Food environment index and preterm birth rate in the counties of the United States.

OBJECTIVES: To determine the relationship between Food Environment Index (FEI) and Preterm Birth (PTB) rate at the county level of the United States of America (USA) (primary), while evaluating the interaction of multiple factors within a framework of sociodemographic, maternal health, maternal behavioral, and environmental factors. METHODS: This is a population-based retrospective cohort ecological study from 2015-2018. The study compares the characteristics of the population of the counties of the USA. All counties with complete data on their PTB rate and the independent variables were included in the study. Independent variables with greater than 20% missing data were excluded from the study. Purposive sampling technique was applied. A total of 2983/3142 counties were included in the study. RESULTS: The median PTB rate of all counties was 9.90%. The highest PTB rate (23.3%) was in Tallapoosa County, Alabama and the lowest (3.4%) in San Juan County, Washington State. After adjusting for variables, PTB rate had a significant association with FEI (coefficient of correlation - 0.36, p < 0.01, 95% CI - 0.19 to - 0.04). Increase in the rate of unemployment, African American race, adult smoking, obesity, uninsured rate, sexually transmitted diseases (STD), high school education and air pollution was associated with an increase in PTB rate, while an increase in FEI and alcohol abuse rates was associated with a decrease in PTB rate. CONCLUSIONS: FEI can predict the PTB rate in USA counties after adjusting for sociodemographic, health, behavioral and environmental factors. Future studies are needed to confirm these associations and consider them when making policies to reduce PTBs.

A neuro-fuzzy controller for mobile robot navigation and multirobot convoying.

A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets.

A neural network filter to detect small targets in high clutter backgrounds.

The detection of objects in high-resolution aerial imagery has proven to be a difficult task. In the authors' application, the amount of image clutter is extremely high. Under these conditions, detection based on low-level image cues tends to perform poorly. Neural network techniques have been proposed in object detection applications due to proven robust performance characteristics. A neural network filter was designed and trained to detect targets in thermal infrared images. The feature extraction stage was eliminated and raw gray levels were utilized as input to the network. Two fundamentally different approaches were used to design the training sets. In the first approach, actual image data were utilized for training. In the second case, a model-based approach was adopted to design the training set vectors. The training set consisted of object and background data. The neuron transfer function was modified to improve network convergence and speed and the backpropagation training algorithm was used to train the network. The neural network filter was tested extensively on real image data. Receiver operating characteristic (ROC) curves were determined in each case. The detection and false alarm rates were excellent for the neural network filters. Their overall performance was much superior to that of the size-matched contrast-box filter, especially in the images with higher amounts of visual clutter.

Circular-Mellin features for texture segmentation.

Texture is an important cue in region-based segmentation of images. We provide an insight into the development of a new set of distortion-invariant texture operators. These "circular-Mellin" operators are invariant to both scale and orientation of the target and represent the spectral decomposition of the image scene in the polar-log coordinate system. Coupled with the unique shift invariance property of the correlator architecture, we show that these circular-Mellin operators can be used for rotation-and scale-invariant feature extraction. We note that while these feature extractors have a functional form that is similar to the Gabor operators, they have distortion-invariant characteristics unlike the Gabor functions that make them more suitable for texture segmentation. A detailed analytical description of these operators and segmentation results to highlight their salient properties are presented.

Simulation and animation of sensor-driven robots.

Most simulation and animation systems utilized in robotics are concerned with simulation of the robot and its environment without simulation of sensors. These systems have difficulty in handling robots that utilize sensory feedback in their operation. In this paper, a new design of an environment for simulation, animation, and visualization of sensor-driven robots is presented. As sensor technology advances, increasing numbers of robots are equipped with various types of sophisticated sensors. The main goal of creating the visualization environment is to aid the automatic robot programming and off-line programming capabilities of sensor-driven robots. The software system will help the users visualize the motion and reaction of the sensor-driven robot under their control program. Therefore, the efficiency of the software development is increased, the reliability of the software and the operation safety of the robot are ensured, and the cost of new software development is reduced. Conventional computer-graphics-based robot simulation and animation software packages lack of capabilities for robot sensing simulation. This paper describes a system designed to overcome this deficiency.