Action Recognition by Hierarchical Mid-level Action Elements

Realistic videos of human actions exhibit rich spatiotemporal structures at multiple levels of granularity: an action can always be decomposed into multiple finer-grained elements in both space and time. To capture this intuition, we propose to represent videos by a hierarchy of mid-level action elements (MAEs), where each MAE corresponds to an action-related spatiotemporal segment in the video. We introduce an unsupervised method to generate this representation from videos. Our method is capable of distinguishing action-related segments from background segments and representing actions at multiple spatiotemporal resolutions. Given a set of spatiotemporal segments generated from the training data, we introduce a discriminative clustering algorithm that automatically discovers MAEs at multiple levels of granularity. We develop structured models that capture a rich set of spatial, temporal and hierarchical relations among the segments, where the action label and multiple levels of MAE labels are jointly inferred. The proposed model achieves state-of-the-art performance in multiple action recognition benchmarks. Moreover, we demonstrate the effectiveness of our model in real-world applications such as action recognition in large-scale untrimmed videos and action parsing

Dynamic Repositioning For Bikesharing Systems

Bikesharing systems’ popularity has continuously been rising during the past years due to technological advancements. Managing and maintaining these emerging systems are indispensable parts of these systems and are necessary for their sustainable growth and successful implementation. One of the challenges that operators of these systems are facing is the uneven distribution of bikes due to users’ activities. These imbalances in the system can result in a lack of bikes or docks and consequently cause user dissatisfaction. A dynamic repositioning model that integrates prediction and routing is proposed to address this challenge. This operational model includes prediction, optimization, and simulation modules and can assist the operators of these systems in maintaining an effective system during peak periods with less number of unmet demands. It also can provide insights for planners by preparing development plans with the ultimate goal of more efficient systems. Developing a reliable prediction module that has the ability to predict future station-level demands can help system operators cope with the rebalancing needs more effectively. In this research, we utilize the expressive power of neural networks for predicting station-level demands (number of pick-ups and drop-offs) of bikeshare systems over multiple future time intervals. We examine the possibility of improving predictions by taking into account new sources of information about these systems, namely membership type and status of stations. A mathematical formulation is then developed for repositioning the bikes in the system with the goal of minimizing the number of unmet demands. The proposed module is a dynamic multi-period model with a rolling horizon which accounts for demands in the future time intervals. The performance of the optimization module and its assumptions are evaluated using discrete event simulation. Also, a three-step heuristic method is developed for solving large-size problems in a reasonable time. Finally, the integrated model is tested on several case studies from Capital Bikeshare, the District of Columbia’s bikeshare program

Subgroups of Groups of Units mod n

University of Minnesota M.S. thesis. June 2019. Major: Mathematics. Advisor: Joseph Gallian. 1 computer file (PDF); iii, 29 pages.The set of all positive integers less than n and relatively prime to n with multiplication mod n is a group denoted U(n). These groups are useful in algebra, number theory and computer science. We are interested in studying the structure of certain subgroups of U(n). As part of their 1980’s paper titled Factoring Groups of Integers Modulo n Gallian and Rusin determined the structure of U(n) and U_s (n) for n=st where gcd(s,t)=1 and U_s (n)={x∈U(n)┤|x (mod s)=1}. We extend this definition to U_k (n) where k is any positive integer and not necessarily a divisor of n. Moreover for a subgroup H of U(n) and an integer k we define: U_(k,H) (n)={x∈U(n)┤|x (mod k)∈H}. We find the structure of these subgroups and the factor group U(n)/U_k (n) in terms of an external direct product of cyclic groups. Our methods also determine group elements of U(n) that form a subgroup with a desired structure. We then shift our attention to the class of subgroups defined as: U(n)^((k))={x^k ┤| x∈U(n)}. We fully classify subgroups of this form and their factor groups. They are useful in finding Sylow p-subgroups of U(n) groups. We also prove some general results about U(n) groups including when the order of U(n) is a power of a prime. Finally we give a simple proof that every finite Abelian group is isomorphic to a subgroup of a U-group

Visual Geo-Localization and Location-Aware Image Understanding

Geo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images. In this dissertation, we address three questions central to geo-spatial analysis of ground-level imagery: 1) How to geo-localize images and videos captured at unknown locations? 2) How to refine the geo-location of already geo-tagged data? 3) How to utilize the extracted geo-tags? We present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing the consistency among their global features (e.g., GIST) using GMCP. Our evaluations using a new data set of 102k Street View images shows the proposed method outperforms the state-of-the-art by 10 percent. Geo-localization of images can be extended to geo-localization of a video. We have developed a novel method for estimating the geo-spatial trajectory of a moving camera with unknown intrinsic parameters in a city-scale. The proposed method is based on a three step process: 1) individual geo-localization of video frames using Street View images to obtain the likelihood of the location (latitude and longitude) given the current observation, 2) Bayesian tracking to estimate the frame location and video\u27s temporal evolution using previous state probabilities and current likelihood, and 3) applying a novel Minimum Spanning Trees based trajectory reconstruction to eliminate trajectory loops or noisy estimations. Thus far, we have assumed reliable geo-tags for reference imagery are available through crowdsourcing. However, crowdsourced images are well known to suffer from the acute shortcoming of having inaccurate geo-tags. We have developed the first method for refinement of GPS-tags which automatically discovers the subset of corrupted geo-tags and refines them. We employ Random Walks to discover the uncontaminated subset of location estimations and robustify Random Walks with a novel adaptive damping factor that conforms to the level of noise in the input. In location-aware image understanding, we are interested in improving the image analysis by putting it in the right geo-spatial context. This approach is of particular importance as the majority of cameras and mobile devices are now being equipped with GPS chips. Therefore, developing techniques which can leverage the geo-tags of images for improving the performance of traditional computer vision tasks is of particular interest. We have developed a location-aware multimodal approach which incorporates business directories, textual information, and web images to identify businesses in a geo-tagged query image

LOCATING CHARGING STATIONS FOR ELECTRIC VEHICLES IN RURAL AND URBAN NETWORKS

Using new alternative fuels for motorized transportation vehicles has become increasingly popular with the growing concerns on the limitation of fossil fuels and environmental degradation. Introduction of numerous models of electric vehicles in the 21 century raised hope for replacing conventional internal combustion engine vehicles with these vehicles; however several barriers has adversely impacted the widespread adoption of these vehicles. Providing adequate number of charging stations and planning the layout of their infrastructure will help overcome some of the existing challenges. In this thesis, two formulations are presented for the optimal layout of these stations in rural and urban networks and the models are applied on two networks. For the rural model, the results indicate the solution is highly sensitive to the assumptions about the range of vehicles for which we are designing the layout. In the urban context, the decision about number and location of chargers is highly dependent on the probability threshold we choose for satisfying the demand

High Speed Reconfigurable NRZ/PAM4 Transceiver Design Techniques

While the majority of wireline standards use simple binary non-return-to-zero (NRZ) signaling, four-level pulse-amplitude modulation (PAM4) standards are emerging to increase bandwidth density. This dissertation proposes efficient implementations for high speed NRZ/PAM4 transceivers. The first prototype includes a dual-mode NRZ/PAM4 serial I/O transmitter which can support both modulations with minimum power and hardware overhead. A source-series-terminated (SST) transmitter achieves 1.2Vpp output swing and employs lookup table (LUT) control of a 31-segment output digital-to-analog converter (DAC) to implement 4/2-tap feed-forward equalization (FFE) in NRZ/PAM4 modes, respectively. Transmitter power is improved with low-overhead analog impedance control in the DAC cells and a quarter-rate serializer based on a tri-state inverter-based mux with dynamic pre-driver gates. The transmitter is designed to work with a receiver that implements an NRZ/PAM4 decision feedback equalizer (DFE) that employs 1 finite impulse response (FIR) and 2 infinite impulse response (IIR) taps for first post-cursor and long-tail ISI cancellation, respectively. Fabricated in GP 65-nm CMOS, the transmitter occupies 0.060mm² area and achieves 16Gb/s NRZ and 32Gb/s PAM4 operation at 10.4 and 4.9 mW/Gb/s while operating over channels with 27.6 and 13.5dB loss at Nyquist, respectively. The second prototype presents a 56Gb/s four-level pulse amplitude modulation (PAM4) quarter-rate wireline receiver which is implemented in a 65nm CMOS process. The frontend utilize a single stage continuous time linear equalizer (CTLE) to boost the main cursor and relax the pre-cursor cancelation requirement, requiring only a 2-tap pre-cursor feed-forward equalization (FFE) on the transmitter side. A 2-tap decision feedback equalizer (DFE) with one finite impulse response (FIR) tap and one infinite impulse response (IIR) tap is employed to cancel first post-cursor and longtail inter-symbol interference (ISI). The FIR tap direct feedback is implemented inside the CML slicers to relax the critical timing of DFE and maximize the achievable data-rate. In addition to the per-slice main 3 data samplers, an error sampler is utilized for background threshold control and an edge-based sampler performs both PLL-based CDR phase detection and generates information for background DFE tap adaptation. The receiver consumes 4.63mW/Gb/s and compensates for up to 20.8dB loss when operated with a 2- tap FFE transmitter. The experimental results and comparison with state-of-the-art shows superior power efficiency of the presented prototypes for similar data-rate and channel loss. The usage of proposed design techniques are not limited to these specific prototypes and can be applied for any wireline transceiver with different modulation, data-rate and CMOS technology