Implementation of ACR and AVR controls for high voltage gain DC-DC Converter

Step up power conversion is universally used in many applications. The application that uses step-up power conversion can be observed in renewable energy such as photovoltaic (PV) system, wind turbine, data center and Electric vehicle. There are many applications which use the DC-DC boost converter to get higher DC voltage from the low input voltage. In this project, Marx topology boost converter (MTBC) analyzed and proposed for conversion from low input dc voltage to high output dc voltage. (MTBC) depends on the principle of the Marx generator. The proposed (MTBC) is multi-stage and consist from 4-stage, by multistage of converter the stress in the components will be reduced, where the parallel charging at input side to reduce the current stress, and series discharging at output to reduce the voltage stress. The stress on the components of the converter will inversely proportional with a number of stages. By implementation of ACR and AVR combination with using PI control technique the output voltage can be controlled. Based on the simulation results the obtained output voltage 400V DC by boosting input voltage 48V DC and by using 4- stage proposed converter, but any drop in the value of input voltage will effect on the output voltage, so that when battery voltage drop to 40V the output will be 340V. After implementing the control system for the AVR and ACR and combine between them, it will be possible to obtain 400V from different value input voltage (40V, 45V, 55V), as well as for 450V and 500V output voltage

Proposing Robust LAD-Atan Penalty of Regression Model Estimation for High Dimensional Data

       لاقت قضية نموذج الانحدار اهتمامًا بالغ الأهمية لاختيار المتغيرات، إذ انه يؤدي دورًا أساسيًا في التعامل مع البيانات ذات  الابعاد العالية. يتم استخدام معكوس الظل الذي يشير إليه  دالة جزاء Atan في كل من التقدير والاختيار المتغير كطريقة فعالة. ومع ذلك ، فإن دالة الجزاء  Atan حساسة جدًا للقيم الشاذة لمتغيرات الاستجابة أو توزيع ملتوي للأخطاء أو توزيع ذو ذيل ثقيل. بينما  : LAD هي وسيلة جيدة للحصول على حصانة تقدير الانحدار. ان الهدف الاساس من هذا البحث هو اقتراح مُقدّر Atan يجمع بين هاتين الفكرتين في آن واحد. لقد اظهرت تجارب المحاكاة وتطبيق البيانات الحقيقية أن مقدّر LAD-Atan المقترح هو الافضل مقارنة بالمقدرات الاخرى.           The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator has superior performance compared with other estimators. &nbsp

The Impact of Dumping Policy on the Food Gap of Chicken Meat in Iraq For the Period (2004-2019) - Turkish Imports Of Chicken Meat a Case Study

Purpose: As a result of the sudden and ill-considered trade openness of Iraq after 2003 to the countries of the world in general and the neighboring countries in particular, and in the absence of the necessary support for the national productive forces and the lack of effective standardization and quality control devices, this led to the exposure of most local products, especially agricultural ones, to decline and inability On the competition and thus dumping the Iraqi market, especially the agricultural products, with imported products, this study came to find out the effect that dumping has on the local production of chicken meat and the impact of that impact on the size of the food gap, and whether the results of the practical study will be in accordance with the logic of economic theory.   Theoretical framework: The concept of dumping, its causes and the difference between it and competition, the WTO position of dumping, and the concept of the food gap were also discussed.   Design/methodology/approach: The data was collected from its official sources, and then the mathematical equations were developed according to economic theory and logic, and using the (EViews) program, the final results were obtained, which were compatible with the logic of economic theory.   Findings: The results of the study proved that when the dumping of chicken meat increased by (1%), the food gap of chicken meat increased by (5.1%).   Research, Practical & Social implications: This study contributes to proving the negative impact of dumping resulting from commercial exposure or unstudied commercial openness, and thus its impact on the food security of the Iraqi community. Originality/value: This research is the first of its kind in the field of agricultural economics, as it links the food gap and dumping policy in a theoretical and practical way

Towards an LTE hybrid unicast broadcast content delivery framework

The era of ubiquitous access to a rich selection of interactive and high quality multimedia has begun; with it, significant challenges in data demand have been placed on mobile network technologies. Content creators and broadcasters alike have embraced the additional capabilities offered by network delivery; diversifying content offerings and providing viewers with far greater choice. Mobile broadcast services introduced as part of the Long Term Evolution (LTE) standard, that are to be further enhanced with the release of 5G, do aid in spectrally efficient delivery of popular live multimedia to many mobile devices, but, ultimately rely on all users expressing interest in the same single stream. The research presented herein explores the development of a standards aligned, multi-stream aware framework; allowing mobile network operators the efficiency gains of broadcast whilst continuing to offer personalised experiences to subscribers. An open source, system level simulation platform is extended to support broadcast, characterised and validated. This is followed by the implementation of a Hybrid Unicast Broadcast Synchronisation (HUBS) framework able to dynamically vary broadcast resource allocation. The HUBS framework is then further expanded to make use of scalable video content

Collaborative Filtering Based Recommendation System: A survey

Abstract—the most common technique used for recommendations is collaborative filtering. Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships from a group of user who share the same preferences and taste. In this paper we have explored various aspects of collaborative filtering recommendation system. We have categorized collaborative filtering recommendation system and shown how the similarity is computed. The desired criteria for selection of data set are also listed. The measures used for evaluating the performance of collaborative filtering recommendation system are discussed along with the challenges faced by the recommendation system. Types of rating that can be collected from the user to rate items are also discussed along with the uses of collaborative filtering recommendation system

Adaptive subframe allocation for next generation multimedia delivery over hybrid LTE unicast broadcast

The continued global roll-out of long term evolution (LTE) networks is providing mobile users with perpetually increasing ubiquitous access to a rich selection of high quality multimedia. Interactive viewing experiences including 3-D or free-viewpoint video require the synchronous delivery of multiple video streams. This paper presents a novel hybrid unicast broadcast synchronisation (HUBS) framework to synchronously deliver multi-stream content. Previous techniques on hybrid LTE implementations include staggered modulation and coding scheme grouping, adaptive modulation coding or implementing error recover techniques; the work presented here instead focuses on dynamic allocation of resources between unicast and broadcast, improving stream synchronisation as well as overall cell resource usage. Furthermore, the HUBS framework has been developed to work within the limitations imposed by the LTE specification. Performance evaluation of the framework is performed through the simulation of probable future scenarios, where a popular live event is broadcast with stereo 3-D or multi-angle companion views interactively offered to capable users. The proposed framework forms a ``HUBS group'' that monitors the radio bearer queues to establish a time lead or lag between broadcast and unicast streams. Since unicast and broadcast share the same radio resources, the number of subframes allocated to the broadcast transmission are then dynamically increased or decreased to minimise the average lead/lag time offset between the streams. Dynamic allocation showed improvements for all services across the cell, whilst keeping streams synchronised despite increased user loading

Induction of Photodegradation of Poly(vinyl alcohol) Using a Cobalt Binary Mixed Ligand Complex

The present study included optical degradation of pure polyvinyl films and also when mixing the complex [Aqua diphylcarbazide thiourea Cobalt (||)]chloride by weight (0.1% - 0.00625 %) and polymer films dissolved in water and thickness (60±5) microns. The optical degradation of the prepared chips was followed up with the presence and absence of the added complex by violet-visible spectroscopy (U.V) using light with a maximum wavelength (356nm) and at different irradiation times (0,10,20,40,80) hours by calculating the constant rate of photolysis of the polymer additive. Using (FT. IR) by increasing the absorption coefficient of the hydroxyl group (IOH). It was found that an increase in the concentration of the additive complex leads to an increase in the photolysis of polymer sheets compared to sheets without it, which in turn leads to an increase in the values of hydroxyl absorption coefficients. These results are consistent with the value of the speed constant of optical degradation the highest value of the dissociation constant (KD) and follow-up by changing the viscosity to weight ratio, degree of degradation and rate of chain cutting

Preparation and Characterization of Fluorone Ligand Complexes for Co+2, Mn+2, and Fe+2 Metallic Ions

In this study, new complexes are produced by bonding of transition metal dinuclear chlorides (CoCl2.6H2O, MnCl2.4H2O, and FeCl2.6H2O) with a polychelated ligand called 9-Phenyl-2,3,7-trihydroxy-6-fluorone, represented by (L), in a 1:2 (metal: ligand) ratio. Applying the appropriate chemical and physical methods, such as melting points, molar electrical conductivity, magnetic sensitivity, FT-IR, UV-Visible, and mass spectrometric studies, the produced complexes were characterized. All complexes are non-electrolytic, according to analyzes of molar electrical conductivity. All complexes have an octahedral structure, according to studies of magnetic sensitivity and Uv-Visible spectra. The mass spectrometry measurements showed the presence of the molar mass bands of the complex and the appearance of other bands resulting from the fragmentation of the complexes. For the metallic complexes spectra, FT-IR revealed a shift in the shape and intensity of the complexes bands at lower frequencies. The bonding of the metal to the oxygen atom can be observed by this shift in the bond stretch of the ligands. The size of the coordination between the ligand and the ions of the metallic elements is the cause. The measurement of the complexes' melting point is high, which suggests that the resultant complexes are quite stable

Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm: MemeWSN

In wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new-fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state-of-the-art methods like MobAC (Singh and Lohani, 2019), EPSO-C (Pathak, 2020), and PBC-CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime

Radio frequency traffic classification over WLAN

Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types