Jaerock Kwon

Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application

An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. The proposed system made good use of a popular technology that combines a Smartphone application with a microcontroller. This will be easy to make and inexpensive compared to others. The designed in-vehicle device works using Global Positioning System (GPS) and Global system for mobile communication / General Packet Radio Service (GSM/GPRS) technology that is one of the most common ways for vehicle tracking. The device is embedded inside a vehicle whose position is to be determined and tracked in real-time. A microcontroller is used to control the GPS and GSM/GPRS modules. The vehicle tracking system uses the GPS module to get geographic coordinates at regular time intervals. The GSM/GPRS module is used to transmit and update the vehicle location to a database. A Smartphone application is also developed for continuously monitoring the vehicle location. The Google Maps API is used to display the vehicle on the map in the Smartphone application. Thus, users will be able to continuously monitor a moving vehicle on demand using the Smartphone application and determine the estimated distance and time for the vehicle to arrive at a given destination. In order to show the feasibility and effectiveness of the system, this paper presents experimental results of the vehicle tracking system and some experiences on practical implementations.

Multiscale exploration of mouse brain microstructures using the knife-edge scanning microscope brain atlas.

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions.

Fast macro-scale transmission imaging of microvascular networks using KESM

Accurate microvascular morphometric information has significant implications in several fields, including the quantification of angiogenesis in cancer research, understanding the immune response for neural prosthetics, and predicting the nature of blood flow as it relates to stroke. We report imaging of the whole mouse brain microvascular system at resolutions sufficient to perform accurate morphometry. Imaging was performed using Knife-Edge Scanning Microscopy (KESM) and is the first example of this technique that can be directly applied to clinical research. We are able to achieve ≈ 0.7μm resolution laterally with 1μm depth resolution using serial sectioning. No alignment was necessary and contrast was sufficient to allow segmentation and measurement of vessels.

Evolution of recollection and prediction in neural networks

A large number of neural network models are based on a feedforward topology (perceptrons, backpropagation networks, radial basis functions, support vector machines, etc.), thus lacking dynamics. In such networks, the order of input presentation is meaningless (i.e., it does not affect the behavior) since the behavior is largely reactive. That is, such neural networks can only operate in the present, having no access to the past or the future. However, biological neural networks are mostly constructed with a recurrent topology, and recurrent (artificial) neural network models are able to exhibit rich temporal dynamics, thus time becomes an essential factor in their operation. In this paper, we will investigate the emergence of recollection and prediction in evolving neural networks. First, we will show how reactive, feedforward networks can evolve a memory-like function (recollection) through utilizing external markers dropped and detected in the environment. Second, we will investigate how recurrent networks with more predictable internal state trajectory can emerge as an eventual winner in evolutionary struggle when competing networks with less predictable trajectory show the same level of behavioral performance. We expect our results to help us better understand the evolutionary origin of recollection and prediction in neuronal networks, and better appreciate the role of time in brain function.

Internal state predictability as an evolutionary precursor of self-awareness and agency

What is the evolutionary value of self-awareness and agency in intelligent agents? One way to make this problem tractable is to think about the necessary conditions that lay the foundation for the emergence of agency, and assess their evolutionary origin.We postulate that one such requirement is the predictability of the internal state trajectory. A distinct property of onepsilas own actions compared to someone elsepsilas is that onepsilas own is highly predictable, and this gives the sense of ldquoauthorshiprdquo. In order to investigate if internal state predictability has any evolutionary value, we evolved sensorimotor control agents driven by a recurrent neural network in a 2D pole-balancing task. The hidden layer activity of the network was viewed as the internal state of an agent, and the predictability of its trajectory was measured. We took agents exhibiting equal levels of performance during evolutionary trials, and grouped them into those with high or low internal state predictability (ISP). The high-ISP group showed better performance than the low-ISP group in novel tasks with substantially harder initial conditions. These results indicate that regularity or predictability of neural activity in internal dynamics of agents can have a positive impact on fitness, and, in turn, can help us better understand the evolutionary role of self-awareness and agency.

Autonomous tour guide robot by using ultrasonic range sensors and QR code recognition in indoor environment

This paper addresses the challenge of mobile robot navigation in indoor environments. There is a critical need for cost-effective, reliable, and fairly accurate solutions to meet the demands of indoor robotic applications. Currently, researchers are exploring various approaches for this problem. The one we are presenting in this paper is based on QR (Quick Response) codes to provide location references for mobile robots. The mobile robot is equipped with a Smartphone that is programmed to detect and read information on QR codes that are strategically placed in the operating environment of the robot. The mobile robot can perform the autonomous run throughout the guide route by using real-time QR code recognition. The lab information on QR code is played to the visitors using Text-to-Speech provided through Android device. Ultrasonic range sensors which can detect objects and measure distances with high accuracy are used to implement the wall-following and obstacle-avoidance behaviors. The collected sonar range information by ultrasonic range sensors is processed by a microcontroller that autonomously controls a tour guide robot. An algorithm based on a proportional-integral-derivative (PID) control is applied to the tour guide robot to perform more accurate robot motion control. A Bluetooth technology is used to send stored information on QR codes from the Smartphone to the tour guide robot wirelessly. The experimental setup of the tour guide robot along with the successful implementation of the efficient method for a navigation technique is presented.

Ultrasonic-sensor deployment strategies and use of smartphone sensors for mobile robot navigation in indoor environment

This paper presents deployment strategies of ultrasonic sensors and a way of using Smartphone sensors to help mobile robot navigation in indoor environments. There are critical needs for cost-effective, reliable, and fairly accurate solutions to meet the demands of indoor robotic applications. Ultrasonic sensors have been popular in detecting simple objects due to the low-cost and simplicity despite their limitations. We propose an efficient way of deployment of ultrasonic sensors for low-cost mobile robots. A Smartphone has many high performance sensors that can be utilized to navigate and localize mobile robots. The sensors include a camera, a gyroscope, and an accelerometer. We analyzed the use of orientation sensor of a Smartphone and compared its performance to a conventional approach. The comparison results were promising. The combination of the efficient way of the sensor deployment and the use of Smartphone sensors shows a possibility of developing a low-cost indoor mobile robotics platform for college education and robotics research laboratories.

Fast cell detection in high-throughput imagery using GPU-accelerated machine learning

High-throughput microscopy allows fast imaging of large tissue samples, producing an unprecedented amount of sub-cellular information. The size and complexity of these data sets often out-scale current reconstruction algorithms. Overcoming this computational bottleneck requires extensive parallel processing and scalable algorithms. As high-throughput imaging techniques move into main stream research, processing must also be inexpensive and easily available. In this paper, we describe a method for cell soma detection in Knife-Edge Scanning Microscopy (KESM) using machine learning. The proposed method requires very little training data and can be mapped to consumer graphics hardware, allowing us to perform real-time cell detection at a rate that exceeds the data rate of KESM.

TIME, CONSCIOUSNESS, AND MIND UPLOADING

The function of the brain is intricately woven into the fabric of time. Functions such as (i) storing and accessing past memories, (ii) dealing with immediate sensorimotor needs in the present, and (iii) projecting into the future for goal-directed behavior are good examples of how key brain processes are integrated into time. Moreover, it can even seem that the brain generates time (in the psychological sense, not in the physical sense) since, without the brain, a living organism cannot have the notion of past nor future. When combined with an evolutionary perspective, this seemingly straightforward idea that the brain enables the conceptualization of past and future can lead to deeper insights into the principles of brain function, including that of consciousness. In this paper, we systematically investigate, through simulated evolution of artificial neural networks, conditions for the emergence of past and future in simple neural architectures, and discuss the implications of our findings for conscious...

Knife-edge scanning microscopy for connectomics research

In this paper, we will review a novel microscopy modality called Knife-Edge Scanning Microscopy (KESM) that we have developed over the past twelve years (since 1999) and discuss its relevance to connectomics and neural networks research. The operational principle of KESM is to simultaneously section and image small animal brains embedded in hard polymer resin so that a near-isotropic, sub-micrometer voxel size of 0.6 µm × 0.7 µm × 1.0 µm can be achieved over ∼1 cm3 volume of tissue which is enough to hold an entire mouse brain. At this resolution, morphological details such as dendrites, dendritic spines, and axons are visible (for sparse stains like Golgi). KESM has been successfully used to scan whole mouse brains stained in Golgi (neuronal morphology), Nissl (somata), and India ink (vasculature), providing unprecedented insights into the system-level architectural layout of microstructures within the mouse brain. In this paper, we will present whole-brain-scale data sets from KESM and discuss challenges and opportunities posed to connectomics and neural networks research by such detailed yet system-level data.