-dioxane, trihalomethanes (THMs), triclosan (TCS), triclocarban (TCC), antibiotic resistant bacteria (ARB) and antibiotic resistant genes (ARGs), under low intensity of UV exposure. E. coli with an ARGs-carrying circular plasmid (pUC19) was used as a model antibiotic resistant bacterium. Our results show that commercial zinc oxide (C-ZnO) assisted photodegradation of 1,4-dioxane, and dehalogenation of THMs, TCS, and TCC, while tetrapodal zinc oxide (T-ZnO) enhanced the dehalogenation of TCS and TCC. Additionally, T-ZnO assisted the photocatalytic inactivation of the E. coli within 6 h and caused structural changes in the plasmid DNA (pUC19) with additional UV exposure, resulting in non-functional AGR-containing plasmids. These results also suggest that higher UV dose is required not only to inactivate ARB but also to damage ARGs in the ARB in order to decrease risks in promoting ARB population in the environment. Overall, our results implicated that, under low UV intensity, ZnO-assisted photocatalysis is a promising alternative to simultaneously remove biological and emerging chemical contaminants in treated wastewater for safe reuse.

Lignocellulosic biomass, packed with sugars, is one of the most available renewable resources for biofuels and bioproducts production. To release the sugars for the production, enzymatic hydrolysis (saccharification) of pretreated lignocellulosic biomass are required. However, the saccharification process is costly, inefficient, and requires multi-step operations. This is in part due to the high cost and the limited selection of commercial enzymes which commonly have different optimal pH and temperatures. Here we reported a one-step saccharification of pretreated lignocellulosic biomass using immobilized biocatalysts containing five different saccharifying enzymes (SEs) with a similar optimum pH and temperature. The five SEs - endo-1,4-β- d -glucanase (an endoglucanase, eglS), cellobiohydrolase (an exoglucanase, cbhA), and β-glucosidase (bglH), endo-1,4-β-xylanase (an endoxylanase, xynC) and β-xylosidase (bxlB) – were successfully expressed and produced by E. coli BL21. Better saccharification of pretreated corn husks was observed when using the five crude SE enzymes than those using two commonly used SEs, endo-1,4-β- d -glucanase and β-glucosidase. The five SEs were cross-linked in the absence or the presence of magnetic nanoparticles (hereafter referred as SE-CLEAs and M-SE-CLEAs, respectively). By using SE-CLEAs, the highest amount of reduced sugar (250 mg/g biomass) was measured. The activity of immobilized SEs is better than free crude SEs. The M-SE-CLEAs can be reused at least 3 times for effective saccharification of pretreated lignocellulosic biomass.

Per- and poly-fluoroalkyl substances (PFASs) are man-made chemicals that are toxic and widely detected in the environment, including drinking water sources. A cost-effective treatment process for PFASs is currently not available. We developed reusable hydrogel sorbents to remove long- and short-chain perfluoroalkyl acids and 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)propanoic acid (GenX), which is are emerging PFAS. Through fluoridation and amination of poly(ethylene glycol) diacrylate (PEGDA), the newly synthesized sorbents can sorb the five targeted PFASs (perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorobutanesulfonic acid (PFBS), and perfluorobutanoic acid (PFBA) and GenX) to different degrees from aqueous solution. Aminated PEGDA showed the highest sorption capacity for all five PFASs, particularly for PFBA and PFBS. The bifunctionalized PEGDA showed higher capacities for PFOA and PFOS, suggesting that both hydrophobic interactions and charges contribute to the sorption. Both aminated and bifunctionalized sorbents can remove GenX from water, with the highest sorption capacity of 98.7 μmol g aminated PEGDA–1 within 6 h. The absorbed PFASs on the sorbents were observed and characterized by Fourier-transform infrared spectroscopy. The spent sorbents were reusable after readily regenerated with 70% methanol contained 1% NaCl.

Poly(3-hydroxybutyrate) (PHB)-a renewable and biodegradable polymer-is a promising alternative to nonbiodegradable synthetic plastics that are derived from petrochemicals. The methods currently employed for PHB production are costly, in part, due to the expensive cultivation feedstocks and the need to sterilize the culture medium, which is energy-intensive. This study investigates the feasibility of nonsterile PHB production from several saline organic wastes using a salt-tolerant strain

Escherichia coli 4s is a Gram-negative bacterium found in the equine intestinal ecosystem alongside diverse other coliform bacteria and bacteriophages. This announcement describes the complete genome of the T7-like E. coli 4s podophage Penshu1. From its 39,263-bp genome, 54 protein-encoding genes and a 179-bp terminal repeat were predicted.

Polyhydroxybutyrate (PHB) is biodegradable and renewable and thus considered as a promising alternative to petroleum-based plastics. However, PHB production is costly due to expensive carbon sources for culturing PHB-accumulating microorganisms under sterile conditions. We discovered a hyper PHB-accumulating denitrifying bacterium, Zobellella denitrificans ZD1 (referred as strain ZD1 hereafter) capable of using non-sterile crude glycerol (a waste from biodiesel production) and nitrate to produce high PHB yield under saline conditions. Nevertheless, the underlying genetic mechanisms of PHB production in strain ZD1 have not been elucidated. In this study, we discovered a complete pathway of glycerol conversion to PHB, a novel PHB synthesis gene cluster, a salt-tolerant gene cluster, denitrifying genes, and an assimilatory nitrate reduction gene cluster in the ZD1 genome. Interestingly, the novel PHB synthesis gene cluster was found to be conserved among marine Gammaproteobacteria. Higher levels of PHB accumulation were linked to higher expression levels of the PHB synthesis gene cluster in ZD1 grown with glycerol and nitrate under saline conditions. Additionally, a clustered regularly interspaced short palindromic repeat (CRISPR)-Cas type-I-E antiviral system was found in the ZD1 genome along with a long spacer list, in which most of the spacers belong to either double-stranded DNA viruses or unknown phages. The results of the genome analysis revealed strain ZD1 used the novel PHB gene cluster to produce PHB from non-sterile crude glycerol under saline conditions.

Triacylglycerols (TAGs) are starting materials for the production of biolipid-based fuels such as biodiesel and biojet fuel. While various microorganisms can produce TAGs from renewable resources, the cultivation of TAG-producing microorganisms under sterilization conditions to avoid microbial contamination and application of solvent to extract TAGs from the TAG-filled microorganisms are costly. To overcome these challenges, this study reports the feasibility of a non-sterile cultivation of an oleaginous bacterium Rhodococcus opacus PD631SpAHB under saline conditions, followed by the use of a solvent-free, phage-lysis-protein-based bioextraction approach for TAGs release. The engineered strain PD631SpAHB was developed by introducing a recombinant plasmid carrying a phage lytic gene cassette (pAHB) into Rhodococcus opacus PD631 via transformation, followed by adaptive evolution under saline conditions. This newly developed strain is a salt-tolerant strain with the inducible plasmid pAHB to enable TAGs release into the supernatant upon induction. Cell lysis of PD631SpAHB was confirmed by the decrease of the optical density of cell suspension, by the loss of cell membrane integrity, and by the detection of TAGs in the culture medium. Up to 38% of the total TAGs accumulated in PD631SpAHB was released into supernatant after the expression of the lytic genes. PD631SpAHB strain is a promising candidate to produce TAGs from non-sterile growth medium and release of its TAGs without solvent extraction - a new approach to reduce the overall cost of biolipid-based biofuel production.

Lipid-based biofuel is a clean and renewable energy that has been recognized as a promising replacement for petroleum-based fuels. Lipid-based biofuel can be made from three different types of intracellular biolipids; triacylglycerols (TAGs), wax esters (WEs), and polyhydroxybutyrate (PHB). Among many lipid-producing prokaryotes and eukaryotes, biolipids from prokaryotes have been recently highlighted due to simple cultivation of lipid-producing prokaryotes and their ability to accumulate high biolipid contents. However, the cost of lipid-based biofuel production remains high, in part, because of high cost of lipid extraction processes. This review summarizes the production mechanisms of these different types of biolipids from prokaryotes and extraction methods for these biolipids. Traditional and improved physical/chemical approaches for biolipid extraction remain costly, and these methods are summarized and compared in this review. Recent advances in biological lipid extraction including phage-based cell lysis or secretion of biolipids are also discussed. These new techniques are promising for bacterial biolipids extraction. Challenges and future research needs for cost-effective lipid extraction are identified in this review.

Traffic lights perception problem is one of the key challenges for autonomous vehicle controllers in urban areas. While a number of approaches for traffic light detection have been proposed, these methods often require a prior knowledge of map and/or show high false positive rates. Recent successes suggest that deep neural networks will be widely used in self-driving cars, but current public datasets do not provide sufficient amount of labels for training such large deep neural networks. In this paper, we developed a two-step computational method that can detect traffic lights from images in a real-time manner. The first step exploits a deep neural object detection architecture to fine true traffic light candidates. In the second step, a point-based reward system is used to eliminate false traffic lights out of the candidates. To evaluate the proposed approach, we collected a human-annotated large-scale traffic lights dataset (over 60 hours). We also designed a real-world experiment with an instrumented self-driving vehicle and observed that the proposed method was able to handle false traffic lights substantially better compared with the baseline considered.

When a camera rotates rapidly or shakes severely, a conventional KLT (Kanade–Lucas–Tomasi) feature tracker becomes vulnerable to large inter-image appearance changes. Tracking fails in the KLT optimization step, mainly due to an inadequate initial condition equal to final image warping in the previous frame. In this paper, we present a gyro-aided feature tracking method that remains robust under fast camera–ego rotation conditions. The knowledge of the camera’s inter-frame rotation, obtained from gyroscopes, provides an improved initial warping condition, which is more likely within the convergence region of the original KLT. Moreover, the use of an eight-degree-of-freedom affine photometric warping model enables the KLT to cope with camera rolling and illumination change in an outdoor setting. For automatic incorporation of sensor measurements, we also propose a novel camera/gyro auto-calibration method which can be applied in an in-situ or on-the-fly fashion. Only a set of feature tracks of natural landmarks is needed in order to simultaneously recover intrinsic and extrinsic parameters for both sensors. We provide a simulation evaluation for our auto-calibration method and demonstrate enhanced tracking performance for real scenes with aid from low-cost microelectromechanical system gyroscopes. To alleviate the heavy computational burden required for high-order warping, our publicly available GPU implementation is discussed for tracker parallelization.

In this paper, we propose a low-complexity histogram of oriented gradients (HOG) implementation for efficient video saliency framework. After showing how original HOG calculations present significant computation bottleneck for visual understanding pipes, we present the optimized HOG flow and algorithm for video saliency framework, which can reduce computational requirements without losing algorithmic performance. Furthermore, simplification for light-weight computations and data-reusable scanning for optimal memory usage are explained for improving system efficiency. Based on our testing and analysis, the proposed HOG implementation optimizes computational complexity and performance while maintaining the video saliency algorithm capability.

Target tracking function is essential for autonomous human-interactive robots such as guide robots, security guard robots, pet robots etc. However, there is no detail study about the control ofmobile robots for this target tracking purpose. In this paper, target tracking of mobile robots is studied in functional viewpoint. It turned out that the robot basically has to have two functions to accomplish the target tracking behavior. Proper algorithms for these two functions will be introduced and verified with simulation study.

In sports, wearable gaze tracking devices can enrich the viewer experience and be a powerful training tool. Because devices can be used for long periods of time, often outside, it is desirable that they do not use active illumination (infra-red light sources) for safety reasons and to minimize the interference of the sun. Unlike traditional wearable devices, in sports, the gaze tracking method must be robust to (often dramatic) movements of the user in relation to the device (i.e., the common assumption that because the device is wearable, the eye does not move with regards to the camera no longer holds.) This paper extends a visible-spectrum gaze tracker in the literature to handle the requirements of a motor-sports application. Specifically, the method presented removes the assumption (in the original method) that the eye position is fixed, and proposes the use of template matching to allow for changes in the eye location from frame to frame. Experimental results demonstrate that the proposed method can handle severe changes in the eye location and is very fast to compute (up to 60 frames per second in modern hardware.).

We present a drift-free attitude estimation method that uses image line segments for the correction of accumulated errors in integrated gyro rates when an unmanned aerial vehicle (UAV) operates in urban areas. Since man-made environments generally exhibit strong regularity in structure, a set of line segments that are either parallel or orthogonal to the gravitational direction can provide visual measurements for the absolute attitude from a calibrated camera. Line segments are robustly classified with the assumption that a single vertical vanishing point or multiple horizontal vanishing points exist. In the fusion with gyro angles, we introduce a new Kalman update step that directly uses line segments rather than vanishing points. The simulation and experiment based on urban images at distant views are provided to demonstrate that our method can serve as a robust visual attitude sensor for aerial robot navigation.

We propose a novel inertial-aided KLT feature tracking method robust to camera ego-motions. The conventional KLT uses images only and its working condition is inherently limited to small appearance change between images. When big optical flows are induced by a camera-ego motion, an inertial sensor attached to the camera can provide a good prediction to preserve the tracking performance. We use a low-grade MEMS-based gyroscope to refine an initial condition of the nonlinear optimization in the KLT. It increases the possibility for warping parameters to be in the convergence region of the KLT. For longer tracking with less drift, we use the affine photometric model and it can effectively deal with camera rolling and outdoor illumination change. Extra computational cost caused by this higher-order motion model is alleviated by restraining the Hessian update and GPU acceleration. Experimental results are provided for both indoor and outdoor scenes and GPU implementation issues are discussed.

A GPU becomes an affordable solution for accelerating a slow process on a commercial system. The most of achievements using it for non-rendering problems are the exact re-implementation of existing algorithms designed for a serial CPU. We study about conditions of a good parallel algorithm, and show that it is possible to design an algorithm targeted to a parallel hardware, though it may be useless on a CPU. The optical flow estimation problem is investigated to show the possibility. In some time-critical applications, it is more important to get results in a limited time than to improve the results. We focus on designing optical flow approximation algorithms tailored for a GPU to get a reasonable result as fast as possible by reformulating the problem as change detection with hypothesis generation using features tracked in advance. Two parallel algorithms are proposed: direct interpolation and testing multiple hypotheses. We discuss implementation issues in the CUDA framework. Both methods are running on a GPU in a near video rate providing reasonable results for the time-critical applications. These GPU-tailored algorithms become useful by running about 240 times faster than the equivalent serial implementations which are too slow to be useful in practice.

We present an easy-to-use calibration method for MEMS inertial sensor units based on the factorization method which was originally invented for shape-and-motion recovery in computer vision. Our method requires no explicit knowledge of individual motions applied during calibration procedure. Instead a set of motion constraints in the form of an inner-product is used to factorize sensor measurements into a calibration matrix (that represents intrinsic sensor parameters) and a motion matrix (that represents acceleration or angular velocity). These motion constraints can be collected quickly from a low-cost calibration apparatus. Our method is not limited to just triad configurations but also applicable to any coordination of more than three sensor elements. A redundant configuration has the benefit that all the calibration parameters including biases are estimated at once. Simulation and experiments are provided to verify the proposed method.

An imaging sensor made of multiple light-weight non-overlapping cameras is an effective sensor for a small unmanned aerial vehicle that has strong payload limitation. This paper presents a method for motion estimation by assuming that such a multi-camera system is a spherical imaging system (that is, the cameras share a single optical center). We derive analytically and empirically a condition for a multi-camera system to be modeled as a spherical camera. Interestingly, not only does the spherical assumption simplify the algorithms and calibration procedure, but also motion estimation based on that assumption becomes more accurate.

We present an efficient two-phase approach to motion planning for small fixed-wing unmanned aerial vehicles (UAVs) navigating in complex 3D air slalom environments. A coarse global motion planner first computes a kinematically feasible obstacle-free path in a discretized 3D workspace which roughly satisfies the kinematic constraints of the UAV. Given a coarse global path, a fine local motion planner is used to compute a more accurate trajectory for the UAV at a higher level of detail. The local planner is iterated as the vehicle traverses and refines the global path as needed up to its planning horizon. We also introduce a new planning heuristic for 3D motions of fixed-wing UAVs based on 2D Dubins curves, along with precomputed sets of motion primitives derived from the vehicle dynamics model in order to achieve high efficiency.

This paper describes the major components of software architecture of an autonomous visitor guide robot with the brief explanation of its hardware system. This robot, “Butler,” was deployed in a real office environment, Intelligent Robot Research Center at KIST, where it guided many visitors successfully. To make the robot navigate the office and interact with visitors without problems, many challenges such as navigation in dynamic environments and interaction through various methods should be overcome. In this paper, the solutions for those problems are illustrated and our conclusion about this project is discussed.

Human friendly service robots such as home service robots, cleaning robots, entertainment robots take great attention in order to create new markets for robots since the markets for conventional industrial robots are saturating. It is a step to change the working environments of robots from industries to homes and offices, and to extend markets for robots from industrial markets to commercial home appliance markets. This paper introduces a home service robot 'ISSAC' applicable for vacuum cleaning, home security to find intruders and to transfer captured images of the intruder to the police or security companies, voice-based information service, real-time image transfer through wireless TCP/IP Ethernet and/or CDMA-2000 wireless communication, and a telepresence controllable by mobile computers such as pocket PC's and/or cellular phones. The overall controller is developed as a network-based layered modular controller using CAN (controller area network). The robot is tested in an apartment for a week successfully.

In this paper, we present a simple linear method for localization an indoor mobile robot based on a natural landmark model and a robust tracking algorithm. The landmark model is sets of three or more natural lines such as baselines, door edges and linear edges in tables or chairs, to take the advantages of fast landmark detection. Lucas-Kanade optical flow algorithm is applied to track the landmark model by using gradient descent. Then, a quick localization method for mobile robots from correspondent lines is proposed by adopting a linear technique. We present experimental results that demonstrate the robustness of the method with respect to image illumination and noises. The performance in indoor environments shows the feasibility of the proposed localization algorithm in real-time.