Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps of Multi-Channel sEMG Signals

Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.Comment: Paper accepted to IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 202

Distribution, Natural History and the Conservation Status of Hemiphyllodactylus typus and Lepidodactylus lugubris (Reptilia: Gekkonidae) in Sri Lanka

Sri Lanka has a rich assemblage of gekkonid fauna. Among Sri Lankan geckos, rare species such as Hemiphyllodactylus typus and Lepidodactylus lugubris are poorly studied; both are considered vulnerable in national conservation assessments. Detailed ecological studies are needed for robust conservation assessments of these species, especially with the focus on island-wide distribution and microhabitat requirements. This study was conducted via patch sampling to record relative abundance and distribution of H. typus and L. lugubris based on random walks to 82 locations representing the three major bioclimatic zones of Sri Lanka. Morphological characteristics, behavior, and habitat use of the focal species were recorded. A total of 17 and 14 individuals of H. typus and L. lugubris were found, respectively, which indicated the low abundance of both species. Both species were nocturnal, arboreal, and did not den with conspecifics; they mostly preferred close-canopy, dense woody vegetation having mature moss-covered tree trunks with peeling barks and crevices over built-up environments. No records on oviposition were noted for either species. Both species were sluggish in their movements, even when disturbed. Currently known populations of both species occur in severely fragmented unprotected small forest patches. Therefore, habitat loss and fragmentation threaten these populations unless protected areas of Sri Lanka are expanded and functional connectivity is established

Seeded Self-Modulation of Elliptical Beams in Plasma Wakefields

This thesis is concerned with investigating the effects of ellipticity on the seeded self-modulation process of long particle beams in plasma accelerators, and the implications for accelerated beams, using theoretical and computational models. Proton-driven plasma wakefield acceleration is a novel approach to plasma-based particle acceleration pioneered by the AWAKE experiment at CERN. The use of high-energy-content proton beams allows sustained high gradient (gigavolts per meter) plasma wakefields, allowing in principle, acceleration of witness beams over 10s or even 100s of meters. The scheme used at AWAKE relies on the seeded self-modulation (SSM) of long particle bunches, where the stability of the microbunch trains produced by SSM over tens or hundreds of meters is crucial for extrapolating this scheme as proposed for use in several high energy plasma-based linear colliders. Further, the uniformity and reproducibility of the resultant wakefields is essential for determining injection parameters and preservation of phase-space quality for witness beams during acceleration. Transverse asymmetry is often characteristic of synchrotron-generated beams. However, aside from the competing hosing instability, few works have examined other effects of transverse asymmetry in this process. In this thesis, analytical modelling and 3D particle-in-cell (PIC) simulations are used to characterise the impact on the SSM growth process and resultant wakefields due to elliptical transverse asymmetry in the beam. Metrics are constructed for quantifying the asymmetry of the evolving transverse beam profile in PIC simulations. Using these it is found that while beam asymmetry undergoes an order-of-magnitude increase during saturation of the SSM, the initial azimuthal complexity remains low and increases only slightly during the SSM growth stage. This allows the construction of a new analytical model for asymmetric SSM growth, from which a scaling for the reduction of the SSM growth rate with aspect ratio of the initial beam profile is obtained. A new method for estimating the SSM growth rate from simulations is developed, which allows these to be quantitatively verified. Finally, using heuristic knowledge of witness beam behaviour, the impact of azimuthal asymmetry of the longitudinal component of the wakefield on the final energy spread of an accelerated witness beam is estimated and found to be at most a few percent of the energy spread due to variation in a symmetric wakefield

Techniques for large scale deployment of demand-aware bus transit systems

Public transit systems have been constantly plagued by the inherent connectivity gap due to fixed routes and schedules of feeder bus services. Demand-aware bus transit systems that rely on real-time scheduling of flexible routes have gained popularity as an alternative to bridge the connectivity gap, thereby enhancing user experience and operator profitability. In this research, scalable techniques have been proposed to realize citywide deployment of a demand-aware bus transit system to replace the conventional fixed-route based feeder bus services. In Chapter 3, a graph-based representation has been proposed to model demand-aware flexible route generation. The mixed integer programming model for generating the optimal flexible routes incorporates real-life scenarios including actual distances of the road network and asymmetric distance/time matrices that represent the different `to and fro' distance/travelling times between two given points. The proposed model can successfully generate optimal flexible routes to enhance the travel times of passengers (user experience) or vehicle miles travelled by the fleet (operator profitability). In particular, the normalized weighted technique has been introduced to facilitate trade-off analysis based on user requirements to ensure that the flexible routes are sensitive to both the user experience and operator profitability. The proposed model has been successfully employed to prune the design space to speed up route computations without compromising optimality. Experimental results demonstrate the capability of the model in performing diverse what-if analyses by varying different input parameters. A heuristic routing technique has been proposed in Chapter 4 to accelerate the flexible route generation process by combining both the Ꜫ-constraint method and a genetic algorithm. The technique incorporates nearest neighbour heuristic to generate superior initial solutions, selection of genetic operators for fast convergence, and a hybrid parent selection algorithm for balancing solution quality and diversity. Experimental results confirm that routes generated by the proposed technique deviate only 3% from the optimal values. The rapid convergence of the proposed technique results in a 26% reduction in runtime when compared to a widely-used baseline algorithm. A directionality-centric technique has been proposed for the systematic segmentation of bus transit network in Chapter 5. The network segmentation technique generates sub-zones based on the feasible shortest path routes from its bus stops to the destination. Heuristic technique proposed in Chapter 4 has been employed to generate the flexible routes of the size limited sub-zones. This has led to notable speed-up of flexible route computations that can also benefit from parallel computations, thereby paving the way for a highly scalable technique without compromising the responsiveness demanded by demand-aware bus transit systems. The outlier bus stops of sub-zones are incorporated into the neighbouring sub-zones on-the-fly to minimize vehicle detours. Moreover, dynamic methods for demand-aware allocation of EVs and workload balancing among sub-zones improves the overall responsiveness of a large-scale deployment. Experimental results confirm that the routes generated using the proposed technique achieves over 7x speed-up when compared to a global, heuristic routing technique without compromising on solution quality. A similar performance improvement was also evident for the case of sporadic demands, highlighting the applicability of the proposed network segmentation technique to real-life scenarios. In Chapter 6, applicability of the proposed methods to a large scale deployment of demand-aware bus transit system has been demonstrated. This necessitated the systematic segmentation of feeder bus services into sub-zones and outlier bus stops as well as point-to-point trunk services. Identification of independent transit hub regions of a large scale transit system as well as segmenting each transit hub into workload balanced zones has notably improved the responsiveness of route computations. Experimental results confirm that, compared to a widely-used unsupervised learning algorithm, the zone-wise runtime has improved by 83% while also improving the quality of routes. A demand-aware scheduling technique to improve the user experience and operator profitability of trunk bus services has also been proposed in this chapter. In Chapter 7, the various techniques proposed in this thesis have been integrated into a framework to realize the citywide deployment of demand-aware flexible routing. The model for demand prediction was trained using multi-modal sensing inputs from mobile apps and vision-based crowd counting. This has paved the way for estimating near-future demand at each bus stop to schedule flexible routes in real-time. Runtime performance of citywide route generation process has also been improved by the offline processing of a significant component of the workload and by limiting the generation of sub-zones, based on near-future demand estimation, to online. Experiments confirm that the flexible routes can be computed in 20 and 24 seconds for workloads of 50 and 65 passengers respectively, when implemented on a 4-core Xeon E5-1630 V4 CPU running at 3.70 GHz. The proposed sensing methods also lend well for the periodic generation of offline schedules for trunk bus services. Finally, real-life deployment of the proposed techniques for the city-wide replacement of fixed-route based bus transit systems has been successfully demonstrated to minimise the connectivity gap inherent in current implementations.Doctor of Philosoph

Genetic algorithm based dynamic scheduling of EV in a demand responsive bus service for first mile transit

Demand responsive transit (DRT) services have significantly evolved in the past few years owing to developments in information and communication technologies. Among the many forms of DRT services, demand responsive bus (DRB) services are gaining traction as a complimentary mode to existing public transit services, especially to dynamically bridge the first/last mile connectivity. Simultaneously, the stern regulations imposed by regulators on greenhouse gas emission have enforced electric vehicles (EV) to replace conventional vehicles. However, state-of-the-art (SoA) work proposed to generate routes for EV-based DRB services are inhibited by the low number of ride matches and the excessively high computation time of the algorithms deeming them unsuitable for real-time computations. To this end, we propose a genetic algorithm for dynamic scheduling of EV in a DRB service that reacts to first mile ride requests of passengers. In addition, we also formulate an optimal mixed integer program to generate baseline results. Experiments on an actual road network show that the proposed GA generates significantly accurate results compared to the baseline in real-time. Further, we analyze the benefits of rescheduling passengers and flexible estimated time of arrival of EV to optimize the total travel time of passengers

A hybrid methodology for optimal fleet management in an electric vehicle based flexible bus service

The ever-increasing traffic congestion and CO-{2} emission caused by rapid urbanization, calls for smarter and energy efficient transit services. Conventional public transit lacks the ability to meet these diversified needs. As a result, intelligent transit systems, influenced by the digital revolution have created a profound impact by enhancing the user-experience of transit services. Consequently, demand responsive transit (DRT) services, which operate with flexible routes and schedules have become a common option among commuters. Thus, in this work, we propose an electric vehicle (EV) based flexible bus service, a variant of DRT, that satisfies passenger demand in a given geographical zone. Next, we present a hybrid methodology to optimally manage the EV fleet minimizing the total vehicle miles travelled (VMT). Experimental results with a real map show that the proposed hybrid method achieves near-optimal results with 120x improvement in computation time. Further, the flexible bus service reduces VMT by over 70% in comparison to single occupancy vehicles, thus reducing both traffic congestion and CO-{2} emissions

Genetic algorithm based EV scheduling for on-demand public transit system

The popularity of real-time on-demand transit as a fast evolving mobility service has paved the way to explore novel solutions for point-to-point transit requests. In addition, strict government regulations on greenhouse gas emission calls for energy efficient transit solutions. To this end, we propose an on-demand public transit system using a fleet of heterogeneous electric vehicles, which provides real-time service to passengers by linking a zone to a predetermined rapid transit node. Subsequently, we model the problem using a Genetic Algorithm, which generates routes and schedules in real-time while minimizing passenger travel time. Experiments performed using a real map show that the proposed algorithm not only generates near-optimal results but also advances the state-of-the-art at a marginal cost of computation time.National Research Foundation (NRF)This research project is partially funded by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme with the Technical University of Munich at TUMCREATE

A simulation framework for a real-time demand responsive public transit system

Transit systems have encountered a radical change in the recent past as a result of the digital disruption. Consequently, traditional public transit systems no longer satisfy the diversified demands of passengers and hence, have been complemented by demand responsive transit solutions. However, we identify a lack of simulation tools developed to test and validate complex scenarios for real-time demand responsive public transit. Thus, in this paper, we propose a simulation framework, which combines complex scenario creation, optimization algorithm execution and result visualization using SUMO, an open source continuous simulator. In comparison to a state-of-the-art work, the proposed tool supports features such as varying vehicle capacity and driving range, immediate and advance passenger requests and maximum travel time constraints. Further, the framework follows a modular architecture that allows plug-and-play support for external modules