Survey on Smart Security Surveillance System

As an integral part of the safety and security many organizations, video rental has established its value and benefits many times by providing immediate management of property, people, the environment and property. This project operates in the form of the Embedded Real-Time Surveillance System Based Raspberry Pi SBC for internal detection that enhances monitoring technology to provide critical safety in our lives as well as consistent performance and alert operation. The proposed security solution depends on our integration of cameras and motion detectors into a web application. Raspberry Pi operates and controls motion detectors and video cameras for remote hearing and monitoring, streams streaming video and recording for future playback. This research focuses on the development of a detection system that detects strangers and responds quickly by taking and transferring images to wireless modules based on owners. This Raspberry Pi program based on Smart Surveillance System provides a remote location monitoring concept. The proposed solution provides a fully functional, efficient and easy-to-use global solution. This project will also introduce the concept of motion detection and tracking using image processing. This type of technology is very important when it comes to surveillance and security. The live video stream will be used to show how things can be found and tracked. The detection and tracking process will be based on the pixel threshold. Keyword: Internet Of Things (IOT), Raspberry pi, Picamera, PIR Sensor, Dropbox.

DETERMINATION OF BOUNDARY CONDITIONS FOR THE RADIATION PATTERN OF A MICROELECTRO-OPTICAL INTELLIGENT PASSIVE INFRARED MOTION DETECTOR

In this paper, a block diagram of a microelectro-optical intelligent passive infrared motion detector is proposed. On the basis of the proposed structural scheme and analytically conducted synthetic processing of information from primary sources [5-17], boundary conditions for the directivity diagram of such a detector are determined. The analytical information collected in this article will be necessary for further modeling in computer-aided design with a view to new developments and improvements to existing motion detectors.

Neural mechanisms to exploit positional geometry for collision avoidance

Visual motion provides rich geometrical cues about the three-dimensional configuration the world. However, how brains decode the spatial information carried by motion signals remains poorly understood. Here, we study a collision avoidance behavior in Drosophila as a simple model of motion-based spatial vision. With simulations and psychophysics, we demonstrate that walking Drosophila exhibit a pattern of slowing to avoid collisions by exploiting the geometry of positional changes of objects on near-collision courses. This behavior requires the visual neuron LPLC1, whose tuning mirrors the behavior and whose activity drives slowing. LPLC1 pools inputs from object- and motion-detectors, and spatially biased inhibition tunes it to the geometry of collisions. Connectomic analyses identified circuitry downstream of LPLC1 that faithfully inherits its response properties. Overall, our results reveal how a small neural circuit solves a specific spatial vision task by combining distinct visual features to exploit universal geometrical constraints of the visual world.

Manipulating neural dynamics to tune motion detection

Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. It remains untested how response dynamics of individual cell types drive motion detection and velocity sensitivity. Here, we sped up or slowed down specific neuron types in Drosophila's motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neurons upstream of motion detectors changed their integrating properties and increased their sensitivity to fast or slow visual motion, exposing distinct roles for dynamics in tuning directional signals. A circuit model constrained by data and anatomy reproduced the observed tuning changes. Together, these results reveal how excitatory and inhibitory dynamics jointly tune a canonical circuit computation.

Rats spontaneously perceive global motion direction of drifting plaids

Computing global motion direction of extended visual objects is a hallmark of primate high-level vision. Although neurons selective for global motion have also been found in mouse visual cortex, it remains unknown whether rodents can combine multiple motion signals into global, integrated percepts. To address this question, we trained two groups of rats to discriminate either gratings (G group) or plaids (i.e., superpositions of gratings with different orientations; P group) drifting horizontally along opposite directions. After the animals learned the task, we applied a visual priming paradigm, where presentation of the target stimulus was preceded by the brief presentation of either a grating or a plaid. The extent to which rat responses to the targets were biased by such prime stimuli provided a measure of the spontaneous, perceived similarity between primes and targets. We found that gratings and plaids, when uses as primes, were equally effective at biasing the perception of plaid direction for the rats of the P group. Conversely, for G group, only the gratings acted as effective prime stimuli, while the plaids failed to alter the perception of grating direction. To interpret these observations, we simulated a decision neuron reading out the representations of gratings and plaids, as conveyed by populations of either component or pattern cells (i.e., local or global motion detectors). We concluded that the findings for the P group are highly consistent with the existence of a population of pattern cells, playing a functional role similar to that demonstrated in primates. We also explored different scenarios that could explain the failure of the plaid stimuli to elicit a sizable priming magnitude for the G group. These simulations yielded testable predictions about the properties of motion representations in rodent visual cortex at the single-cell and circuitry level, thus paving the way to future neurophysiology experiments.

Rats spontaneously perceive global motion direction of drifting plaids

Computing global motion direction of extended visual objects is a hallmark of primate high-level vision. Although neurons selective for global motion have also been found in mouse visual cortex, it remains unknown whether rodents can combine multiple motion signals into global, integrated percepts. To address this question, we trained two groups of rats to discriminate either gratings (G group) or plaids (i.e., superpositions of gratings with different orientations; P group) drifting horizontally along opposite directions. After the animals learned the task, we applied a visual priming paradigm, where presentation of the target stimulus was preceded by the brief presentation of either a grating or a plaid. The extent to which rat responses to the targets were biased by such prime stimuli provided a measure of the spontaneous, perceived similarity between primes and targets. We found that gratings and plaids, when uses as primes, were equally effective at biasing the perception of plaid direction for the rats of the P group. Conversely, for G group, only the gratings acted as effective prime stimuli, while the plaids failed to alter the perception of grating direction. To interpret these observations, we simulated a decision neuron reading out the representations of gratings and plaids, as conveyed by populations of either component or pattern cells (i.e., local or global motion detectors). We concluded that the findings for the P group are highly consistent with the existence of a population of pattern cells, playing a functional role similar to that demonstrated in primates. We also explored different scenarios that could explain the failure of the plaid stimuli to elicit a sizable priming magnitude for the G group. These simulations yielded testable predictions about the properties of motion representations in rodent visual cortex at the single-cell and circuitry level, thus paving the way to future neurophysiology experiments.

Employing entropy measures to identify visitors in multi-occupancy environments

Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

Robots and Robotics Kits for Early Childhood and First School Age

<p class="0abstract">Educational robotics can consider one of the newest trends in education and they have been introduced into the classrooms ranging from kindergarten through high school as a means of enriching the learning environment and promote knowledge-building activities. Especially, robotics technologies offer opportunities for young age children, for a practical, hands-on understanding of the things they meet in their daily life but do not fully understand, such as proximity sensors, motion detectors, and light sensors, reasoning failures (software bugs) and connection problems (Wi-Fi, Bluetooth disconnection). In this article, we presented robots that can be used in early childhood and first primary classes of education. The purpose of this article is not to advocate against an educational robot or robotic kit but indeed to inform the educational community so that to make informed decisions regarding the introduction of this kind of technology into the classroom.</p>

The Rotating Snakes Illusion Is a Straightforward Consequence of Nonlinearity in Arrays of Standard Motion Detectors

The Rotating Snakes illusion is a motion illusion based on repeating, asymmetric luminance patterns. Recently, we found certain gray-value conditions where a weak illusory motion occurs in the opposite direction. Of the four models for explaining the illusion, one also explains the unexpected perceived opposite direction.We here present a simple new model, without free parameters, based on an array of standard correlation-type motion detectors with a subsequent nonlinearity (e.g., saturation) before summing the detector outputs. The model predicts (a) the pattern-appearance motion illusion for steady fixation, (b) an illusion under the real-world situation of saccades across or near the pattern (pattern shift), (c) a relative maximum of illusory motion for the same gray values where it is found psychophysically, and (d) the opposite illusion for certain luminance values. We submit that the new model’s sparseness of assumptions justifies adding a fifth model to explain this illusion.