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Machines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 52
Author(s):  
Mark Jacob Schrader ◽  
Peter Smytheman ◽  
Elizabeth H. Beers ◽  
Lav R. Khot

This note describes the development of a plug-in imaging system for pheromone delta traps used in pest population monitoring. The plug-in comprises an RGB imaging sensor integrated with a microcontroller unit and associated hardware for optimized power usage and data capture. The plug-in can be attached to the top of a modified delta trap to realize periodic image capture of the trap liner (17.8 cm × 17.8 cm). As configured, the captured images are stored on a microSD card with ~0.01 cm2 pixel−1 spatial resolution. The plug-in hardware is configured to conserve power, as it enters in sleep mode during idle operation. Twenty traps with plug-in units were constructed and evaluated in the 2020 field season for codling moth (Cydia pomonella) population monitoring in a research study. The units reliably captured images at daily interval over the course of two weeks with a 350 mAh DC power source. The captured images provided the temporal population dynamics of codling moths, which would otherwise be achieved through daily manual trap monitoring. The system’s build cost is about $33 per unit, and it has potential for scaling to commercial applications through Internet of Things-enabled technologies integration.


2021 ◽  
Vol 11 (23) ◽  
pp. 11134
Author(s):  
Luis Orlando Philco ◽  
Luis Marrone ◽  
Emily Estupiñan

Coverage is an important factor for the effective transmission of data in the wireless sensor networks. Normally, the formation of coverage holes in the network deprives its performance and reduces the lifetime of the network. In this paper, a multi-intelligent agent enabled reinforcement learning-based coverage hole detection and recovery (MiA-CODER) is proposed in order to overcome the existing challenges related to coverage of the network. Initially, the formation of coverage holes is prevented by optimizing the energy consumption in the network. This is performed by constructing the unequal Sierpinski cluster-tree topology (USCT) and the cluster head is selected by implementing multi-objective black widow optimization (MoBWo) to facilitate the effective transmission of data. Further, the energy consumption of the nodes is minimized by performing dynamic sleep scheduling in which Tsallis entropy enabled Bayesian probability (TE2BP) is implemented to switch the nodes between active and sleep mode. Then, the coverage hole detection and repair are carried out in which the detection of coverage holes if any, both inside the cluster and between the clusters, is completed by using the virtual sector-based hole detection (ViSHD) protocol. Once the detection is over, the BS starts the hole repair process by using a multi-agent SARSA algorithm which selects the optimal mobile node and replaces it to cover the hole. By doing so, the coverage of the network is enhanced and better QoSensing is achieved. The proposed approach is simulated in NS 3.26 and evaluated in terms of coverage rate, number of dead nodes, average energy consumption and throughput.


2021 ◽  
Vol 15 ◽  
Author(s):  
Ro’ee Gilron ◽  
Simon Little ◽  
Robert Wilt ◽  
Randy Perrone ◽  
Juan Anso ◽  
...  

Adaptive deep brain stimulation (aDBS) is a promising new technology with increasing use in experimental trials to treat a diverse array of indications such as movement disorders (Parkinson’s disease, essential tremor), psychiatric disorders (depression, OCD), chronic pain and epilepsy. In many aDBS trials, a neural biomarker of interest is compared with a predefined threshold and stimulation amplitude is adjusted accordingly. Across indications and implant locations, potential biomarkers are greatly influenced by sleep. Successful chronic embedded adaptive detectors must incorporate a strategy to account for sleep, to avoid unwanted or unexpected algorithm behavior. Here, we show a dual algorithm design with two independent detectors, one used to track sleep state (wake/sleep) and the other used to track parkinsonian motor state (medication-induced fluctuations). Across six hemispheres (four patients) and 47 days, our detector successfully transitioned to sleep mode while patients were sleeping, and resumed motor state tracking when patients were awake. Designing “sleep aware” aDBS algorithms may prove crucial for deployment of clinically effective fully embedded aDBS algorithms.


2021 ◽  
pp. 108567
Author(s):  
Fatima Salahdine ◽  
Johnson Opadere ◽  
Qiang Liu ◽  
Tao Han ◽  
Ning Zhang ◽  
...  
Keyword(s):  

Author(s):  
Md. Faiz Zulfadly Bin Hj Abdul Latif ◽  
S. H. Shah Newaz ◽  
Alaelddin F. Y. Mohammed ◽  
Au Thien Wan ◽  
Saiful Omar

Author(s):  
M S Pramod ◽  
Sanket N Shettar ◽  
Mohammed Sazeed ◽  
Anirudh Jena ◽  
P Neeraj ◽  
...  

2021 ◽  
Vol 11 (16) ◽  
pp. 7313
Author(s):  
Seung Soo Kwak ◽  
Yun Chan Im ◽  
Yong Sin Kim

As smart grids develop rapidly, low-cost monitoring systems for pole-mounted transformers increase in demand. Even though battery-powered wireless monitoring systems appear to provide optimal solutions, they consume large amounts of energy for continuous sampling and data transmission. Operation and maintenance costs then increase owing to reduced battery lifetime and battery replacement. To overcome this problem, this paper presents an event-driven battery-powered wireless monitoring system that monitors abnormalities of a transformer and transmits data only if an abnormality occurs. When the proposed event controller detects an abnormality, it enables a root mean square (RMS) converter and a peak detector for sampling and transmitting the maximum RMS value of the abnormal signal and then falls into sleep mode until the next event to save energy. Simulation and experimental results show that the proposed system enhances battery lifetime by up to two orders of magnitude compared to a conventional battery-powered wireless monitoring system.


Author(s):  
Sandeep Bishla

This paper shows the scope of making an automatic Power Factor controller with the help of Fuzzy Logic. A Single-phase PF circuit is taken for experimental purposes in two sets of capacitor banks. Single-phase supply connected to Induction Motor as load considering for power factor correction. Capacitor along with its circuit connected in parallel to the Induction Motor. Selection of capacitor is done based on multiplier table using K Factor along with initial Cos of the load. Incoming control from MCB and current measurement are done at incoming as well as on capacitor banks individually. Seeking best selection, represent the relation between KVAr and µF. Results show we got desired permutations & combinations of data for making the FLC rule table. By this, we also thought about the industrial application using reactive power during the jerking load and sleep mode of machines.


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