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Guava Trees Disease Monitoring Using the Integration of Machine Learning and Predictive Analytics
The increase in population, food demand, and the pollution levels of the environment are considered major problems of this era. For these reasons, the traditional ways of farming are no longer suitable for early and accurate detection of biotic stress. Recently, precision agriculture has been extensively used as a potential solution for the aforementioned problems using high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. In this paper, several methods of machine learning have been utilized
Leveraging primary feedback and spectrum sensing for cognitive access
We consider a time-slotted primary system where both the primary channel and primary activity are modeled as two independent two-state Markov chains. The primary transmitter can be idle or busy, whereas the channel can be in erasure or not. Moreover, the sensing channel between the primary transmitter and secondary transmitter is modeled as a two-state Markov chain to represent two levels of sensing reliability. At the beginning of each time slot, the secondary transmitter may remain idle, transmit directly, or probe the channel and access the channel only if it is sensed to be free. At the
Optimal random access and random spectrum sensing for an energy harvesting cognitive radio
We consider a secondary user with energy harvesting capability. We design access schemes for the secondary user which incorporate random spectrum sensing and random access, and which make use of the primary automatic repeat request (ARQ) feedback. The sensing and access probabilities are obtained such that the secondary throughput is maximized under the constraints that both the primary and secondary queues are stable and that the primary queueing delay is kept lower than a specified value needed to guarantee a certain quality of service (QoS) for the primary user. We consider spectrum sensing
Optimal random access for a cognitive radio terminal with energy harvesting capability
The deterministic multicast capacity of 4-node relay networks
In this paper, we completely characterize the deterministic capacity region of a four-node relay network with no direct links between the nodes, where each node communicates with the three other nodes via a relay. Towards this end, we develop an upper bound on the deterministic capacity region, based on the notion of a one-sided genie. To establish achievability, we use the detour schemes that achieve the upper bound by routing specific bits via indirect paths instead of sending them directly. © 2013 IEEE.
Towards evolving sensor actor networks
Sensor Actor NETworks (SANET) represent a major component of ubiquitous service environments promising interesting solutions to a wide range of problems. Despite the obvious increase in the research activities proposing architectures and protocols for SANETs, we are still no where near the production of industrial-grade SANET software that can be relied upon for mission critical applications. The cost of programming, deploying and maintaining SANET environments is still highly prohibitive due to the lack of industrial tools capable of realizing adaptive SANET software in a cost effective way