Biocomputing - a cutting-edge field of technology - operates at the intersection of biology, engineering, and computer science. It seeks to use cells or their sub-component molecules (such as DNA or RNA) to perform functions traditionally performed by an electronic computer.
 
The ultimate goal of biocomputing is to mimic some of the biological ‘hardware’ of bodies like ours - and to use it for our computing needs. From less to more complicated, this could include:

1. Using DNA or RNA as a medium of information storage and data processing
2. Connecting neurons to one another, similar to how they are connected in our brains
3. Designing computational hardware from the genome level up

 
Cells Already Compute
 
Cells are far more powerful at computing than our best computers. For example:

1. Cells store data in DNA
2. Receive chemical inputs in RNA (data input)
3. Perform complex logic operations using ribosomes
4. Produce outputs by synthesizing proteins

 
Biocomputing’s engineering challenge is to gain a granular level of control of the reactions between organic compounds like DNA or RNA.
Overheating & High Energy Use
 
Traditional computers use microchips, which heat up quickly. Supercomputers are usually a collection of several high-speed traditional computers, combined into a single unit. Generally, they are not qualitatively different from traditional computers. Even so, supercomputers use a lot of energy, heat up quickly, and require massive cooling units in order to function at full speed. On the other hand, biological matter can perform calculations and process data without using as much energy, and without heating up significantly.
 
Multitasking
 
Regular computers perform one task at a time and switch quickly between tasks to give the user a seamless experience of multiple tasks running simultaneously. Biological systems, on the other hand, engage in ‘parallel computation’ – whereby multiple tasks can be executed truly simultaneously.
 
Early proof-of-concept work has been completed using myosin - a superfamily of motor proteins which cause muscle contraction and convert chemical energy into mechanical energy. Myosin-enabled biocomputing could perform multiple computations simultaneously.
 
Self-Organizing and Self-Repairing
 
Biological molecules also display an intelligent ability to self-organize and self-repair. So, biocomputing engineers will have to find ways to simulate this intelligent ‘software’ on top of the biological molecule ‘hardware’ to produce, organize, and repair the biocomputing system.
 
Similar to a living organism the “software” in biological systems is responsible for producing and assembling the hardware which in turn will help run the software.
 
Conclusion
 
While biocomputing is in an early phase, biocomputers have the potential to enable far more powerful computing than today’s best computers – while using less energy and generating less heat. Furthermore, biocomputers will be able to use parallel computing, which will represent a significant improvement upon regular computing, and will be able to better self-organize and self-repair. While authoritative estimates of the eventual environmental impact of biocomputing do not yet exist, biocomputing could potentially reduce our reliance on the silicon and rare earth minerals that power today’s computers.


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Be future ready by understanding today's emerging technologies.
Shaan Ray

Sensor fusion is the process of combining data from multiple physical sensors in real time, while also adding information from mathematical models, to create an accurate picture of the local environment. A system can then use this data to plan and act toward an objective or destination. Sensor fusion is an important part of the design of autonomous systems.
 
The cost of sensors has been declining for decades, while the quality of information collected by sensors has been increasing. Still, each type of sensor has its shortcomings. Even if a sensor provides a significant volume of high-quality data, the sensor could be thrown off under some conditions, leading to inaccurate readings. For example, a sensor on an autonomous vehicle could be thrown off by unusual weather, smog and pollution, speed, altitude, visibility, angle and positioning. By adding more sensors, engineers can often improve the accuracy of collected data.
 
The Process: Sense, Perceive, Plan, Act, Repeat
 
Sensors “sense” by collecting data from the physical world. Then, systems “perceive”, or interpret this data based on algorithms, use cases and requirements. Next, they “plan”, or find a path to move forward toward the desired outcome or destination. Lastly, based on their plans, they “act”, or follow a path toward the intended destination or outcome. The system goes on repeating these steps until it accomplishes its task or reaches its destination.
 
Direct and Indirect Sensor Fusion
 
Direct fusion happens when data originates from identical sensors in a given environment, while indirect fusion occurs when data originates from non-identical sensors in a given environment. For improved environmental awareness, it is important to boost both the quality and the quantity of data collected. So, ideally, a system should use both direct and indirect sensor fusion.
 
Sensor Types
 
Sensor fusion can be achieved using a wide variety of sensors. These include: accelerometers, GPS, magnetic sensors, phased arrays, electronic support measures, seismic sensors, sonobuoys, radio telescopes, cameras, radar, LIDAR and sonar systems.
 
Benefits of Sensor Fusion
 
Sensor fusion has several benefits:

• It usually increases the quality of data a system receives, as discussed
• It increases system reliability
• It can help estimate unmeasured states
• It can be used to increase the coverage area
• It reduces latency in the system
• It increases the ability to share data

 
Use Cases
 
Today, sensor fusion is primarily used in autonomous vehicles like self-driving cars. However, research and testing is underway to use sensor fusion in other use cases in fields like space exploration, remote search and rescue, industrial internet, defense, and environmental monitoring. The field of sensor fusion is also seeing increased interest among academics and roboticists.
 
Conclusion
 
Sensor fusion increases the number of identical sensors, diversifies the types of sensors, and uses mathematical models to synthesize and refine information collected by sensors. Since sensor fusion enables more accurate localization, positioning, detecting and tracking, it improves autonomous systems’ situational awareness and makes the systems more consistent, accurate and dependable. The use of sensor fusion in autonomous vehicles will lead to significant innovation and know-how, which will find application in a wide variety of industries.


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Be future ready by understanding today's emerging technologies.
Shaan Ray