Engineering Anomalies Research

The Princeton Engineering Anomalies Research (PEAR) program, which flourished for nearly three decades under the aegis of Princeton University’s School of Engineering and Applied Science, has completed its experimental agenda of studying the interaction of human consciousness with sensitive physical devices, systems, and processes, and developing complementary theoretical models to enable better understanding of the role of consciousness in the establishment of physical reality. PEAR has now incorporated its present and future operations into the broader venue of the ICRL, a 501(c)(3) not-for-profit research organization, in addition to Psyleron—a company that provides Random Event Generator devices to enable the continued exploration of PEAR’s findings by the general public and research communities. On the accompanying menu of pages we have attempted to sketch the substance, spirit, and findings of this scholarly endeavor.

United Nations University Psi Land : Electronqiue Tout d'abord, en parapsychologie on parle souvent de pluridisciplinarité, et si elle apparaît quelque part, c'est bien dans ce domaine. Cela dit, nous nous limiterons à un descriptif de l'usage de l'électronique, ce qui permettra tout de même d'aborder quelques idées spécifiques de cette recherche. Mais d'abord, il ne sera sans doute pas inutile de définir ce qu'est la parapsychologie. Dans le grand "fourre-tout" du paranormal, on trouve une belle série de bizarreries, telles que "le triangle des Bermudes", "l'ufologie", "les médecines douces", "le Yéti", la "parapsychologie", etc., etc. Ce "grand sac" est bien pratique aux charlatants pour escroquer, ou aux "debunkers" pour dénigrer obstinément. Mais bien que l'ufologie puisse être un sujet honorable, elle n'a rien à voir avec l'astrologie ou avec la parapsychologie. Pour se définir en tant que recherche scientifique, la parapsychologie a besoin de définir son objet d'étude. Il s'agit d'une expérience de télépathie de groupe. Ganzfeld ESP

WordNet Psyleron, Inc. - Consciousness Research and Technologies using Random Event Generators The Connectome — Harvard School of Engineering and Applied Sciences Lead investigators Hanspeter Pfister (SEAS ), Jeff Lichtman (FAS/Molecular & Cellular Biology, Center for Brain Science) and Clay Reid (HMS/Neurobiology, Center for Brain Science) Description The overall goal of the Connectome project is to map, store, analyze and visualize the actual neural circuitry of the peripheral and central nervous systems in experimental organisms, based on a very large number of images from high-resolution microscopy. The proposing team from the Center for Brain Sciences has already demonstrated its capacity for, and expertise in, high-throughput imaging.

Rob Seward :: True Random Number Generator Below is version 1 This generator uses avalanche noise, and is based on a design by Will Ware. Herein are instructions as to how you can use a pic chip to analyze a noise source and output random data serially. I've included circuit diagrams as well as links to instructions for fabricating your own board. There are two types of random numbers: true and pseudo. Pseudo random numbers are created by an algorithm. How it Works From Will Ware: "This circuit uses avalanche noise in a reverse-biased PN junction, the emitter-base junction of the first transistor. The Max232 chip has two pins across which we can get 20V. The Pic chip is for analyzing the noise and sending out random bits. The pic code works like this: the pic records the number of spikes it detects in a short time interval. Also, the board has input for two switches. Code Fabricating the board To create the board I used a laser-printer transfer technique. Read Concise first to get an overview of the process. Parts List Output Analysis

Main Page - Social Justice Wiki Arduino Random Number Generator Inspired by other designs I've seen online, most directly Rob Seward's design, I decided to build my own random number generator based Will Ware's "avalanche noise in a reverse-biased PN junction" (try this mirror of Will Ware's page). Also important is turbid which introduced me to the concept of hash saturation and the math behind it. Unlike Seward's design, I treat the noise source as a digital input. The digital noise is biased, but with no excess correlation (after a few years of aging, ~80% "1" bits, entropy upper-bound (shannon entropy) of about .74 bits per bit). In order to ensure that the output data is high in entropy, 48 input bits are mixed into a 16-bit hash (a CRC with lots of '1' coefficients to ensure efficient mixing) which is then sent out via serial. The package includes a program to read random bytes (rr), and one to add random bytes to the linux /dev/random entropy pool (rd). For more details, see the README and source code (gplv2+) included in the latest zip file.