Three levels of metric for evaluating wayfinding

Three levels of virtual environment (VE) metric are proposed, based on: (1) users’ task performance (time taken, distance traveled and number of errors made), (2) physical behavior (locomotion, looking around, and time and error classification), and (3) decision making (i.e., cognitive) rationale (think aloud, interview and questionnaire). Examples of the use of these metrics are drawn from a detailed review of research into VE wayfinding. A case study from research into the fidelity that is required for efficient VE wayfinding is presented, showing the unsuitability in some circumstances of common metrics of task performance such as time and distance, and the benefits to be gained by making fine-grained analyses of users’ behavior. Taken as a whole, the article highlights the range of techniques that have been successfully used to evaluate wayfinding and explains in detail how some of these techniques may be applied

Decolonizing the Barrio: The Spatial Politics of Culture in Chicago’s Paseo Boricua

In the current neoliberal climate that has stoked an intense intercity competition for internationally footloose capital, many cities with global aspirations are encouraging urban redevelopment projects that brand and promote heterogeneous cultural enclaves as destinations for leisure and tourism consumption. Oftentimes, such enclaves emerge as ethnoscapes that ostensibly express the cultural identity of its residents, usually immigrant populations. In Chicago, municipal policies aimed at enhancing a visitor economy have been instrumental in the creation of the Paseo Boricua, a Puerto Rican ethnoscape . This study examines the intersection of Chicago\u27s urban redevelopment policies and the spatial politics of culture that unfold in the streets of the Humboldt Park neighborhood. Here, Puerto Rican nationalists have pursued an agenda to decolonize their spaces by subverting the intended consequences of the city\u27s ethnoscaping process in acts of contestation, resistance, and transgression

Energy distribution of the Einstein-Klein-Gordon system for a static spherically symmetric spacetime in (2+1)-dimensions

We use Moeller's energy-momentum complex in order to explicitly compute the energy and momentum density distributions for an exact solution of Einstein's field equations with a negative cosmological constant minimally coupled to a static massless scalar field in a static, spherically symmetric background in (2+1)-dimensions.Comment: 9 pages, 1 figur

Locally Homogeneous Spaces, Induced Killing Vector Fields and Applications to Bianchi Prototypes

An answer to the question: Can, in general, the adoption of a given symmetry induce a further symmetry, which might be hidden at a first level? has been attempted in the context of differential geometry of locally homogeneous spaces. Based on E. Cartan's theory of moving frames, a methodology for finding all symmetries for any n dimensional locally homogeneous space is provided. The analysis is applied to 3 dimensional spaces, whereby the embedding of them into a 4 dimensional Lorentzian manifold is examined and special solutions to Einstein's field equations are recovered. The analysis is mainly of local character, since the interest is focused on local structures based on differential equations (and their symmetries), rather than on the implications of, e.g., the analytic continuation of their solution(s) and their dynamics in the large.Comment: 27 pages, no figues, no tables, one reference added, spelling and punctuation issues correcte

Reduced Complexity Maximum Likelihood Detector for DFT-s-SEFDM Systems

In this paper, we report on the design of a Complexity-Reduced Maximum Likelihood (CRML) detector for DFT-spread Spectrally Efficient Frequency Division Multiplexing (DFT-s-SEFDM) systems. DFT-s-SEFDM systems are similar to DFT-spread Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) systems, yet offer improved spectral efficiency. Simulation results demonstrate that the CRML detector can achieve the same bit error rate (BER) performance as the ML detector in DFT-s-SEFDM systems at reduced computational complexity. Specifically, compared to a conventional ML detector, it is shown that CRML can decrease the search region by up to 2^{M} times where M denotes the constellation cardinality. Depending on parameter configuration, CRML can offer up to two orders of magnitude improvement in execution runtime performance. CRML is best-suited to applications with small system sizes, for example, in narrowband Internet of Things (NB-IoT) networks

FPGA design of low complexity SEFDM detection techniques

This paper presents for the first time the hardware design of low complexity detection algorithms for the recovery of Spectrally Efficient Frequency Division Multiplexing (SEFDM) signals. The work shows that a practical design is feasible using Field Programmable Gate Arrays (FPGAs). Two detection techniques can be implemented using the proposed system architecture, namely Zero Forcing (ZF) and Truncated Singular Value Decomposition (TSVD), demonstrating that our hardware design is flexible. TSVD offers a significant reduction in complexity compared to optimal detection techniques, such as Maximum Likelihood (ML) while outperforming ZF, in terms of Bit Error Rate (BER). Results show excellent fixed-point performance and are comparable to existing floating-point computer-based simulations

Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis

The paradigm of automated waste classification has recently seen a shift in the domain of interest from conventional image processing techniques to powerful computer vision algorithms known as convolutional neural networks (CNN). Historically, CNNs have demonstrated a strong dependency on powerful hardware for real-time classification, yet the need for deployment on weaker embedded devices is greater than ever. The work in this paper proposes a methodology for reconstructing and tuning conventional image classification models, using EfficientNets, to decrease their parameterisation with no trade-off in model accuracy and develops a pipeline through TensorRT for accelerating such models to run at real-time on an NVIDIA Jetson Nano embedded device. The train-deployment discrepancy, relating how poor data augmentation leads to a discrepancy in model accuracy between training and deployment, is often neglected in many papers and thus the work is extended by analysing and evaluating the impact real world perturbations had on model accuracy once deployed. The scope of the work concerns developing a more efficient variant of WasteNet, a collaborative recycling classification model. The newly developed model scores a test-set accuracy of 95.8% with a real world accuracy of 95%, a 14% increase over the original. Our acceleration pipeline boosted model throughput by 750% to 24 inferences per second on the Jetson Nano and real-time latency of the system was verified through servomotor latency analysis