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Many people have learned to appreciate the advent of GPS based navigational applications in our daily lives through the use of street level navigation, and many more loathe the same applications when using them to navigate established public transportation systems. Many of these travelers become confused and frustrated when attempting to understand and act on the directions given to them by such existing applications that primarily focus on large-scale street navigation, especially if the user has a visual or cognitive impairment. Several existing applications will not even attempt to aid someone in the navigation of say, a metro, train or bus station, and instead simply inform the user of the label of the route that the application intends the user to take. Without any small-scale directions many people find themselves struggling to figure out what platform or boarding zone they need to use to get on their preferred method of transportation, as well as how to get to these platforms and boarding zones in the first place. These transit hubs, plazas, malls, and the like have long been a pain in the side of developers and users alike when it comes to navigation. Innovation has long been overdue in this space concerning small scale transit plaza navigation, with major players holding large market shares in navigation not even attempting to address this longstanding problem. The only existing application to offer indoor navigation offers very limited as well as inconsistent functionality including only two-dimensional indoor mapping, due to manually uploaded floor plans that are only available in the first place from partnering locations. This has continued to be an issue due to a lack of adoption by existing locations, as each location is required to draw out their floor plan on an antiquated image file and submit it for approval. Solving this problem would ease a large amount of stress for those navigating in areas they are not familiar with, as well as saving time that could possibly make the difference between a missed train and a nearly missed train.

Although GPS can be used for localization outdoors, indoor environments (office buildings, shopping malls, transit hubs) can be particularly challenging for many of the general population, and especially for blind walkers. GPS-denied environments have received considerable attention in recent years as our population’s digital expectations grow. To address GPS-denied environments, various services have been explored, including technology based on Bluetooth low energy (BLE), Wi-Fi, and camera. Drawbacks with these approaches are common, including calibration (fingerprinting) overhead using Wi-Fi, beacon infrastructure costs using BLE, and unoccluded visibility requirements in camera-based systems. While localization and wayfinding using inertial sensing overcomes these challenges, large errors with accumulated drift are known. Moreover, the decoupling of the orientation of the phone from the direction of walking, as well as accurately detecting walker’s velocity and detecting steps and measuring stride lengths, have also been challenges for traditional pedestrian dead reckoning (PDR) systems. Relatedly, blind walkers (especially those who do not use a dog guide) often tend to veer when attempting to walk in a straight line, and this unwanted veering may generate false turn detections with such inertial methods.

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The field of autonomous transportation is rapidly evolving to operate in diverse settings and conditions. However, as the number of autonomous vehicles on the road increases the complexity of the computations needed to safely operate all of the autonomous vehicles grows rapidly. across multiple vehicles, this creates a very large volume of computations that must be performed very quickly (e.g., in real or near-real time).   Thus, treating each autonomous vehicle as an independent entity may result in inefficient use of computing resources, as many redundant data collections and computations may be performed (e.g., two vehicles in close proximity may be performing computations related to the same detected object). To address this issue, researches at UC Berkeley proposed algorithms for the management and exchange of shared information across nearby and distant vehicles.According to the proposed arrangement, autonomous vehicles may share data collected by their respective sensor systems with other autonomous vehicles and adjust their operations accordingly in a manner that is more computationally efficient. This can not only increase safety but at the same time reduce computational load required by each individual vehicle.

UCLA researchers in the Department of Bioengineering have developed a novel multichannel electrode array to restore locomotion in paralyzed or physically impaired patients.

Background: Current navigation systems offer “shortest-distance” or “shortest-time” functions to help avoid traffic congestion but neither of them determine the most fuel efficient route. With rising gas prices and vehicle emissions, a more advanced navigation system with additional functions, such as an environmentally-friendly feature, is needed. This accomplishment can make a huge improvement on increasing fuel costs and air pollution. The in-vehicle navigation system is also expected to competitively penetrate the US market in the next couple years with annual sales quadrupling to $13M.  Brief Description: UCR researchers have developed an innovative vehicle navigation system (VNS) that will allow users to choose a route that is the most gas efficient and emanates less emissions. The energy- and emissions-minimization function is incorporated on top of distance- and time-minimizing functions that currently exists in the traditional VNS. This new intelligent transportation system utilizes a state-of-the-art modal emissions model (CMEM) that encompasses real-world vehicle activity patterns, and can calculate the fuel consumption and emission values of each vehicle trajectory.

Assistive exoskeletons are designed to enable humans to perform tasks otherwise beyond their capacities. One area of particular interest is the upper limb. Existing devices for upper limb assistance are powered by active or passive methods. Active devices use motors, but require complicated controllers and consistent power to perform tasks. Passive devices do not require power, but often have fixed parameters meaning that they are not especially versatile. Moreover, the devices that currently exist tend to be bulky, costly, and inefficient. To address those deficiencies, UC Berkeley researchers have developed a semi-passive assistive device for upper limbs. The Berkeley device is lightweight, reduces user fatigue, and increases load carrying capacity. The device is highly versatile, and is able to increase the mobility and functionality of a user’s arm.

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