Humans and machines do not interface well. In an attempt to bridge the gap between humans and the systems they interact with, a plethora of input methods have been devised: keyboards, mouse, joysticks, game controllers, and touch screens are just a few examples. Unfortunately, none of these devices remove the barrier between man and machine. With the Magic Glove control system, we aim to remove this obstruction by allowing the user to control a hardware device using natural gestures. The Magic Glove takes advantage of a multitude of sensors to capture hand movements and uses this information control a device -in this case, a modified RC car. The goal of this paper is to capture simple hand gestures from the Magic Glove and use that input to wirelessly control a modified RC car. Controlled variables include speed, steering, lights and sounds using a combination of flex, force and gyroscopic sensors. Multiple variables are controlled simultaneously as Magic Glove outputs a constant control signal. Testing showed that novice users were able to wear the glove and control the car with only a small amount of instruction. With some future improvements, it may be possible to remove the learning curve completely.

Botnets are presently the key stage for some Internet assaults, for example, spam, dispersed foreswearing of-benefit (DDoS), fraud, and phishing. The vast majority of the current botnet identification approaches work just on particular botnet order and control (C&C) conventions (e.g., IRC) and structures (e.g., brought together), and can progress toward becoming insufficient as botnets change their C&C strategies. In this paper, we present a general identification structure that is autonomous of botnet C&C convention and structure, what's more, requires no from the earlier information of botnets, (for example, caught bot parallels and henceforth the botnet marks, what's more, C&C server names/addresses). We begin from the definition and fundamental properties of botnets. We characterize a botnet as an organized gathering of malware occurrences that are controlled by means of C&C correspondence channels. The fundamental properties of a botnet are that the bots speak with some C&C servers/peers, perform malevolent exercises, and do as such in a comparative or related way. As needs be, our identification system groups comparative correspondence activity and comparative malevolent movement, and performs cross group connection to recognize the hosts that offer both comparative correspondence designs also, comparable vindictive movement designs. These hosts are in this way bots in the checked system. We have actualized our BotMiner model framework and assessed it utilizing numerous genuine system follows. The outcomes demonstrate that it can recognize certifiable botnets (IRC-based, HTTP-based, and P2P botnets including Nugache and Tempest worm), and has a low false positive rate.

No abstract

No abstract

System on programmable chip for the performance estimation of loom machine, which calculates the efficiency and meter count for weaved cloth automatically. Also it calculates the efficiency of loom machine. Previously the same was done using manual process which was not efficient. This article is intended for loom machines which are not modern.