Assessing Water Consumption of Major Crops in the Command Area of Malwah Distributary, Shaheed Benazirabad, Sindh.

Soil and water are vital natural resources on which agriculture sector growth and village livelihood depend and having the proper knowledge of the Soil, Plant, and water relationship are extremely important to achieve sustainable agricultural productivity. Pakistan has entered the 21st century with the rising challenge to meet food and fiber requirements for its population for domestic consumption and export. Without having appropriate knowledge about the intense water need of plants, most of the agricultural land in Pakistan is still being irrigated by conventional methods, which in turn produces so many problems and reduces the agricultural productivity putting extra stress on the country’s economy, so to avoid these issues, it is extremely necessary to provide the required quantity of water to plant, which will only be possible by consideration and accurate estimation of Evapotranspiration of plant so to enhance awareness and practice of water-saving agriculture in Pakistan to increase the agricultural commodities. In this study, estimation of Actual Evapotranspiration ( ETa ) of Malwah Distributary located in Shaheed Benazirbad, Sindh was selected from Command area of Rohri Canal, ET of four different crops; Cotton, Fallow, Rice and Sugarcane for the period of Rabi 2019-2020 and Kharif 2020 was estimated by using satellite-based evapotranspiration mapping tool namely METRIC REFLUX. The actual ET for each season was obtained using the Reference ET fraction (ETrf) of satellite data and reference ET(ETr) obtained from the literature. The classified crop mask was obtained using maximum likelihood classification on bands 8,4, and 3 of sentinel-2 images of the year 2020. The overall accuracy obtained is 93% with a kappa coefficient 0.921841. The average Actual Evapotranspiration of different crops namely, banana, cotton, rice, and sugarcane were found to be 1527.2 mm, 536.6 mm, 386.80 mm, and 814.02 m

A Symmetric RZ-DPSK Based Colorless NG-PON using Optical Carrier Suppression Scheme

In this paper a simultaneous transmission of a 10 Gbps RZ-DPSK data signal for downstream as well as for upstream is proposed and successfully simulated. An OCS (Optical Carrier Suppression) scheme for generation of second order dual side-band optical carrier is utilized by quadrupling a 10 GHz clockfrequency with a 10 GHz LN-MZM (Lithium-Niobate Mach-Zehnder-Modulator). The upper side second order band is used to generate a RZ-DPSK (Return to Zero-Differential Phase Shift Keying) data signal at the OLT (Optical Line Terminal). At the receiving ONU (Optical Network Unit) 50 km away from the OLT the unmodulated lower side second order band coupled with the downlink transmitted signal is utilized for the uplink modulation of 10 Gbps data in RZ-DPSK format. The simulation results show that the performance of the single-tone RZ-DPSK data modulation format is a suitable choice for the WDMPON (Wavelength Division Multiplexing-Passive Optical Network) link with a transmission span of 50 km. The proposed architecture eliminates the need of any pulse carver and mid-span power amplifiers along with the requirement of any power splitting device used in the ONU for colorless uplink transmission. In this scheme, high data rate transmission over long distance is achieved. This scheme merges the boundaries of local access networks and MAN (Metropolitan Area Networks). The proposed scheme is a highly robust, cost effective, backward compatible as well as future proof WDM-PON architecture

FEM based Approximations for the TV Denoising Optimization Problem

FEM (Finite Element Method) based approximation model is proposed in this paper. Our goal is to solve a TV (Total Variation) denoising optimization problem using finite element method with triangular grid as a computational domain and also to study the local choice of smoothness parameters for the given problem. We provide some confidence measures in the form of average absolute error and average absolute square error corresponding to the different choices of locally selected regularization weights; moreover we show that how the regularization parameters play a crucial part in the reduction of error in the approximations and the diffusion enhancement specifically for this TV model

Convolutional Code Based PAPR Reduction Scheme for Multicarrier Transmission with Higher Number of Subcarriers

International audienceMulticarrier transmission technique has become a prominent transmission technique in high-speed wireless communication systems. It is due to its frequency diversity,small inter-symbol interference in the multipath fading channel, simple equalizer structure, and high bandwidth efficiency. Nevertheless, in thetime domain, multicarrier transmission signal has high PAPR (Peak-to-Average Power Ratio) thatinterprets to low power amplifier efficiencies. To decrease the PAPR, a CCSLM (Convolutional Code Selective Mapping) scheme for multicarrier transmission with a high number of subcarriers is proposed in this paper. Proposed scheme is based on SLM method and employs interleaver and convolutional coding. Related works on the PAPR reduction have considered either 128 or 256 number of subcarriers. However, PAPR of multicarrier transmission signal will increase as a number of subcarriers increases. The proposed method achieves significant PAPR reduction for ahigher number of subcarriers as well as better power amplifier efficiency. Simulation outcomes validate the usefulness of projected scheme

Application and Analysis of Performance of DQPSK Advanced Modulation Format in Spectral Amplitude Coding OCDMA

SAC (Spectral Amplitude Coding) is a technique of OCDMA (Optical Code Division Multiple Access) to encode and decode data bits by utilizing spectral components of the broadband source. Usually OOK (ON-Off-Keying) modulation format is used in this encoding scheme. To make SAC OCDMA network spectrally efficient, advanced modulation format of DQPSK (Differential Quaternary Phase Shift Keying) is applied, simulated and analyzed. m-sequence code is encoded in the simulated setup. Performance regarding various lengths of m-sequence code is also analyzed and displayed in the pictorial form. The results of the simulation are evaluated with the help of electrical constellation diagram, eye diagram and bit error rate graph. All the graphs indicate better transmission quality in case of advanced modulation format of DQPSK used in SAC OCDMA network as compared with OO

SNR and BER Models and the Simulation for BER Performance of Selected Spectral Amplitude Codes for OCDMA

Many encoding schemes are used in OCDMA (Optical Code Division Multiple Access Network) but SAC (Spectral Amplitude Codes) is widely used. It is considered an effective arrangement to eliminate dominant noise called MAI (Multi Access Interference). Various codes are studied for evaluation with respect to their performance against three noises namely shot noise, thermal noise and PIIN (Phase Induced Intensity Noise). Various Mathematical models for SNR (Signal to Noise Ratios) and BER (Bit Error Rates) are discussed where the SNRs are calculated and BERs are computed using Gaussian distribution assumption. After analyzing the results mathematically, it is concluded that ZCC (Zero Cross Correlation Code) performs better than the other selected SAC codes and can serve larger number of active users than the other codes do. At various receiver power levels, analysis points out that RDC (Random Diagonal Code) also performs better than the other codes. For the power interval between -10 and -20 dBm performance of RDC is better ZCC. Their lowest BER values suggest that these codes should be part of an efficient and cost effective OCDM access network in the future

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

The deterioration of railway wheel tread causes unexpected breakdowns with increasing risk of operational failure leading to higher maintenance costs. The timely detection of wheel faults, such as wheel flats and false flanges, leading to varying conicity levels, helps network operators schedule maintenance before a fault occurs in reality. This study proposes a multiple model-based novel technique for the detection of railway wheelset conicity. The proposed idea is based on an indirect method to identify the actual conicity condition by analyzing the lateral acceleration of the wheelset. It in fact incorporates a combination of multiple Kalman filters, tuned on a particular conicity level, and a fuzzy logic identification system. The difference between the actual conicity and its estimated version from the filters is calculated, which provides the foundation for further processing. After preprocessing the residuals, a fuzzy inference system is used that identifies the actual conicity of the wheelset by assessing the normalized rms values from the residuals of each filter. The proposed idea was validated by simulation studies to endorse its efficacy

Analyzing ML-Based IDS over Real-Traffic

The rapid growth of computer networks has caused a significant increase in malicious traffic, promoting the use of Intrusion Detection Systems (IDSs) to protect against this ever-growing attack traffic. A great number of IDS have been developed with some sort of weaknesses and strengths. Most of the development and research of IDS is purely based on simulated and non-updated datasets due to the unavailability of real datasets, for instance, KDD '99, and CIC-IDS-18 which are widely used datasets by researchers are not sufficient to represent real-traffic scenarios. Moreover, these one-time generated static datasets cannot survive the rapid changes in network patterns. To overcome these problems, we have proposed a framework to generate a full feature, unbiased, real-traffic-based, updated custom dataset to deal with the limitations of existing datasets. In this paper, the complete methodology of network testbed, data acquisition and attack scenarios are discussed. The generated dataset contains more than 70 features and covers different types of attacks, namely DoS, DDoS, Portscan, Brute-Force and Web attacks. Later, the custom-generated dataset is compared to various available datasets based on seven different factors, such as updates, practical-to-generate, realness, attack diversity, flexibility, availability, and interoperability. Additionally, we have trained different ML-based classifiers on our custom-generated dataset and then tested/analyzed it based on performance metrics. The generated dataset is publicly available and accessible by all users.  Moreover, the following research is anticipated to allow researchers to develop effective IDSs and real traffic-based updated datasets

Analyzing ML-Based IDS over Real-Traffic

The rapid growth of computer networks has caused a significant increase in malicious traffic, promoting the use of Intrusion Detection Systems (IDSs) to protect against this ever-growing attack traffic. A great number of IDS have been developed with some sort of weaknesses and strengths. Most of the development and research of IDS is purely based on simulated and non-updated datasets due to the unavailability of real datasets, for instance, KDD '99, and CIC-IDS-18 which are widely used datasets by researchers are not sufficient to represent real-traffic scenarios. Moreover, these one-time generated static datasets cannot survive the rapid changes in network patterns. To overcome these problems, we have proposed a framework to generate a full feature, unbiased, real-traffic-based, updated custom dataset to deal with the limitations of existing datasets. In this paper, the complete methodology of network testbed, data acquisition and attack scenarios are discussed. The generated dataset contains more than 70 features and covers different types of attacks, namely DoS, DDoS, Portscan, Brute-Force and Web attacks. Later, the custom-generated dataset is compared to various available datasets based on seven different factors, such as updates, practical-to-generate, realness, attack diversity, flexibility, availability, and interoperability. Additionally, we have trained different ML-based classifiers on our custom-generated dataset and then tested/analyzed it based on performance metrics. The generated dataset is publicly available and accessible by all users.  Moreover, the following research is anticipated to allow researchers to develop effective IDSs and real traffic-based updated datasets