Matching Items (157)
Description
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
ContributorsBraun, Henry (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2012
Description
With increased usage of green energy, the number of photovoltaic arrays used in power generation is increasing rapidly. Many of the arrays are located at remote locations where faults that occur within the array often go unnoticed and unattended for large periods of time. Technicians sent to rectify the faults have to spend a large amount of time determining the location of the fault manually. Automated monitoring systems are needed to obtain the information about the performance of the array and detect faults. Such systems must monitor the DC side of the array in addition to the AC side to identify non catastrophic faults. This thesis focuses on two of the requirements for DC side monitoring of an automated PV array monitoring system. The first part of the thesis quantifies the advantages of obtaining higher resolution data from a PV array on detection of faults. Data for the monitoring system can be gathered for the array as a whole or from additional places within the array such as individual modules and end of strings. The fault detection rate and the false positive rates are compared for array level, string level and module level PV data. Monte Carlo simulations are performed using PV array models developed in Simulink and MATLAB for fault and no fault cases. The second part describes a graphical user interface (GUI) that can be used to visualize the PV array for module level monitoring system information. A demonstration GUI is built in MATLAB using data obtained from a PV array test facility in Tempe, AZ. Visualizations are implemented to display information about the array as a whole or individual modules and locate faults in the array.
ContributorsKrishnan, Venkatachalam (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Ayyanar, Raja (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, we consider the problem of fast and efficient indexing techniques for time sequences which evolve on manifold-valued spaces. Using manifolds is a convenient way to work with complex features that often do not live in Euclidean spaces. However, computing standard notions of geodesic distance, mean etc. can get very involved due to the underlying non-linearity associated with the space. As a result a complex task such as manifold sequence matching would require very large number of computations making it hard to use in practice. We believe that one can device smart approximation algorithms for several classes of such problems which take into account the geometry of the manifold and maintain the favorable properties of the exact approach. This problem has several applications in areas of human activity discovery and recognition, where several features and representations are naturally studied in a non-Euclidean setting. We propose a novel solution to the problem of indexing manifold-valued sequences by proposing an intrinsic approach to map sequences to a symbolic representation. This is shown to enable the deployment of fast and accurate algorithms for activity recognition, motif discovery, and anomaly detection. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. Experiments show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The proposed methods are ideally suited for real-time systems and resource constrained scenarios.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Recent advances in camera architectures and associated mathematical representations now enable compressive acquisition of images and videos at low data-rates. While most computer vision applications of today are composed of conventional cameras, which collect a large amount redundant data and power hungry embedded systems, which compress the collected data for further processing, compressive cameras offer the advantage of direct acquisition of data in compressed domain and hence readily promise to find applicability in computer vision, particularly in environments hampered by limited communication bandwidths. However, despite the significant progress in theory and methods of compressive sensing, little headway has been made in developing systems for such applications by exploiting the merits of compressive sensing. In such a setting, we consider the problem of activity recognition, which is an important inference problem in many security and surveillance applications. Since all successful activity recognition systems involve detection of human, followed by recognition, a potential fully functioning system motivated by compressive camera would involve the tracking of human, which requires the reconstruction of atleast the initial few frames to detect the human. Once the human is tracked, the recognition part of the system requires only the features to be extracted from the tracked sequences, which can be the reconstructed images or the compressed measurements of such sequences. However, it is desirable in resource constrained environments that these features be extracted from the compressive measurements without reconstruction. Motivated by this, in this thesis, we propose a framework for understanding activities as a non-linear dynamical system, and propose a robust, generalizable feature that can be extracted directly from the compressed measurements without reconstructing the original video frames. The proposed feature is termed recurrence texture and is motivated from recurrence analysis of non-linear dynamical systems. We show that it is possible to obtain discriminative features directly from the compressed stream and show its utility in recognition of activities at very low data rates.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis proposes the concept of soft robotic supernumerary limbs to assist the wearer in the execution of tasks, whether it be to share loads or replace an assistant. These controllable extra arms are made using soft robotics to reduce the weight and cost of the device, and are not limited in size and location to the user's arm as with exoskeletal devices. Soft robotics differ from traditional robotics in that they are made using soft materials such as silicone elastomers rather than hard materials such as metals or plastics. This thesis presents the design, fabrication, and testing of the arm, including the joints and the actuators to move them, as well as the design and fabrication of the human-body interface to unite man and machine. This prototype utilizes two types of pneumatically-driven actuators, pneumatic artificial muscles and fiber-reinforced actuators, to actuate the elbow and shoulder joints, respectively. The robotic limb is mounted at the waist on a backpack frame to avoid interfering with the wearer's biological arm. Through testing and evaluation, this prototype device proves the feasibility of soft supernumerary limbs, and opens up opportunities for further development into the field.
ContributorsOlson, Weston Roscoe (Author) / Polygerinos, Panagiotis (Thesis director) / Zhang, Wenlong (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The researchers build a drone with a grasping mechanism to wrap around branches to perch. The design process and methodology are discussed along with the software and hardware configuration. The researchers explain the influences on the design and the possibilities for what it could inspire.
ContributorsDowney, Matthew Evan (Co-author) / Macias, Jose (Co-author) / Goldenberg, Edward (Co-author) / Zhang, Wenlong (Thesis director) / Aukes, Daniel (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This thesis presents a gas sensor readout IC for amperometric and conductometric electrochemical sensors. The Analog Front-End (AFE) readout circuit enables tracking long term exposure to hazardous gas fumes in diesel and gasoline equipments, which may be correlated to diseases. Thus, the detection and discrimination of gases using microelectronic gas sensor system is required. This thesis describes the research, development, implementation and test of a small and portable based prototype platform for chemical gas sensors to enable a low-power and low noise gas detection system. The AFE reads out the outputs of eight conductometric sensor array and eight amperometric sensor arrays. The IC consists of a low noise potentiostat, and associated 9bit current-steering DAC for sensor stimulus, followed by the first order nested chopped £U£G ADC. The conductometric sensor uses a current driven approach for extracting conductance of the sensor depending on gas concentration. The amperometric sensor uses a potentiostat to apply constant voltage to the sensors and an I/V converter to measure current out of the sensor. The core area for the AFE is 2.65x0.95 mm2. The proposed system achieves 91 dB SNR at 1.32 mW quiescent power consumption per channel. With digital offset storage and nested chopping, the readout chain achieves 500 fÝV input referred offset.
ContributorsKim, Hyun-Tae (Author) / Bakkaloglu, Bertan (Thesis advisor) / Vermeire, Bert (Committee member) / Spanias, Andreas (Committee member) / Thornton, Trevor (Committee member) / Arizona State University (Publisher)
Created2011
Description
Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels are considered. The performance of these algorithms and the effects of wireless communication channels on the performance are studied. In a first class of problems, distributed inference over fading Gaussian multiple-access channels with amplify-and-forward is considered. Sensors observe a phenomenon and transmit their observations using the amplify-and-forward scheme to a fusion center (FC). Distributed estimation is considered with a single antenna at the FC, where the performance is evaluated using the asymptotic variance of the estimator. The loss in performance due to varying assumptions on the limited amounts of channel information at the sensors is quantified. With multiple antennas at the FC, a distributed detection problem is also considered, where the error exponent is used to evaluate performance. It is shown that for zero-mean channels between the sensors and the FC when there is no channel information at the sensors, arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the FC. In stark contrast, when there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the FC is shown to be no more than a factor of 8/π for Rayleigh fading channels between the sensors and the FC, independent of the number of antennas at the FC, or correlation among noise samples across sensors. In a second class of problems, sensor observations are transmitted to the FC using constant-modulus phase modulation over Gaussian multiple-access-channels. The phase modulation scheme allows for constant transmit power and estimation of moments other than the mean with a single transmission from the sensors. Estimators are developed for the mean, variance and signal-to-noise ratio (SNR) of the sensor observations. The performance of these estimators is studied for different distributions of the observations. It is proved that the estimator of the mean is asymptotically efficient if and only if the distribution of the sensor observations is Gaussian.
ContributorsBanavar, Mahesh Krishna (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Duman, Tolga (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2010
Description
This paper describes the attempt of designing and building a two wheeled platform that is inherently unstable and discovering what tail design is suitable for stabilizing the platform. The platform is a 3D printed box that carries an Arduino, breadboard, MPU6050, a battery and a servo. This box is connected to two continuous servo motors (one on each side) that are attached to wheels, the breadboard and Arduino are mounted on the inside and the MPU6050 is mounted on the back of the base. The MPU6050 collects the data. In the program, that data will be the position of the accelerometer’s x-axis and that data will be sent to the servo motor with the tail for the controls aspect.
ContributorsOnonye, Frank Nwachukwu (Author) / Aukes, Daniel (Thesis director) / Zhang, Wenlong (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In the last decade, a large variety of algorithms have been developed for use in object tracking, environment mapping, and object classification. It is often difficult for beginners to fully predict the constraints that multirotors place on machine vision algorithms. The purpose of this paper is to explain some of the types of algorithms that can be applied to these aerial systems, why the constraints for these algorithms exist, and what could be done to mitigate them. This paper provides a summary of the processes involved in a popular filter-based tracking algorithm called MOSSE (Minimum Output Sum of Squared Error) and a particular implementation of SLAM (Simultaneous Localization and Mapping) called LSD SLAM.
ContributorsVan Hazel, Colton (Author) / Zhang, Wenlong (Thesis director) / Yang, Yezhou (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05