Dynamic Pattern Generation Research Articles

Game of life simulation with React.js

The Game of Life, created by John Conway in 1970, is a solitary game that utilizes a grid of living or dead cells. Through simple rules of interaction, the game evolves automatically from an initial configuration set by the player. The game takes place on a theoretically infinite grid, although in practice, a finite grid is used due to physical or programming limitations. Each cell interacts with its neighbors following predefined rules. This project employed the JavaScript library React.js to implement a graphical interface that allows the user to interact with the Game of Life and observe the generated patterns. An application was created that displays a grid where different patterns and configurations can be applied, and the simulation speed can be controlled. It serves as a model for understanding and studying complex and dynamic phenomena. By exploring and applying it in programming using tools like React.js, a better understanding of dynamic systems and pattern generation can be obtained. The obtained results showcase the evolution of the game over time, generating changing patterns and demonstrating the complexity and diversity of emergent behaviors.

Video anomaly detection based on spatio-temporal relationships among objects

Video anomaly detection is to automatically identify predefined anomalous contents (e.g. abnormal objects, behaviors and scenes) in videos. The performance of video anomaly detection can be effectively improved by making the model focus more on the anomalous objects in videos. However, such existing approaches usually rely on pre-trained models, which not only require additional auxiliary information but also face the challenge of anomaly diversity in the real world. In this paper, we propose a new video anomaly detection method based on spatio-temporal relationships among objects. Concretely, we use a fully convolutional encoder-decoder network with symmetric skip connections as the backbone network, which can effectively extract features from the object regions at different scales. In the encoding stage, an attention mechanism is used to enhance the model’s understanding of the spatio-temporal relationships among various types of objects in the video. In the decoding stage, a dynamic pattern generator is designed to memorize the inter-object spatio-temporal relationships, which thus enhances the reconstructions of normal samples while making the reconstructions of abnormal samples more difficult. We conduct extensive experiments on three widely used video anomaly detection datasets CUHK Avenue, ShanghaiTech Campus and UCSD Ped2, and the experimental results show that our proposed method can significantly improve the performance, and achieves state-of-the-art overall performance (considering both effectiveness and efficiency). In particular, our method achieves a state-of-the-art AUC of 98.4% on the UCSD Ped2 dataset that consists of various anomalies in real-world scenarios.

Multi-level Attack with Dynamic S-box Variable key Pattern Generation for Key Cohort Using AES

In recent times, data transmission in electronic medium is found to be more susceptible to several attacks. The study aims to control the multi-level attacks in encryption and decryption process by using Advanced Encryption Standard (AES) algorithm based S box operations. In AES based variable key generation pattern, every round generates the new key. The generation of multiple keys strengthen the operation of AES-dynamic S box. The AES algorithm performs operation on a 128 bit plain text and utilizes identical key for decryption and encryption process. The proposed algorithm shows significant improvements in the quality of encryption and decryption. The performance of the proposed system has been analysed in accordance with delay, power consumption and number of slices. Further the efficiency of the proposed system has been compared with other existing methods such as Positive Polarity Reed Muller (PPRM), Modified Positive Polarity Reed Muller (MPPRM) Twisted Binary Decision Diagram (TBDD) and Composite Field (CF) architecture. The results exposed that the proposed system outperforms with superior performance.

Edge of Chaos in Memristor Cellular Nonlinear Networks

Information processing in the brain takes place in a dense network of neurons connected through synapses. The collaborative work between these two components (Synapses and Neurons) allows for basic brain functions such as learning and memorization. The so-called von Neumann bottleneck, which limits the information processing capability of conventional systems, can be overcome by the efficient emulation of these computational concepts. To this end, mimicking the neuronal architectures with silicon-based circuits, on which neuromorphic engineering is based, is accompanied by the development of new devices with neuromorphic functionalities. We shall study different memristor cellular nonlinear networks models. The rigorous mathematical analysis will be presented based on local activity theory, and the edge of chaos domain will be determined in the models under consideration. Simulations of these models working on the edge of chaos will show the generation of static and dynamic patterns.

An Automated Testing Framework for Gesture Recognition System using Dynamic Image Pattern Generation with Augmentation

In the field of information technology, the gesture recognition system plays a very essential role. As it has achieved vast importance, it is mandatory to test the recognition system to ensure the quality of the system by identifying the bugs in the software. In our research, we suggested a dynamic testing method for gesture recognition software. using dynamic image pattern generation with augmentation. The automated software testing framework is a set of processes to create new test cases for properly testing a image processing software. The research intention for generate automated testing cases by following a standard process which helps to increase the performance and efficiency of the gesture recognition system. We have built the framework to give proper testing and give result (accuracy and defect) for which gesture recognition system already in the market. our research, the team strongly following and adding two software testing standard. First one is ISO/IEC/IEEE/291129-3 to define the process for testing software. And the second one is ISO/IEC/IEEE/291129-5 to implement the techniques for software testing. We proposed this framework with major five parameters by noise, rotation, background, contrast, and scale. Which are the most use with every gesture recognition system. Our developed framework’s phase is used to generate new testing cases based on the existing gesture recognition system’s data. There are we work with five systems, commonly with the gesture recognition for experiments. We provide the testing report with total accuracy and defect by comparing existing well-known system’s data. At the final result, our system suggested an analysis report based on the testing result. And tell what are the improvement needs for the existing system to consider noised images or different scaled images to build a robust system.
 GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 42-50

Fuzzy Dynamic Gait Pattern Generation for Real-Time Push Recovery Control of a Teen-Sized Humanoid Robot

A fuzzy dynamic gait pattern generator, which allows a teen-sized humanoid robot to generate, in real-time, a suitable gait pattern when it is hit by an unexpected force, is proposed in this paper. Conventional gait pattern generators usually utilize the ideal Zero Moment Point (ZMP) to plan the trajectory of the Center of Mass (CoM), along with a cycloid to generate steps. However, pre-planned gait patterns cannot deal with unexpected situations, especially instances when the robot experiences an unknown force. Therefore, we propose a dynamic gait pattern generator that leverages the Virtual Force Linear Inverted Pendulum Model (VFLIPM) to adjust the trajectory of the CoM, and which detects balance status by estimating the trajectory of the ZMP using eight high-precision load cell pressure sensors mounted onto the robot's soles. We integrate an accelerometer and the pressure sensors through a fuzzy controller to instantly respond to external forces and generate a suitable gait pattern. When the robot is pushed suddenly, it first adopts a pre-planned gait pattern to replace the current gait. At the same time, the fuzzy controller calculates the recovery gait, with appropriate strides and lean angles to absorb the impact. The proposed method is implemented on the teen-sized humanoid robot, David Junior II, for the Push Recovery event at RoboCup. David Junior II endured a hit with potential energy during walking, which is 1.3 times more robust than when standing.

A Compact Modular Soft Surface With Reconfigurable Shape and Stiffness

A variety of reconfigurable surface devices, utilizing large numbers of actuated physical pixels to produce discretized three-dimensional contours, have been developed for different purposes in research and industry. The difficulty of integrating many actuators in close configuration has limited the DOF and resolution and performance of existing devices. Utilizing vacuum power and soft material actuators, we have developed a soft reconfigurable surface (SRS) with multimodal control and performance capabilities. The SRS is comprised of a square grid array of linear vacuum-powered soft pneumatic actuators, built into plug-and-play modules which enable the arrangement, consolidation, and control of many DoF. In addition to the practical benefits of system integration, this architecture facilitates the construction of customized assemblies with an overall compact form factor. A series of experiments is performed to illustrate and validate the versatility of the SRS for achieving diverse tasks including force-controlled modulation of interface pressure through integrated sensors, lateral manipulation of a variety of objects, static and dynamic shape and pattern generation for haptic interaction, and variable surface stiffness tuning. This SRS concept is scalable, space efficient, and features diverse functional potential. This will extend the utility and accessibility of tangible robotic interfaces for future applications from industrial to home and personal use.

Fault-Tolerant Electro-Responsive Surfaces for Dynamic Micropattern Molds and Tunable Optics

Electrically deformable surfaces based on dielectric elastomers have recently demonstrated controllable microscale roughness, ease of operation, fast response, and possibilities for programmable control. Potential applications include marine anti-biofouling, dynamic pattern generation, and voltage-controlled smart windows. Most of these systems, however, exhibit limited durability due to irreversible dielectric breakdown. Lowering device voltage to avoid this issue is hindered by an inadequate understanding of the electrically-induced wrinkling deformation as a function of the deformable elastic film thickness. Here we report responsive surfaces that overcome these shortcomings: we achieve fault-tolerant behavior based on the ability to self-insulate breakdown faults, and we enhance fundamental understanding of the system by quantifying the critical field necessary to induce wrinkles in films of different thickness and comparing to analytical models. We also observe new capabilities of these responsive surfaces, such as field amplification near local breakdown sites, which enable actuation and wrinkle pattern formation at lower applied voltages. We demonstrate the wide applicability of our responsive, fault-tolerant films by using our system for adjustable transparency films, tunable diffraction gratings, and a dynamic surface template/factory from which various static micropatterns can be molded on demand.

Complex multicellular functions at a unicellular eukaryote level: Learning, memory, and immunity.

According to experimental data, eukaryote unicellulars are able to learn, have immunity and memory. Learning is carried out in a very primitive form, and the memory is not neural but an epigenetic one. However, this epigenetic memory, which is well justified by the presence and manifestation of hormonal imprinting, is strong and permanent in the life of cell and also in its progenies. This memory is epigenetically executed by the alteration and fixation of methylation pattern of genes without changes in base sequences. The immunity of unicellulars is based on self/non-self discrimination, which leads to the destruction of non-self invaders and utilization of them as nourishment (by phagocytosis). The tools of learning, memory, and immunity of unicellulars are uniformly found in plasma membrane receptors, which formed under the effect of dynamic receptor pattern generation, suggested by Koch etal., and this is the basis of hormonal imprinting, by which the encounter between a chemical substance and the cell is specifically memorized. The receptors and imprinting are also used in the later steps of evolution up to mammals (including man) in each mentioned functions. This means that learning, memory, and immunity can be deduced to a unicellular eukaryote level.

A 3D Optical Surface Profilometer Using a Dual-Frequency Liquid Crystal-Based Dynamic Fringe Pattern Generator.

We propose a liquid crystal (LC)-based 3D optical surface profilometer that can utilize multiple fringe patterns to extract an enhanced 3D surface depth profile. To avoid the optical phase ambiguity and enhance the 3D depth extraction, 16 interference patterns were generated by the LC-based dynamic fringe pattern generator (DFPG) using four-step phase shifting and four-step spatial frequency varying schemes. The DFPG had one common slit with an electrically controllable birefringence (ECB) LC mode and four switching slits with a twisted nematic LC mode. The spatial frequency of the projected fringe pattern could be controlled by selecting one of the switching slits. In addition, moving fringe patterns were obtainable by applying voltages to the ECB LC layer, which varied the phase difference between the common and the selected switching slits. Notably, the DFPG switching time required to project 16 fringe patterns was minimized by utilizing the dual-frequency modulation of the driving waveform to switch the LC layers. We calculated the phase modulation of the DFPG and reconstructed the depth profile of 3D objects using a discrete Fourier transform method and geometric optical parameters.

A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment

Industrial chemical plant diagnosis is the task of analyzing process data to sufficiently pinpoint the causes of abnormal events as fast and as accurately as possible so corrective action can be taken in a timely manner. The need to identify failures explicitly and support human centered decision making becomes pronounced for enterprises. Qualitative diagnostic models offer robustness in capturing diagnostic behaviors when there is little or no data on fault conditions. This article develops, analyzes, and demonstrates a qualitative diagnostic methodology called Causal Link Assessment (CLA). CLA avoids the drawbacks of other methodologies while leveraging several new concepts that include dynamic pattern generation, single time step modeling with multitime step interpretation, and discretized, low granularity dynamic modeling. CLA is demonstrated for an existing ethylene production facility. Model building, robustness, reusability, unaccounted for faults and failures and alignment with emerging Smart Manufacturing infrastructure concepts are discussed. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3420–3431, 2016

FuSM the dynamic behavior pattern generation technique of the NPC according to user based on Genetic Algorithm

FuSM the dynamic behavior pattern generation technique of the NPC according to user based on Genetic Algorithm

Dynamic model and CPG network generation of the underwater self-reconfigurable robot

In this paper, we use one unified method based on graph theory and path matrix to solve problems of kinematic and dynamic modeling and Central Pattern Generator (CPG) network generation of different configurations of the underwater self-reconfigurable robot. Considering complex and changeable characteristics of the underwater self-reconfigurable robot, we introduce the knowledge of graph theory to solve the problem of the unified configuration description and use a path matrix to describe different configurations. Combining Kane’s equations, we propose a unified modeling method of the underwater self-reconfigurable robot. In view of rhythmic movement characteristics of the underwater self-reconfigurable robot, we introduce CPG as the basic unit. Combining the path matrix, the method of making the CPG network evolving from the configuration is proposed. Taking typical configurations of the underwater self-reconfigurable robot as example, applicability and effectiveness of the proposed method are verified.

Dynamic Statistical-Timing-Analysis-Based VLSI Path Delay Test Pattern Generation

Nanoscale VLSI systems are subject to increasingly significant performance variability. Accurate timing analysis and effective silicon-based performance verification techniques are critical to successful nanoscale VLSI design. The state-of-the-art statistical static timing analysis (SSTA) techniques cannot capture performance variability due to primary inputs and sequential element states which, however, is critical to path delay test generation. In this paper, we present the first dynamic statistical timing analysis-based VLSI path delay test pattern generation technique. We observe that VLSI timing analysis and power estimation target the same signal toggling activity. By leveraging the existing power estimation techniques, we have developed signal-probability-based statistical timing analysis (SPSTA), and SPSTA-based VLSI delay test pattern generation (SPSTA-DTPG) techniques. Our experimental results based on ISCAS’89 benchmark circuits show that the state-of-the-art statistical static timing analysis-based delay test pattern generation (SSTA-DTPG) achieves an average of 47.32%, 45.14%, and 57.98%, SPSTA-DTPG achieves an average of 57.41%, 61.43%, and 68.05%, while signal probability-based statistical timing analysis-based delay test pattern generation with (test pattern) compaction (SPSTA-DTPG-C) achieves an average of 83.09%, 87.48% and 90.30% coverage of the top 50, 100, and 200 timing-critical paths, respectively.

Hiding the squid: patterns in artificial cephalopod skin.

Cephalopods employ their chromomorphic skins for rapid and versatile active camouflage and signalling effects. This is achieved using dense networks of pigmented, muscle-driven chromatophore cells which are neurally stimulated to actuate and affect local skin colouring. This allows cephalopods to adopt numerous dynamic and complex skin patterns, most commonly used to blend into the environment or to communicate with other animals. Our ultimate goal is to create an artificial skin that can mimic such pattern generation techniques, and that could produce a host of novel and compliant devices such as cloaking suits and dynamic illuminated clothing. This paper presents the design, mathematical modelling and analysis of a dynamic biomimetic pattern generation system using bioinspired artificial chromatophores. The artificial skin is made from electroactive dielectric elastomer: a soft, planar-actuating smart material that we show can be effective at mimicking the actuation of biological chromatophores. The proposed system achieves dynamic pattern generation by imposing simple local rules into the artificial chromatophore cells so that they can sense their surroundings in order to manipulate their actuation. By modelling sets of artificial chromatophores in linear arrays of cells, we explore the capability of the system to generate a variety of dynamic pattern types. We show that it is possible to mimic patterning seen in cephalopods, such as the passing cloud display, and other complex dynamic patterning.

Controlling Rhythm in the Frequency Domain

This article describes a way to approach music sequencing, with particular focus on the generation of dynamic rhythm patterns. In contrast to the well known “step sequencer” or “piano roll” style interfaces that specify note pitches and amplitudes as values located at points in time, the device described here calculates note patterns from the sum of a series of discrete sinusoids. When these patterns trigger velocity sensitive percussion sounds, useful rhythms can be generated and transformed with very few controls.

Simulation platform for the underwater snake-like robot swimming based on Kane’s dynamic model and central pattern generator

A systematic method for swimming control of the underwater snake-like robot is still lacking. We construct a simulation platform of the underwater snake-like robot swimming based on Kane’s dynamic model and central pattern generator (CPG). The partial velocity is deduced. The forces which contribute to dynamics are determined by Kane’s approach. Hydrodynamic coefficients are determined by experiments. Then, we design a CPG-based control architecture implemented as the system of coupled nonlinear oscillators. The CPG, like its biological counterpart, can produce coordinated patterns of rhythmic activity while being modulated by simple control parameters. The relations between the CPG parameters and the speed of the underwater snake-like robot swimming are investigated. Swimming in a straight line, turning, and switching between swimming modes are implemented in our simulation platform to prove the feasibility of the proposed simulation platform. The results show that the simulation platform can imitate different swimming modes of the underwater snake-like robot.

Matched filtering Generalized Phase Contrast using binary phase for dynamic spot- and line patterns in biophotonics and structured lighting

This work discusses the use of matched filtering Generalized Phase Contrast (mGPC) as an efficient and cost-effective beam shaper for applications such as in biophotonics, optical micromanipulation, microscopy and two-photon polymerization. The theoretical foundation of mGPC is described as a combination of Generalized Phase Contrast and phase-only correlation. Such an analysis makes it convenient to optimize an mGPC system for different setup conditions. Results showing binary-only phase generation of dynamic spot arrays and line patterns are presented.

Research of Slicing CAD Models with Pro/TOOLKIT for Integral Stereolithography

According to the requirements of integral Stereolithography (integral SL) System, a new direct slicing method under Pro/ENGINEER environment is developed. 3D CAD models are sliced with Pro/TOOLKIT in Pro/ENGINEER environment, and section image of 3D models can be obtained and saved as image format. Dynamic masks are produced with the cross-section images which obtained through the slicing method by dynamic pattern generator, and then 3D real prototyping is fabricated conveniently with integral SL System. A 3D tower model is sliced with this method, and the tower prototyping also is fabricated with integral SL System. Compared with the STL format files slicing method, the new slicing method can achieve high accuracy of layer section profile. The direct slicing method provides a new solution for integral SL system to build objects with high accuracy.

Power Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud

Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip aware VM scheduler. The experimental results show that the appropriate usage of different power saving states guarantees significant energy conservation in handling stochastic nature of workload without compromising the performance, both when the data center is in low as well as moderate utilization.