The light fidelity technology refers to visible light communication that uses light as a medium to deliver high speed data which is much greater than that of WiFi. LiFi data is transmitted in several bit streams and the receiver side consisting an IR detector decodes the message. The transmission happens in the form of binary data where 0 means LED in ‘OFF’ state and 1 means that the LED is in the ‘ON’ state. Transmitter and receiver sections contain Arduino which is programmed using Arduino IDE. High power intensity led’s are used in the LiFi transmitter. In receiver section photodiode module is used to detect the light signal generated by the LiFi transmitter. In this we are transmitting the 2 different data using light they are Audio signal and Text signal. Hence the study of various topologies to understand the characteristics a LiFi system.
Wireless sensing element networks (WSNs) area unit vulnerable to selective forwarding attacks which will maliciously drop asubset of forwarding packets to degrade network performance and jeopardize the data integrity. Meanwhile, due to the unstable wireless channel in WSNs, the packet loss rate during the communication of sensing element nodes could also be high and vary from time to time. It poses a good challenge to tell apart the malicious drop and traditional packet loss. During this paper, we propose a Channel-aware name System with adaptive detection threshold (CRS-A) to find selective forwarding attacks in WSNs. The CRS-A evaluates the information forwarding behaviors of sensor nodes, in step with the deviation of the monitored packet loss and also the calculable traditional loss. To optimize the detection accuracy of CRS-A, we tend to in theory derive the best threshold for forwarding analysis, that is adaptive to the time varied channel condition and also the calculable attack chances of compromised nodes. What is more, AN attack-tolerant information forwarding theme is developed to collaborate with CRS-A for stimulating the forwarding cooperation of compromised nodes and up the information delivery magnitude relation of the network. Extensive simulation results demonstrate that CRS-A will accurately find selective forwarding attacks and determine the compromised sensing element nodes, whereas the attack-tolerant information forwarding theme will significantly improve the information delivery magnitude relation of the network.
Aspergilli species cause opportunistic fungal infection in immunocompromised individuals. Invasive aspergillosis is a highly fatal opportunistic infection that accounts for amajor risk to immunocompromised patients. Among these species, A.fumigatus is the main opportunistic pathogen followed by A.niger and A.flavus. In immunocompetent individuals, the effective innate immunity eliminates theinhaled conidia and Allergic bronchopulmonary aspergillosis and aspergilloma are the only infections noted in them. Thus,A.fumigatus was considered for years to beainfirm pathogen. With increase in the number of immunosuppressed patients, however, there has been a marked increase in fatal invasive aspergillosis, which is now the widespread mold infection. In this case series, we have described four cases of aspergillosis. Male preponderance is seen, commonly seen in 4th to 5th decade, 3 out of 4 cases are immunocompromised having diabetes, chronic kidney disease, past history of tuberculosis and only one case was not associated with any comorbid illness. In case 4, the recurrence of polypoidalsinosis itself could be a risk factor causing erosion of nasal mucosa and chronic secretion.The morphological features of intraluminal lesions were of prognostic value. Most of the Aspergillosis patients had a good prognosis with early diagnosis and effective antifungal therapy.It can bedeadly if not diagnosed and treated properly.Very rarely aspergillosis may occur in immunocompetent individuals, which urged us to point outthese cases. With studies suggesting surging incidence and mortality rates, early diagnosis and treatment are paramount to upgrade patient survival.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.