Vehicle Anti-Theft Technology

Property crimes are said to hover around 10 million annually. Of this, vehicle theft tops the list and often occurs in all parts of the world. In response to a Right to Information (RTI) query made by The Indian Express shows that a total of 2,801 motor vehicle thefts took place in 2020 Mumbai, where in 1,085 cases account for 39 per cent of the total cases detected. Of the total 2,801 vehicle theft cases, 2,019 cases were of two-wheelers, 185 were four-wheelers and the remaining 623 cases were thefts of other vehicles like rickshaws, tempos etc.. The methods currently involved in vehicle theft detection such as buzzers and alarms have become cognizant to everyone including the burglars and effortlessly override the system and steal the vehicle. This paper proposes a system presenting a mechanism to mitigate the possibility of vehicle thefts. The system provides security by sending an SMS message as soon the vehicle is moved or crosses Geo-Barrier grid without knowledge of owner and also provides the owner with set of actions that can also be taken. System also offers location updates timely to the registered devices through SiM7600E. New features can be periodically be added hence making it future proof.

Real-world objects are not stored in holistic representations in visual working memory

When storing multiple objects in visual working memory, observers sometimes misattribute perceived features to incorrect locations or objects. These misattributions are called binding errors (or swaps) and have been previously demonstrated mostly in simple objects whose features are easy to encode independently and arbitrarily chosen, like colors and orientations. Here, we tested whether similar swaps can occur with real-world objects, where the connection between features is meaningful rather than arbitrary. In Experiments 1 and 2, observers were simultaneously shown four items from two object categories. Within a category, the two exemplars could be presented in either the same or different states (e.g., open/closed; full/empty). After a delay, both exemplars from one of the categories were probed, and participants had to recognize which exemplar went with which state. We found good memory for state information and exemplar information on their own, but a significant memory decrement for exemplar-state combinations, suggesting that binding was difficult for observers and “swap” errors occurred even for meaningful real-world objects. In Experiment 3, we used the same tasks, but on half of the trials, the locations of the exemplars were swapped at test. We found that participants ascribed incorrect states to exemplars more frequently when the locations of exemplars were swapped. We concluded that the internal features of real-world objects are not perfectly bound in working memory, and location updates impair the object representation. Overall, we provide evidence that even real-world objects are not stored in an entirely unitized format in working memory.