4,845 research outputs found
Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification
The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Simulating substrate binding sites in the S. aureus Type II NADH Dehydrogenase
"Type II NADH Oxidoreductase (NDH-2) from Staphylococcus aureus was established as a therapeutic target against the virulency of this bacterium and an alternative to treat Complex I-derived diseases. To accurately model interactions of NDH-2 with its substrates such as menaquinones and NADH, Coarse-Grain (CG) simulations were employed. "N/
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
Implementasi Algoritma Genetika untuk Aplikasi Penjadwalan Sistem Kerja Shift
Perusahaan yang beroperasi jam kerja 24 jam mempunyai tugas berat dalam menentukan jadwal shift kerja. Selain memberikan layanan terbaik untuk pelanggan setiap waktu, dan memiliki segmen pasar khusus. Untuk melakukan produktivitas optimal diperlukan jadwal shift kerja yang teratur bagi perusahaan. Tujuan penelitian ini adalah memecahkan permasalahan pengelolaan jadwal kerja menggunakan algoritma genetika disalah satu perusahaan konektivitas penyedia jaringan internet yang secara manual membuat jadwal dengan bantuan microsoft excel dengan pertimbangan bahwa setiap karyawan memiliki shift kerja berbeda untuk setiap bulannya. Aplikasi ini menggunakan Unified Modeling Language sebagai model dari sistem penjadwalan shift kerja yang bertujuan untuk pengembangan sistem bisa dilakukan secara maksimal. Hasil dari implementasi perhitungan algoritma genetika menggunakan variabel waktu shift, variabel hari, variabel nama karyawan tidak ditemukan permasalahan penjadwalan shift kerja begitu juga setelah dirancang menggunakan aplikasi penjadwalan shift kerja muncul penjadwalan sistem secara otomatis dengan acuan permasalahan yang sudah bernilai value
Modelo de optimización para la selección de proyectos en ciberseguridad y uso de recursos en instituciones públicas del Ecuador, 2022.
Los problemas de seguridad de la información en las organizaciones públicas son
persistentes; una de las causas es la escasez de modelos y métodos adecuados
para medir la eficiencia de los procesos relacionados con la seguridad informática
y la rentabilidad económica de las inversiones en TI. El objetivo de este trabajo es
la eficiencia de la gestión de selección de proyectos estratégicos para garantizar
mejorar la seguridad de la información de una organización pública. Dado un
conjunto de proyectos estratégicos para mejorar la seguridad de la información de
una organización pública, el modelo propuesto determina un subconjunto de
proyectos a ejecutar en un período de tiempo, para el uso eficiente de los recursos
limitados de la organización. Se aplicó una metodología de investigación de
enfoque cuantitativa, de tipo aplicada, con diseño de investigación observacional
correlacional transversal. Como resultado se obtuvo un modelo matemático que
optimiza dos objetivos: maximizar el porcentaje de mejora en seguridad de la
información de los proyectos planificados y minimizar los costos de la organización;
la implementación en lenguaje Python del algoritmo genético de clasificación No
dominada NSGA-II, que brinda a través del frente de Pareto las mejores soluciones
que pueden ser consideradas por los Directores de TI. Se concluyó que el modelo
de optimización presentado es eficiente, la selección de un subconjunto de
proyectos estratégicos permite mejorar la seguridad de la información de una
organización pública, en un rango de 85.30% a 89.00%, considerando las
limitaciones presupuestarias de la organización, tales como mostrado por las
métricas de la simulación realizada. El modelo propuesto es bastante simple de
implementar, muy práctico y puede ser un instrumento adecuado para elegir la
solución más eficiente, considerando los objetivos y limitaciones de una
organización pública
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Bio-inspired Optimization: Algorithm, Analysis and Scope of Application
In the last few years, bio-inspired optimization techniques have been widely adopted in fields such as computer science, mathematics, and biology in order to optimize solutions. Bio inspired optimization problems are usually nonlinear and restricted to multiple nonlinear constraints to tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This work comprises state-of-art of ten recent bio-inspired algorithms, gap analysis, and its applications namely; Particle swarm optimization (PSO), Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), Cuckoo Search Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions on the essence of these algorithms and their connections to self-organization and their applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future
Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in MEC Systems Interconnected by Metro Optical Networks
The increasing demand for diverse emerging applications has resulted in the
interconnection of multi-access edge computing (MEC) systems via metro optical
networks. To cater to these diverse applications, network slicing has become a
popular tool for creating specialized virtual networks. However, resource
fragmentation caused by uneven utilization of multidimensional resources can
lead to reduced utilization of limited edge resources. To tackle this issue,
this paper focuses on addressing the multidimensional resource fragmentation
problem in virtual network embedding (VNE) in MEC systems with the aim of
maximizing the profit of an infrastructure provider (InP). The VNE problem in
MEC systems is transformed into a bilevel optimization problem, taking into
account the interdependence between virtual node embedding (VNoE) and virtual
link embedding (VLiE). To solve this problem, we propose a nested bilevel
optimization approach named BiVNE. The VNoE is solved using the ant colony
system (ACS) in the upper level, while the VLiE is solved using a combination
of a shortest path algorithm and an exact-fit spectrum slot allocation method
in the lower level. Evaluation results show that the BiVNE algorithm can
effectively enhance the profit of the InP by increasing the acceptance ratio
and avoiding resource fragmentation simultaneously
Resource Management in Mobile Edge Computing for Compute-intensive Application
With current and future mobile applications (e.g., healthcare, connected vehicles, and smart grids) becoming increasingly compute-intensive for many mission-critical use cases, the energy and computing capacities of embedded mobile devices are proving to be insufficient to handle all in-device computation. To address the energy and computing shortages of mobile devices, mobile edge computing (MEC) has emerged as a major distributed computing paradigm. Compared to traditional cloud-based computing, MEC integrates network control, distributed computing, and storage to customizable, fast, reliable, and secure edge services that are closer to the user and data sites. However, the diversity of applications and a variety of user specified requirements (viz., latency, scalability, availability, and reliability) add additional complications to the system and application optimization problems in terms of resource management. In this thesis dissertation, we aim to develop customized and intelligent placement and provisioning strategies that are needed to handle edge resource management problems for different challenging use cases: i) Firstly, we propose an energy-efficient framework to address the resource allocation problem of generic compute-intensive applications, such as Directed Acyclic Graph (DAG) based applications. We design partial task offloading and server selection strategies with the purpose of minimizing the transmission cost. Our experiment and simulation results indicate that partial task offloading provides considerable energy savings, especially for resource-constrained edge systems. ii) Secondly, to address the dynamism edge environments, we propose solutions that integrate Dynamic Spectrum Access (DSA) and Cooperative Spectrum Sensing (CSS) with fine-grained task offloading schemes. Similarly, we show the high efficiency of the proposed strategy in capturing dynamic channel states and enforcing intelligent channel sensing and task offloading decisions. iii) Finally, application-specific long-term optimization frameworks are proposed for two representative applications: a) multi-view 3D reconstruction and b) Deep Neural Network (DNN) inference. Here, in order to eliminate redundant and unnecessary reconstruction processing, we introduce key-frame and resolution selection incorporated with task assignment, quality prediction, and pipeline parallelization. The proposed framework is able to provide a flexible balance between reconstruction time and quality satisfaction. As for DNN inference, a joint resource allocation and DNN partitioning framework is proposed. The outcomes of this research seek to benefit the future distributed computing, smart applications, and data-intensive science communities to build effective, efficient, and robust MEC environments
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