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This paper describes efforts to develop a method of determining soiling levels on heliostat mirrors from images taken from aerial drones. Lessons learned from a flight mission relate to drone positioning errors due to heat and GPS drift, and the challenges of handling lots of data. A method to determine waypoints for missions is suggested, to gather data in a random manner with a known confidence level. Methods of interpreting the images taken are described, including a processing pipeline for identification and segmentation of individual mirror facets. Finally, the paper focusses on efforts to develop and validate a method of determining reflectance from visual images, including development of a sky fitting model, examination of the impact of image resolution, and presentation of the estimated reflectance compared to reference results taken with a hand-held reflectometer. The initial results show promise, currently with accuracy within around 2-3% for soiling cases most typical in a commercial heliostat field, however further improvements is required to achieve the target accuracy of

Solar energy is seen as a sustainable and non-depletable source of energy supply. Worldwide, large-scale solar power infrastructure is being installed every day. Such structures can suffer from many faults and defects that degrade their energy output during their operational life. Detecting such faults and defects requires regular inspection over physically large and distributed solar infrastructure. On-site manual human inspection tends to be impractical, risky and costly. As such, replacing humans with autonomous robotic aerial inspection systems has great potential. In this work, we propose an unmanned aerial vehicle (UAV) waypoint generation system that is specifically designed for aerial inspection of solar infrastructure. Our system takes into consideration the physical structure and the dynamic nature of sun-tracking solar modules and generates waypoints with the right camera viewing pose and drone orientation. Statistical methods are used to generate a randomly selected set of modules as a representation of the entire solar farm. The set is guaranteed to satisfy a user-defined confidence level and margin of error requirements. A path is generated to visit selected modules in an optimal way by deploying the traveling-salesman shortest path algorithm, allowing the vehicle to maximize battery use. Illustrative flights and preliminary inspection results are presented and discussed.

The detection of the metallic content of landmines is the predominant methodology employed to locate active mines. Contemporary landmines replace previously metal components with plastics, wood and glass to avoid detection. The situation has required increasingly sensitive metal detection equipment which may not always be available or effective. However, the internal explosives material has remained largely consistent. This paper exploits the changing thermal mass of this explosives material as it is heated by the sun, cooling at a delayed rate of the surrounding soil temperature. This change is recorded and measured by a thermal camera attached to an Unmanned Aerial Vehicle (UAV) over several diurnal cycles, and then analyzed to identify the location of a buried landmine. A series of thermal images of surrogate landmines captured in October-November 2017, revealed clear shapes, with $> 2$ Kelvin thermal differences to their surroundings. This indicates that UAV thermal imaging can be used for landmine detection. The techniques and method presented in this paper can be easily replicated, resulting in the identification, removal of dormant landmines in most environments, saving lives and reducing casualties.

Radio frequency energy harvesting (RFEH) is emerging as a highly promising energy replenishment approach for contemporary and futuristic wireless applications such as wireless sensor networks and the Internet-of-Things. An important performance metric of the harvested energy, which has often been omitted in the literature, is the battery recharge time and its dependency on the harvester's conversion efficiency, battery parameters, and most crucially, wireless propagation environment. Against this background, in this work we perform novel statistical analysis for the battery recharge time over highly generalized wireless fading channels. To this end, we consider α-κ-μ and α-η-μ fading models which accurately characterize the line-of-sight (LOS) and non-line- of-sight (NLOS) propagation scenarios, respectively. Notably, the two models are also suitable for signal shadowing effect. With the novel analysis, we derive new closed-form expressions for the probability density function, cumulative distribution function, and the nth moment of the recharge time. Based on the nth moment, we derive the mean, variance, skewness, and kurtosis of the recharge time. The newly derived expressions are analytically tractable and include important fading models such as Rayleigh, Rice, Hoyt, and Nakagami-m as special cases. Theoretical results are verified via numerical evaluation. Finally, we explicitly examine the impact of harvester's conversion efficiency as well as the fading and battery parameters on the statistics of the recharge time.

Computer vision is one of the most active research fields in technology today. Giving machines the ability to see and comprehend the world at the speed of sight creates endless applications and opportunities. Feature detection and description algorithms are considered as the retina for machine vision. However, most of these algorithms are typically computationally intensive, which prevents them from achieving real-time performance. As such, embedded vision accelerators (FPGA, ASIC, etc.) can be targeted due to their inherent parallelizability. This chapter provides a comprehensive study on some of the recent feature detection and description algorithms and their hardware solutions. Specifically, it begins with a synopsis on basic concepts followed by a comparative study, from which the maximally stable extremal regions (MSER) and the scale invariant feature transform (SIFT) algorithms are selected for further analysis due to their robust performance. The chapter then reports some of their recent algorithmic derivatives and highlights their recent hardware designs and architectures.

Radio frequency energy harvesting is a promising energy replenishment method for the next generation wireless networks. A key performance metric of energy harvesters is the recharge time, which is often omitted in the literature. On the receiver side, the battery parameters and the conversion efficiency are very critical to obtain a realistic evaluation of the recharge time. On the wireless propagation side, practical channel models that accurately characterize the wireless path of the propagating signal needs to be taken into account. To fill this important research gap, in this paper we present a novel and unified statistical modeling of the recharge time for a capacity-limited battery in the generalized η-μ and κ-μ wireless fading channel. Specifically, new unified and exact closed-form expressions are derived for the probability density function, cumulative distribution function, mean, variance and nth order moments. Due to the strong physical background and practicality of the η-μ and κ-μ generalized fading model, our results include important fading models, such as the Rayleigh, Rician, Hoyt, and Nakagami-m models, as special cases. The paper also provides a comprehensive study of the effect of the battery/fading parameters and the conversion efficiency on the harvested energy and the recharge time, which is supported and verified using numerical results.

Implementing high-performance, low-cost hardware accelerators for the computationally intensive image and video processing algorithms has attracted a lot of attention in the last 20 years. Most of the recent research efforts were trying to figure out new design automation methods to fill the gap between the ability of realizing efficient accelerators in hardware and the tight performance requirements of the complex image processing algorithms. High-Level synthesis (HLS) is a new method to automate the design process by transforming high-level algorithmic description into digital hardware while satisfying the design constraints. This chapter focuses on evaluating the suitability of using HLS as a new tool to accelerate the most demanding image and video processing algorithms in hardware. It discusses the gained benefits and current limitations, the recent academic and commercial tools, the compiler's optimization techniques and four case studies.

In this paper, we introduce a novel performance analysis of the η-μ generalized radio fading channels with integer value of the μ fading parameter, i.e. with even number of multipath clusters. This fading model includes other fading models as special cases such as the Nakagami-rn, the Hoyt, and the Rayleigh. We obtain novel unified and generic simple closed-form expressions for the average bit error rates and ergodic channel capacity in the additive white generalized Gaussian noise (AWGGN), which includes the additive Gaussian, the gamma, the Laplacian, and the impulsive noise as special cases. The receiver is assumed to be an L-branch maximal ratio combiner where we study the effect of having more deployed receiver antenna. Numerical evaluation as well as results from technical wireless literature validate the generality and the accuracy of the derived unified expressions under the studied test cases.

Computer vision is one of the most active research fields in information technology today. Giving machines and robots the ability to see and comprehend the surrounding world at the speed of sight creates endless potential applications and opportunities. Feature detection and description algorithms can be indeed considered as the retina of the eyes of such machines and robots. However, these algorithms are typically computationally intensive, which prevents them from achieving the speed of sight real-time performance. In addition, they differ in their capabilities and some may favor and work better given a specific type of input compared to others. As such, it is essential to compactly report their pros and cons as well as their performances and recent advances. This paper is dedicated to provide a comprehensive overview on the state-of-the-art and recent advances in feature detection and description algorithms. Specifically, it starts by overviewing fundamental concepts. It then compares, reports and discusses their performance and capabilities. The Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature Transform algorithms, being two of the best of their type, are selected to report their recent algorithmic derivatives., Comment: Annual IEEE Industrial Electronics Societys 18th International Conf. on Industrial Technology (ICIT), 22-25 March, 2017

In this paper, a novel unified analytical expression for average bit error rates (ABER) and average channel capacity (ACC) is presented for the ${\kappa}-{\mu}$ shadowed fading model. This new shadowed fading model has shown to be suitable for underwater wireless channel modeling for the measurements conducted by the Naval Research Laboratory, and is not so well covered in the public literature. Deploying the Maximal Ratio Combining (MRC) receiver, a new simple analytical expression for the probability density function (PDF) of the receiver output signal to noise ratio is presented. Based on this new PDF, the novel unified ABER and ACC expression derived. To further generalize the error rate analysis, the additive white generalized Gaussian noise model is assumed that models the different noise environments. The new unified expression is accurate, simple and generic, and is suitable for MRC analysis in this generalized shadowed fading and noise environment. Numerical techniques and published results from the technical literature corroborate the generality and accuracy of our analysis under the different test scenarios., Comment: 42nd Annual Conference of the IEEE Industrial Electronics Society (IECON 2016), Florence, Italy, 23-26 Oct. 2016

This paper presents a novel implementation of the Maximally Stable Extremal Regions (MSER) detector on system-on-chip (SoC) using 65nm CMOS technology. The novel SoC was developed following the Application Specific Integrated Circuit (ASIC) design flow which significantly enhanced its realization and fabrication, and overall performances. The SoC has very low area requirement (around 0.05 mm2) and is capable of detecting both bright and dark MSERs in a single run, while computing simultaneously their associated regions' moments, simplifying its interfacing with other image algorithms (e.g. SIFT and SURF). The novel MSER SoC is power-efficient (requires 2.25 mW) and memory-efficient as it saves more than 31% of the memory space reported in the state-of-the-art MSER implementation on FPGA, making it suitable for mobile devices. With 256×256 resolution and its operating frequency of 133 MHz, the SoC is expected to have a 200 frames/second processing rate, making it suitable (when integrated with other algorithms in the system) for time-critical real-time applications such as visual surveillance.

This paper presents a new fast and scalable Parallel Union-Find algorithm for image segmentation and its System-on-Chip (SoC) implementation using 65nm CMOS technology following the Application-Specific Integrated Circuit (ASIC) design flow. The algorithm is capable of labeling all foreground and background pixels, using the least possible pixels scanning. This contrasts the classical labeling algorithms that label only foreground (or background) pixels in a single run. The new algorithm utilizes only two memory blocks. In one memory block, it labels image segments using their seeds as the label and, simultaneously, the segments sizes are used as the other label in second memory block. By this parallel labeling, monitoring the image segments is very fast and efficient. With 350 MHz operating frequency, the processing rate estimated to be 2100 frames/sec, the total chip area of 15950.5 μm2 (off-chip memory) and very low-power of 0.3 mW, the SoC tends to be an excellent candidate for mobile devices and real-time applications.

In this paper, a novel performance analysis of the multi-access multiple-input multiple-output relay reference-modulated differential chaos shift keying cooperative diversity (RM-DCSK-CD) is presented. New closed-form expressions for the average bit error rates are derived for multiple relaying protocols. The carried analysis is based on a novel approximation of the bit error rates of RM-DCSK systems that directly applies for high- efficiency DCSK (HE-DCSK). The analysis assumes Additive White Generalized Gaussian Noise (AWGGN) model, which includes many other noise models as special cases, and Nakagami-m fading (and hence the Rayleigh fading). Numerical results prove the accuracy of our derived expressions.

This paper introduces a dramatically novel traffic signs recognition (TSR) system that can perform traffic sign detection and tracking simultaneously. The proposed approach utilizes intensity images and the depth images, in parallel, to robustly detect and track traffic signs in real-time. Additionally, we suggest to supplement the ordinary traffic signs with the corresponding quick-response (QR) code plates that inherent the many advantages of the QR-codes, introducing the concept of QR-TSR systems.

The paper presents a novel technique to segment and extract streets from satellite images. This technique utilizes, for the first time in the known literature, the Maximally Stable Extremal Regions (MSER) algorithm to robustly identify and segment streets from satellite images. The technique extracts dark MSERs and then classifies them based on multiple metrics such as the intensity of the pixels, the region stability, and the major-to-minor axes ratio. Testing results under multiple scenarios corroborate the accuracy of the proposed technique. The technique will allow fast and accurate implementation of a wide spectrum of applications such as in Global Positioning System (GPS) driving guidance.

Image segmentation is an essential preprocessing step for many computer vision and image processing applications. Implementing algorithms that handle such images in hardware will speed up the processing task considerably. In this paper, a new robust histogram-based image segmentation ASIC design of a System-on-Chip (SoC) using 65nm technology is presented. With a clock frequency of 289 MHz, the SoC can reach a frame rate of a 4410 FPS for an image resolution of 256×256. This is few order of magnitudes faster than the FPGA implementation in the literature. The finished-chip details renders it suitable for real-time and mobile applications.

This paper presents a new Zero-Crossing Digital Phase-Locked Loop (ZC-DPLL) for frequency synchronization and tracking. The proposed hyperbolic Σ-Δ based ZC-DPLL has an improved time jitter by a factor of 6.4 and an extended lock-in range by a factor of sinh(π)/π. These significant improvements were achieved by introducing a hyperbolic nonlinearity and a modified Σ-Δ blocks to the original ZC-DPLL architecture. These intentionally added system blocks adapt the loop filter and hence result in extending the loop locking range and improving its time jitter. The proposed hyperbolic ZC-DPLL performs well in wireless communication systems with high Doppler shift that may cause the system to lose lock. The extensive simulation results of the new architecture under various conditions show the robustness of the new architecture in terms of lock-in range, acquisition time, and jitter.