/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

We present a \MF{terahertz} quantum cascade laser operating on a thermoelectric cooler up to a record-high temperature of 210.5 K. The active region design is based on only two quantum wells and achieves high temperature operation thanks to a systematic optimization by means of a nonequilibrium Green's function model. Laser spectra were measured with a room temperature detector, making the whole setup cryogenic free. At low temperatures ($\sim 40 K), a maximum output power of 200 mW was measured.

/pdf/thermoelectrically-cooled-thz-quantum-cascade-laser-2qfzpg86gy.pdf

Thermoelectrically cooled THz quantum cascade laser operating up to 210 K.

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

https://www.mdpi.com/2079-9292/10/12/1388/pdf

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Do we really need deep CNN for plant diseases identification

Fog computing (FC) is used to reduce the energy consumption and latency for the heterogeneous communication approaches in the smart cities’ applications of the Internet of Everything (IoE). Fog computing nodes are connected through wired or wireless medium. The goal of smart city applications is to develop the transaction relationship of real-time response applications. There are various frameworks in real-world to support the IoE in smart-cities but they face the issues like security, platform Independence, multi-application assistance, and resource management. This article is motivated from the Blockchain and Fog computing technologies and presents a secured architecture Blockchain and Fog-based Architecture Network (BFAN) for IoE applications in the smart cities. The proposed architecture secures sensitive data with encryption, authentication, and Blockchain. It assists the System-developers and Architects to deploy the applications in smart city paradigm. The goal of the proposed architecture is to reduce the latency and energy, and ensure improved security features through Blockchain technology. The simulation results demonstrate that the proposed architecture performs better than the existing frameworks for smart-cities.

https://www.mdpi.com/1999-5903/12/4/61/pdf

Blockchain and Fog Based Architecture for Internet of Everything in Smart Cities

The fog computing models are getting popular as the demand and capacity of data processing is rising for the various applications every year. The fog computing models incorporate the various task scheduling algorithms for the resource selection among the given list of virtual machines (VMs). The task scheduling models are designed around the various task metrics, which include the task length (time), energy, processing cost etc. for the various purposes. The cost oriented scheduling models are primarily built for the customer's perspectives, and saves them a handful amount of money by efficiently assigning the resources for the tasks. In this paper, we have worked upon the multiple task scheduling models based upon the Local Regression (LR), Inter Quartile Range (IQR), Local Regression Robust (LRR), Non-Power Aware (NPA), Median Absolute Deviation (MAD), Dynamic Voltage and Frequency Scheduling (DVFS) and The Static Threshold (THR) methods using the ifogsim simulation designed with the 50 nodes and 50 virtual machines, i.e. 1 virtual machine per node. All of the models have been implemented using the standard input simulation parameters for the purpose of performance assessment in the various domains, specifically in the time domain and effective consumption of energy. The results obtained from the experiments have shown the overall time of 86,400 seconds during the simulation, where the DVFS has been recorded with the 52.98 kWh consumption of energy, which shows the efficient processing in comparison to the 150.68 kWh of energy consumption in the NPA model. Also, there are no SLA violations recorded during both of the simulation, because no VM migration model has been utilized among both of the implemented models, which clearly shows that the VM migrations are the major cause of SLA violation cases. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs).

/pdf/dynamic-task-scheduling-using-balanced-vm-allocation-policy-167wr06py3.pdf

Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms

The plant disease detection is the technique which can detect disease from the plant leaves. The plant disease detection has various steps which are textural feature analysis, segmentation, and classification. This research paper is based on the plant disease detection using the KNN classifier with GLCM algorithm. In the proposed method, the image is taken as input which is preprocessed, GLCM algorithm is applied for the textural feature analysis, k-means clustering is applied for the region-based segmentation, and KNN classifier is applied for the disease prediction. The proposed technique is implemented in MATLAB and simulation results show up to 97% accuracy.

Plant Disease Detection Based on Region-Based Segmentation and KNN Classifier

Just after the COVID-19 pandemic, a national lockout is initially enforced in India from March 24 to April 14, 2020, for three weeks and prolonged until May 31, 2020. Because of the mandatory restrictions, emission levels across the country decelerated dramatically during the first very few days. In this paper, the air quality index (AQI) of Ludhiana city, Punjab, has been investigated during the pre- and post-lockdown period. The air quality data of pollutant parameters (PM10, PM2.5, SO2, NO2, CO, O3, and NH3) has been collected from authorized Punjab Agriculture University (PAU), Ludhiana monitoring station of National Air Quality Index (NAQI). A descriptive statistics analysis was applied to check the acceptance level of six significant parameters, including AQI. The sample data was then analyzed using correlation and paired sample t-test to find out the significant level of each parameter under consideration. The results showed that air quality has significantly improved during the lockdown. The paper has suggested adopting such forced periodic alternatives soon to clean the environment.

Analysis of Air Quality Index During Lockdown: A Case of Ludhiana District-Punjab

An expected 70% of Indian economy relies upon agribusiness. Since there is developing Indian population, which is increasingly dependent on the agricultural yield, generation of the harvests must be improved. The end goal is kept in mind to develop progressively the diseases need to be examined in earlier. Diseases are investigated utilizing different image processing techniques the image processing is the technique which process the digital information stored in the form of images. The plant disease detection is the technique which detects disease from the input images. The plant disease detection consists of three steps: initially the image that is fed to input terminal is preprocessed, thereafter features of the image is analyzed according to their features segmentation is applied and in the last step image is classified using any of the classifier. In this paper, various techniques of plant disease detection is reviewed and discussed in terms of various parameters.

Harpreet Kaur

Papers

Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms

Plant Disease Detection Based on Region-Based Segmentation and KNN Classifier

Analysis of Air Quality Index During Lockdown: A Case of Ludhiana District-Punjab

A review on: Various techniques of plant leaf disease detection

A review on smart grid telecommunication system