1,720,974 research outputs found
Enhancing the spatial resolution of presence detection in a PIR based wireless surveillance network
No full textPyroelectric sensors are low-cost, low-power small components commonly used only to trigger alarm in presence of humans or moving objects. However, the use of an array of pyroelectric sensors can lead to extraction of more features such as direction of movements, speed, number of people and other characteristics. In this work a low-cost pyroelectric infrared sensor based wireless network is set up to be used for tracking people motion. A novel technique is proposed to distinguish the direction of movement and the number of people passing. The approach has low computational requirements, therefore it is well-suited to limited-resources devices such as wireless nodes. Tests performed gave promising results
Crioglobulinemia mista del tipo Lospalluto Meltzer in soggetto con manifestazioni purpuriche conseguenti ad antibioticoterapia per sepsi biliare da fistola duodeno coledocica.
No full textCold-haemagglutinin disease with an autoantibody exhibiting different specificities at different temperatures.
No full textA case of chronic cold-haemagglutinin disease is reported in which an agglutinin apparently carrying two specificities was present. At first, specificity was anti-not-I and anti-I respectively: in the course of time anti-I was replaced by anti-A-1. Both anti-I and anti-A-1 could be demonstrated at room temperature only while specificity was anti-not-I at 4 degrees C. The antibody displayed haemolytic activity at room temperature and gave stronger reactions when treated biphasically. The same pattern of specificity was apparent in haemolysis tests, i.e. anti-not-I in the biphasical reaction 4 degrees C goes to 37 degrees C and first anti-I and later anti-A-1 at room temperature and at 22 degrees C goes to 37 degrees C. Anti-A-1 was not neutralized by A bloodgroup substance of animal origin nor by secretor saliva
Pyroelectric InfraRed sensors based distance estimation
No full textIn this paper a novel technique is proposed to detect person position through the use of an array of lowpower, low-cost Pyroelectric InfraRed (PIR) detectors. Typically, PIR sensing elements are used in surveillance or automatic lighting systems to provide a presence/absence digital signal. However, much more information can be extracted from sensors output. Our approach combines the output from two detectors placed on opposite walls of a hallway and facing each other. Through the fusion of simple features calculated locally on sensor nodes we are able to classify in real-time passages through the hallway into three classes according to the distance of the person from the sensors. We evaluated the use of three classifiers: Naïve Bayes, k-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). We achieved a correct classification ratio of 83.49% using naïve Bayes classifier, 86.06% using a linear SVM classifier and 93.75% using 3-NN (k=3) classifier
Tracking Motion Direction and Distance With Pyroelectric IR Sensors
No full textPassive IR (PIR) sensors are excellent devices for wireless sensor networks (WSN), being low-cost, low-power, and presenting a small form factor. PIR sensors are widely used as a simple, but reliable, presence trigger for alarms, and automatic lighting systems. However, the output of a PIR sensor depends on several aspects beyond simple people presence, as, e.g., distance of the body from the sensor, direction of movement, and presence of multiple people. In this paper, we present a feature extraction and sensor fusion technique that exploits a set of wireless nodes equipped with PIR sensors to track people moving in a hallway. Our approach has reduced computational and memory requirements, thus it is well suited for digital systems with limited resources, such as those available in sensor nodes. Using the proposed techniques, we were able to achieve 100% correct detection of direction of movement and 83.49%–95.35% correct detection of distance intervals
Network-Level Power-Performance Trade-Off in Wearable Activity Recognition: A Dynamic Sensor Selection Approach
No full textWearable gesture recognition enables context aware applications and unobtrusive HCI. It is realized by
applying machine learning techniques to data from on-body sensor nodes. We present an gesture recognition
system minimizing power while maintaining a run-time application defined performance target through
dynamic sensor selection.
Compared to the non managed approach optimized for recognition accuracy (95% accuracy), our technique
can extend network lifetime by 4 times with accuracy >90% and by 9 times with accuracy >70%. We
characterize the approach and outline its applicability to other scenarios
Hidden Markov Model based gesture recognition on low-cost, low-power Tangible User Interfaces
No full textThe development of new human–computer interaction technologies that go beyond traditional mouse and keyboard is gaining momentum as smart interactive spaces and virtual reality are becoming part of our everyday life. Tangible User Interfaces (TUIs) introduce physical objects that people can manipulate to interact with smart spaces. Smart objects used as TUIs can further improve the user experiences by recognizing and coupling natural gesture to command issued to the computing system. Hidden Markov Models (HMM) are a typical approach to recognize gestures. In this paper, we show how the HMM forward algorithm can be adapted for its use on low-power, low-cost microcontrollers without floating point unit that can be embedded into several TUI. The proposed solution is validated on a set of gestures performed with the Smart Micrel Cube (SMCube), a TUI developed within the TANGerINE framework. Through the paper we evaluate the complexity of the algorithm and the performance of the recognition algorithm as a function of the number of bits used to represent data. Furthermore, we explore a multiuser scenario where up to four people share the same cube. Results show that the proposed solution performs comparably to the standard forward algorithm run on a PC with double-precision floating point calculations
An integrated multi-modal sensor network for video surveillance
No full textTo enhance video surveillance systems, multi-modal sensor integration can be a successful strategy. In this work, a computer vision system able to detect and track people from multiple cameras is integrated with a wireless sensor network mounting PIR (Passive InfraRed) sensors. The two subsystems are briefly described and possible cases in which computer vision algorithms are likely to fail are discussed. Then, simple but reliable outputs from the PIR sensor nodes are exploited to improve the accuracy of the vision system. In particular, two case studies are reported: the first uses the presence detection of PIR sensors to disambiguate between an opened door and a moving person, while the second handles motion direction changes during occlusions. Preliminary results are reported and demonstrate the usefulness of the integration of the two subsystems
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