The proliferation of the Internet of Things (IoT) requires innovative solutions for all aspects of computing, including storage. The small footprint of IoT devices limits their capacity for local reliable storage. A solution is presented which combines local and cloud storage in a RAID-like (Redundant Array of Independent Disks) configuration, increasing the amount of storage, access speed, and/or data reliability and availability for systems which implement the discussed configurations. Previously, cloud-RAID, where data is distributed across multiple cloud storage providers, has been proposed and implemented. However, the current architectures place an emphasis on RAID 0, and other levels of RAID with their application to cloud storage have not been thoroughly explored. A novel architecture for local+cloud-RAID storage is presented, and benefits provided by the architecture in the areas of availability, reliability, and security are discussed. An effort to quantify the reliability of various configurations of RAID, cloud-RAID, and hybrid local+cloud-RAID levels will be made. While RAID 4 has been widely regarded as obsolete and supplanted by RAID 5, we argue that RAID 4 can be useful in a local+cloud-RAID configuration. A new RAID level based on RAID 4, with the addition of a second dedicated parity drive, is proposed, and is deemed RAID 4.5. We conclude that cloud storage, from the perspectives of availability, reliability, security, and performance, is beneficial to include in various RAID configurations which include local drives.

Real-time stereo vision has proven to be a useful technology with many applications. However, the computationally intensive nature of stereo vision algorithms makes real-time implementation difficult in resource-limited systems. The field-programmable gate array (FPGA) has proven to be very useful in the implementation of local stereo methods, yet the resource requirements can still be a significant challenge. This paper proposes a variety of sparse census transforms that dramatically reduce the resource requirements of census-based stereo systems while maintaining stereo correlation accuracy. This paper also proposes and analyzes a new class of census-like transforms, called the generalized census transforms. This new transform allows a variety of very sparse census-like stereo correlation algorithms to be implemented while demonstrating increased robustness and flexibility. The resource savings and performance of these transforms is demonstrated by the design and implementation of a parameterizable stereo system that can implement stereo correlation using any census transform. Several optimizations for typical FPGA-based correlation systems are also proposed. The resulting system is capable of running at over 500 MHz on a modern FPGA, resulting in a throughput of over 500 million input pixel pairs per second.

This paper presents a feature descriptor well suited for limited-resource applications such as an unmanned aerial vehicle embedded systems, small microprocessors, and small low-power field programmable gate array (FPGA) fabric. The basis sparse-coding inspired similarity (BASIS) descriptor utilizes sparse coding to create dictionary images that model the regions in the human visual cortex. Due to the reduced amount of computation required for computing BASIS descriptors, reduced descriptor size, and the ability to create the descriptors without the use of a floating point, this approach is an excellent candidate for FPGA hardware implementation. The bit-level-accurate BASIS descriptor was tested on a dataset of real aerial images with the task of calculating a frame-to-frame homography and compared to software versions of scale-invariant feature transform (SIFT) and speeded-up robust features (SURF). Experimental results show that the BASIS descriptor outperforms SIFT and performs comparably to SURF on frame-to-frame aerial feature point matching. BASIS descriptors require less memory storage than other descriptors and can be computed entirely in hardware, allowing the descriptor to operate at real-time frame rates on a low-power embedded platform such as an FPGA.

Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept.

It is evident that the accuracy of stereo vision algorithms has continued to increase based on commonly used quantitative evaluations of the resulting disparity maps. This paper focuses on the development of promising stereo vision algorithms that efficiently tradeoff accuracy for large reductions in required computational resources. An intensity profile shape-matching algorithm is introduced as an example of an algorithm that makes such tradeoffs. The proposed algorithm is compared to both a basic sum-of-absolute-differences (SAD) block-matching algorithm, as well as a stereo vision algorithm that is highly ranked for its accuracy based on the Middlebury evaluation criteria. This comparison shows that the proposed algorithm's accuracy on the commonly used Tsukuba stereo image pair is lower than many published stereo vision algorithms, but that for unrectified stereo image pairs that have even the slightest differences in brightness, it is potentially more robust than algorithms that rely on SAD block matching. An example application that requires 3-D information is implemented to show that the accuracy of the proposed algorithm is sufficient for this use. Timing results show that this is a very fast dense-disparity stereo vision algorithm when compared to other algorithms capable of running on a standard microprocessor.

Accurate optical flow estimation is a crucial task for many computer vision applications. However, because of its computational power and processing speed requirements, it is rarely used for real-time obstacle detection, especially for small unmanned vehicle and embedded applications. Two hardware-friendly vision algorithms are proposed in this paper to address this challenge. A ridge regression-based optical flow algorithm is developed to cope with the existing collinear problem in traditional least-squares approaches for calculating optical flow. Additionally, taking advantage of hardware parallelism, spatial and temporal smoothing operations are applied to image sequence derivatives to improve accuracy. An efficient motion field analysis algorithm using the optical flow values and based on a simplified motion model is also developed and implemented in hardware. The resulting obstacle detection algorithm is specifically designed for ground vehicles moving on planar surfaces. Results from the software simulations and hardware execution of the two proposed algorithms prove that with adequate hardware, a low power, compact obstacle detection sensor can be realized for small unmanned vehicles and embedded applications.

Digital image processing in the field of time-lapse microscopy and biological research has been an interesting research area to provide statistical data of cellular dynamics to the field of cell biology. Digital image processing enables rapid and consistent quantification of qualitative observations. The image processing model examined here provides a tool for the biologists to identify structures called retraction fibers (RF) that are formed during epithelial-mesenchyme transition (EMT), an important developmental process which also occurs during cancer metastasis. Quantifying RF formation is an important tool for biologists studying cellular regulation of EMT. The dynamic EMT process is captured using time-lapse microscopy. We use computer vision algorithms to detect and track the RF in image sequences of cells undergoing EMT to generate statistical information such as the number of RF formed during a window, lifetime of the RF, and their geometric dimension. This information can in turn be used by the biologist to quantitatively measure the extent of EMT under different test conditions.

A novel unsupervised clustering algorithm called Hyperclique Pattern-KMEANS (HP-KMEANS) is presented. Considering recent success in semi-supervised clustering using pair-wise constraints, an unsupervised clustering method that selects constraints automatically based on Hyperclique patterns is proposed. The COP-KMEANS framework is then adopted to cluster instances of data sets into corresponding groups. Experiments demonstrate promising results compared to classical unsupervised k-means clustering.

In this paper we propose a novel image contrast enhancement method using collaborative learning. Block-based histogram equalization methods such as contrast limited adaptive histogram equalization (CLAHE) and exact histogram equalization consider only a local window or neighboring windows for contrast enhancement. Inspired by the collaborative learning of individuals in a knowledge-creating community, we propose using random spatial sampling on the image to create multiple individuals, followed by normalization of the entire image according to the perspectives of these individuals. Our method not only increases the range of intensities for the entire image, but also enhances details of relatively homogeneous regions. Experiments demonstrate very good results.

An accurate real-time motion sensor implemented in an FPGA is introduced in this paper. This sensor applies an optical flow algorithm based on ridge regression to solve the collinear problem existing in traditional least squares methods. It additionally applies extensive temporal smoothing of the image sequence derivatives to improve the accuracy of its optical flow estimates. Implemented on a customized embedded FPGA platform, it is capable of processing 60 320 × 240 images or 15 640 × 480 images per second. By evaluating its accuracy on synthetic sequences, it is shown here that the proposed design achieves very high accuracy compared to other known hardware-based designs.

Future generations of air traffic management systems may give appropriately equipped aircraft the freedom to change flight paths in real time. This would require a conflict avoidance and resolution scheme that is both decentralized and cooperative. We describe a multiagent solution to aircraft conflict resolution based on satisficing game theory. A key feature of the theory is that satisficing decision makers form their preferences by taking into consideration the preferences of others, unlike conventional game theory that models agents that maximize self-interest metrics. This makes possible situational altruism, a sophisticated form of unselfish behavior in which the preferences of another agent are accommodated provided that the other agent will actually take advantage of the sacrifice. This approach also makes possible the creation of groups in which every decision maker receives due consideration. We present simulation results from a variety of scenarios in which the aircraft are limited to constant-speed heading-change maneuvers to avoid conflicts. We show that the satisficing approach results in behavior that is attractive both in terms of safety and performance. The results underscore the applicability of satisficing game theory to multiagent problems in which self-interested participants are inclined to cooperation.

With the adoption of 2G and 3G cellular network technologies, mobile phones now have the bandwidth capability to stream data back to monitoring stations in real-time. Our paper describes the design and evaluation of a Bluetooth electrocardiogram sensor that transmits medical data to a cell phone. This data is displayed and stored on the phone. Future development of the system will relay this data over a cellular GPRS network. The current system provides a low cost and lightweight alternative to existing EKG event monitors. The final GPRS connected system will provide continuous monitoring of a patient’s heart anywhere cellular coverage is available.

Negotiation procedures that are founded on the doctrine of individual rationality, where each participant is committed to maximizing its own satisfaction, are limited in their ability to accommodate the interests of others, and therefore, may unnecessarily constrain the negotiability of a decision maker, particularly in cooperative environments. Satisficing game theory provides a distinct alternative to the hyperrationality of conventional rational choice by waiving reliance on the individual rationality premise and offering an approach to negotiatory decision making that is based on a well-defined mathematical notion of satisficing, or being good enough, that permits the modeling of complex interrelationships between agents. This approach provides a mechanism to compute the attitude, or degree of conflict or contentedness, of the negotiators. Examples illustrate both single-round and multiround satisficing negotiation protocols

Searching a random graph is known to be computationally complex. The classic example for the single agent case is the traveling salesman problem. In this paper, we generalize to the multi-agent case where n autonomous agents must cooperate to visit all the nodes of a random graph in an efficient manner. We impose a limitation on the agent's ability to communicate with each other. A satisficing game approach is ideally suited to this problem since it accommodates multiple solutions for each player and provides the opportunity for negotiation. We demonstrate performance of this approach on both deterministic and random graphs with up to 100 nodes.

This paper describes a new multi-agent test-bed developed to support research in multi-agent systems that incorporate both autonomous operation and human input. Teams compete in a modified version of the game of capture-the-flag in a maze-like environment of arbitrary complexity. Each team consists of a number of mobile robots, a human commander, and a UAV that provides essential position information. The makeup of each team, the motion constraints, and the nature of the game present a significant challenge to any multi-agent coordination scheme. This paper describes the rules of the game and the hardware and software infrastructure we have developed to play it. The paper further describes some of our work in devising an effective technique for planning paths through the maze for the ground-based robots.

Addresses the problem of positioning a group of autonomous but coordinating mobile robots into a specified spatial configuration, with each position occupied by a single robot. Specifically, we assume that there is no central controller or inter-agent communication, and require that the robots must move into position without collision or unnecessary delay. We present an approach to this problem based on praxeic utility theory and evaluate its effectiveness, with particular emphasis on computational feasibility.

This brief contribution introduces a method of quantifying the locality in a given reference sequence and visually representing it as a three dimensional surface. We explore some of the properties of this formulation of locality and show the correlation between graphical features and specific reference patterns. The utility of our formulation of locality is demonstrated through two of its potential applications as a visualizations tool: characterizing and summarizing workload locality, and evaluating the effectiveness of benchmark programs in exercising memory hierarchies.

A new disk caching algorithm is presented that uses an adaptive prefetching scheme to reduce the average service time for disk references. Unlike schemes which simply prefetch the next sector or group of sectors, this method maintains information about the order of past disk accesses which is used to accurately predict future access sequences. The range of parameters of this scheme is explored, and its performance is evaluated through trace-driven simulation, using traces obtained from three different UNIX minicomputers. Unlike disk trace data previously described in the literature, the traces used include time stamps for each reference. With this timing information-essential for evaluating any prefetching scheme-it is shown that a cache with the adaptive prefetching mechanism can reduce the average time to service a disk request by a factor of up to three, relative to an identical disk cache without prefetching.>

This paper explores the architecture of high-performance large scale multiprocessors using private caches for each processor. The caches reduce the average memory access time, but they also result in the well known cache coherence problem. Multiple copies of each memory location are allowed to exist but they must be kept consistent with each other. In this paper, we present a solution to the cache coherence problem specifically for shared bus multiprocessors that adapts dynamically to the reference pattern. Simulation results are presented that demonstrate the high level of performance relative to other protocols particularly during intervals with high levels of sharing.

Using simulation, we examine the efficiency of several distributed, hardware-based solutions to the cache coherence problem in shared-bus multiprocessors. For each of the approaches, the associated protocol is outlined. The simulation model is described, and results from that model are presented. The magnitude of the potential performance difference between the various approaches indicates that the choice of coherence solution is very important in the design of an efficient shared-bus multiprocessor, since it may limit the number of processors in the system.

In this paper we review and qualitatively evaluate schemes to maintain cache coherence in tightly-coupled multiprocessor systems. This leads us to propose a more economical (hardware-wise), expandable and modular variation of the “global directory” approach. Protocols for this solution are described. Performance evaluation studies indicate the limits (number of processors, level of sharing) within which this approach is viable.