Cargo Management System – codeshoppy

Cargo Manager requires user entry of container and product dimensions, together with information on weight and orientation constraints. In-built database facilities for up to 50 containers and products are provided so that input into these screens can be carried out with minimal effort. If information for a particular product code is already held in the 4000 product database then this is automatically entered into the appropriate fields.

Cargo Management System The first of these will attempt to pack as much of the cargo using any of the packing methods available to Cargo Manager. This may be a loading from the floor or from the end of the container. The second and third options are self explanatory and the one most appropriate to the practical circumstances should be selected.

Existing System

The cargo management system is an application that will help in maintaining the cargo trading either through ship or airline or locally. This application can be used by the company to know about the cargo quantity that is managed within its warehouse. This application can drastically reduce the pen paper work as it can be automated. It can also help in storing the information in a more easy way. The data can be stored easily through this application. The user interface will be simple and user friendly. This will be one of the interesting applications that one can implement in real time world.Asp Dotne C# Project Ideas Titles 2020

DISADVANTAGE

Shipment dimensions cannot be given accurately at the time the booking request is made. Often the actual dimensions received at tender do not anyway become part of any permanent record.

Since flights will reach their volume limitations before they hit weight limits on some sectors, some heuristic calculation, such as a density code, is needed to forecast likely volumes.

In this data to gauge the total market demand regardless of the airline’s available capacity. This unconstrained demand is set for each product type by origin and destination (O&D), day of week, time of day.

Proposed System

The fundamental building blocks to cargo revenue maximization begin with sufficient, reliable and accessible data. Full air waybill capture is a good starting point. The waybill history should be stored in an accessible format in a database that can be updated daily.  The real challenge is not just to capture the complete life cycle of a shipment from original booking through to invoicing, but to do so in a dynamic and timely way. With new developments in data handling and communications, it is now possible to build a data warehouse that receives seamless, real time updates from reservation systems and with minimal changes to existing systems.

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Music Course Management System

RF power measuring tool forAndroid OS platform

Smartphones and 3G tablets are becoming a major platform for execution of Internet services as more powerful and less expensive devices are becoming available. From this perspective, a large number of mobile applications are becoming available for end users and corporates [1]. In comparison with home users, corporates require a more stable and reliable network especially for wireless communications. Wireless service providers need to monitor their networks not only for received signal level but also for quality of service (QoS) [2]. Frequently the quality of service is integrated withsubjective information and reported as Quality of Experience (QoE) to the end users [3, 4]. Both metrics, QoS and QoE require automated and expensive monitoring tools. As smartphones’ analyzing power is becoming comparable to personal computers, the possibility of using their capabilitiesfor live network monitoring lays ahead an interesting research field [5]. In this work, we present a mobile application exploiting measuring capabilities of modern Android-based smartphones. The developed application makes use of Android Programming Interfaces (API) to extrapolate interesting parameters from theuser connected network such as received power and network responding time (latency). We use the applications as live monitoring tool in more critical situations such as in the case of fast moving users. A post-processing of the monitored data isprovided via a PHP scripting language and is integrated with Google maps, using their respective programming interfaces. These post-processing results in three new layers; one made of signal quality, the next one of network latency and the last one of user velocity for the surveyed area overimposed to the geographical map. The provided information can be useful for service providers to improve their quality of service for end users. This can be in context, as “you cannot improve what you cannot measure”. The paper is organized as follows: in the first paragraph, we provide some technical equations and concepts of signal-received power, followed by the second section where the extrapolated parameters are presented together with the application interface. In the third paragraph, measured data are provided and the results are commented and analyzed. At the end some conclusions are drawn. II.RF RECEIVED POWER ON MOBILE USER EQUIPMENTIn wireless communication, high data rate is proportionally related to the level of Signal to Noise Ratio of the received signal. Received power level in far field region [6, 7] is inversely proportional to the square of the distance between transmitting and receiving antenna. In GSM/UMTS network, the distance from the user mobile station (MS) and base station(BS) is to be considered as far field for practical cases. In this context, let the received power PRX be expressed in dBm: 20log4λπ++ +=RXTXTXRXPG GdP(1) where GRX and GTX are the gains of the receive and transmit antennas, respectively, λ is the wavelength, d is the relative distance from transmitting and receiving antenna and PTX is the transmit power in dBm. From (1) we see that the received power at a fixed distance d cannot be improve without changing the antennas gain or transmitted power. In (1) only free space loss are considered in respect to other factors as multipath propagation (fading) or propagation loss on differentmedia inserted in optical path from BS and MS. Practically, transmitted power and transmitting antenna gain are the same at any measuring time. The receiving antenna in mobile station is assumed omnidirectional, so its orientation does not influence the measuring procedure. This assumption is coherent with ETSI (European Telecommunications Standards Institute) GSM Technical Specification [8, 9] where equipment with integral antenna may be taken into account assuming a 0 dBi gain antenna. Therefore, at a fixed distance from transmitting antenna the received power must be constant. Inpractical cases, this is not true due to other loss factors as multipath propagation and atmospheric loss. Monitoring received power at a fixed distance from transmitting station will provide information’s of the other loss factors that need to be taken into account for offering high quality of service to end users. In this section, the requirements are given in terms of power levels at the antenna connector of the receiver. This means that the tests on equipment on integral antenna will consider fields strengths (E) related to the power levels (P) specified, by the following formula [8]. ()()()dB V/m dBm 20log77.2MHzfEPμ=++(2) Using smartphones as live probe is possible since normally the received power is automatically evaluated by the device forcommunication capabilities. From a programming point of view, the signal network strength is provided in Android platform as ASU (Arbitrary Strength Unit) levels. The ASU level is an integer in the [0, 31] range (5 bit discretization)directly related to the Received Signal Strength Indicator-RSSIfor GSM network (2G). For UMTS (3G) the same android API reports the level index of CPICH-RSCP (Common Pilot Channel – Received Signal Code Power) defined in TS 25.125. In the UMTS cellular communication system, received signal code power (RSCP) denotes the power measured by a receiver on a particular physical communication channel. It is used as an indication of signal strength. Reporting this information inthe more common measuring unit (dBm) as indicated by ETSI [8] the following formula is used. dBm2113dBmPASU=×−(3) The approximation of 0dBi receiving antenna gain is allowed as the error in equation (1) is smaller than resolutionobtained by 5 bit resolution used in (3) of only 2dBm. III.SMARTPHONES WITH ANDROID OS AS MOBILE PROBEThe modern smartphone with an Android operating system (OS) is meant to be a tool for monitoring parameters and network traffic in order to characterize the behavior of cellular communications [10, 11]. The information retrieved can be logged and exported for further post processing analysis.

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Positioning information •GPS info (latitude, longitude and elevation) •User velocity •Moving direction •Precision on monitored data Network information & operator •Network technology (UMTS, HSDPA) •GSM Cell ID •Signal strength (dBm) •Latency or RTT (Round Trip Time) as Max, Min and median value. •Packet loss Regarding the use of this application, a simpler GUI is provided which flow chart is reported in Figure 1. At the startof the application in an Android compatible device (Android OS 4.2 or higher is required) the application will check if thehosting device fulfills all the requirements for loading the signal monitoring tool. At the compatibility control step, the application controls the operating system version, GPS device presence and if it is enabled or not. The application also controls the 3G network activity and connection. If this compatibility control passes successfully, a main window is presented (Figure 2) otherwise an advising window is shown. In the main interface an online network address needs to be provided; the protocol used for measuring purposes should be provided as well. The choice can be between HTTP (Hypertext Transfer Protocol) and ICMP. The preferred protocol is ICMP but in case the provider’s policies result in disabled ICMP protocol, HTTP can be used. At the same interface we need to provide the total sampling points in [1, 1000] range and the sampling time seen as time difference from two consecutive measurements in the [10, 200] range expressed in seconds. Smaller sampling time can be used for fast moving users and bigger values for slow moving users.

https://codeshoppy.com/android-app-ideas-for-students-college-project.html

Iot Based Smart Supermarket

Proposed work depicts the working of the RFID detection system attached to trolleys. Android application for online processing and cash payment is developed. Which is presented in this section.

The proposed system is as shown in figure 3.1. This IOT based Trolley has the application such as Automatic billing at shopping mall and helps to owners. The Sequence of operations is indicated with dotted line and sequence number. The sequence number details are as follows. Supermarket Billing System Mobile Application

1 Power supply:

First initialize the power of the module using battery i.e. 12v, then it is ready to use for the customer.

2 RFID Reader:

Radio frequency identification is unique for every product. It can read 40 tags and it does not require line of sight to read the products. As soon as the product falls in the trolley the RFID reader reads the RFID tag placed on the product. RFID reader is connected to the Pic microcontroller.

3 PIC Microcontroller:

We are using Pic 1846k22 microcontroller. It contains 2 UART and easily we can write the programme. It verifies the information got from the Rfid reader and information in the memory of the microcontroller. Microcontroller is already installed on the cart for data processing and it is connected to Wi-Fi module to communicate with server.

4 LCD Display:

We are using 16×2 LCD display. If the information matches as in the microcontroller then the cost, name and quantity of the product will be displayed on the LCD.

5 Wi-Fi ESP8266:

Wi-Fi ESP8266 is access point that allows the station or client to connect to any router. LCD trolley is provided with Wi-Fi. Here we are creating new website of shopping mall which can transfer the products information to the main server. In order to communicate server and cart we have chosen esp8266 Wi-Fi technology. It is low power and in expensive.

Using android studio, we can develop the application. Using Wi-Fi, a customer can install the application. For a new user, it asks registration details. If a customer has already a member of this application it asks login details. After login, the home page is displayed. In that, we have product information. By searching the products, customer can buy a product. Customer purchased products information is stored in cloud via Wi-Fi module. As soon as customer login to the app, cloud is sent whole product information like number of items, name, price and total amount will be displayed on cart which is present in the app. It asks for the paymentoption, one is cash payment and another one is online payment.

The proposed model consumes less time, low cost and can easily be used by the customer and owner. It does not require any training. RFID helps us to detect the products easily compared to the barcode. Billing is done by automatic in an inventory updated manner. The requirement of manpower will be reduced. Simultaneously we can serve more number of customers https://codeshoppy.com/

Exam Paper Generator Application

Due to the growing field of education, conducting exams and preparing appropriate papers for the same is proving difficult, inefficient, time consuming and a redundant job for the instructor. Therefore, many applications, software and databases have immerged to combat the situation. Our team has looked into such various applications beforehand, they include the following. This is one of the more popular systems that has immerged which intakes the question bank and a criteria and gives the output of a single question paper. [1] It includes fuzzy logic system for creation of an examination paper.[4] Yet, falls behind in real-time and practical generation and is limited to desktop use. https://codeshoppy.com/ AlkaLeekha, etal [2] also worked on the automated question paper. This systems too intakes the question bank with other important features like error watching and non-repetition but it is available only in desktop format and can intake only a certain limit of questions at one instance and deals with lower complexities. SurbhiChoudhary [3] etal also works on the system which provides a big database and the option to select difficulty. But, it falls behind in portability and complexity and lack of offline use. P.H. Potgieter [5] worked to find out the computerized system to evaluate computerized question paper which is one step ahead. Vijay Krishnan Purohit, [6] etal also performed to generate and manage the system on the similar line.

Conventional exam paper generation systems followed by institutes have many drawbacks and weaknesses such as time requirements and repetition of questions in the paper. To overcome them we have designed the proposed system.

Following are points we have considered to implement in our E-PAGE: 1.Automated Question Paper Generation 2.Flexibility in management of questions 3.Security for Administrator 4.Good selection of questions 5.Formatted Question bank entry

There are 3 modules in the application: A.Administrator Module B.Instructor Module C.User Module

A.Administrator Module The Admin is the selected senior staff from the institute who are responsible to manage the questions with functions such as addition and updating of various parameters such as courses, subjects, chapters and the questions themselves. They have their own specific login details.

In which, Course: – It is the department/field to select as per requirements Semester: – It is the number of semester in the selected course to select as per requirements. Subject: – It is the subject of the selected semester to select as per requirements. Chapter: – It is the chapter of the selected subject to select as per requirements. Marks: – It is the marks of the question to be stated as per requirements. Level: – It is the Bloom’s Taxonomy Level to select as per requirements. These include 3 levels, 1.R which stands for Remembrance. 2.U which stands for Understanding 3.A which stands for Application The actual question text is then added after finding the appropriate fields.

B.Instructor Module An instructor is the staff from the institute responsible for a particular subject. He/she is responsible for the management of the chapters and questions of said subject. An admin can assign instructors for the subject selected accordingly.

C.User Module The user is mainly the student studying in the institute for their own personal study, and the professors searching for a good internal test paper to give the students.

The user can generate a unique question paper by just filling the appropriate information in the form given by the application to search the database and generate a full question paper through the algorithm. View More

An Active Android Application Repacking Detection Approach

student placement system android app source code

Repackaging applications as the main carrier of Android malware have caused huge losses to users. In addition, the third-party application market that Android applications rely on is characterized by missing audits and lax supervision, which further encourages the distribution of repackaged applications. Most of the traditional repackaging detection approaches need to rely on a third-party detection platform to passively determine whether or not the Android application is repackaged, which has a high false negative rate. In order to solve the problem, this paper proposes an active detection approach for Android code repacking. The approach embeds code watermarking with the detection code into the appropriate conditional branch code block by means of dynamic loading to achieve the hidden purpose. Then, the active detection approach compares the consistency of the runtime application signature and the original code watermarking signature to realize the code repackaging recognition. Finally, this work takes eight different types of Android applications from Github on three different mobile phones to verify the validity of the approach. Experimental results show that an Android application containing a self-detecting code watermarking can effectively perform repackaging detection without relying on third parties.

Due to the openness and personalization, the Android operating system has become the most used smartphone operating system in the world, and it also faces severe security issues [1]. Attackers often perform reverse analysis on genuine Android applications, then embed malicious code in applications, and finally repackage them. Therefore, repackaging is the main carrier of Android malware seriously, which affects user security [2]. For example, the attacker makes use of the malicious code to control the user remotely and steals private data such as the user’s short message and address book. There are a large number of third-party application stores on the market, and each Android mobile phone manufacturer installs its own application store in the mobile phone. However, different application stores use different standards to review applications, and even some third-party application markets intentionally tolerate repackaged applications. Due to the lack of audits and strict supervision in the third-party application market, the distribution of repackaged applications is further encouraged, which poses a threat to users’ mobile phones. Therefore, it is important to detect repackaged Android applications to maintain a good market order. The current detection approach is that the application store checks the application to be put on the shelf. The application store rejects it on the shelf if the Android application is determined to be a repackaged application. student placement system android app source code However, the Android system application distribution management is confusing, and some small application stores do not have the ability to scan all the repackaged applications. Even some application stores are inherently malicious. Therefore, this paper proposes an active Android application repacking detection approach. The active detection approach embeds the code watermarking with detection function in the application program, reinforces the application by itself, and resists the attacker’s repackaging attack. The effectiveness of proposed approach is verified by comparing application signatures and code watermarking signatures.

Scholars proposed many ways to identify repackaged Android applications, which mainly focus on static and dynamic analysis. Zhou et al. detect the repackaged applications according to the fingerprint similarity with those applications from the official Android Market [3]. They generate app-specific fingerprint based on a fuzzy hashing technique. To improve detection speed and analyze a much larger application corpus, Hanna et al. proposed a scalable distributed similarity detection infrastructure based on feature hashing [4]. However, the static analysis approaches have a high false positive rate. In order to reduce the false positive rate, researchers began to use dynamic analysis methods to determine the Android code repackaging [5]. Crussell et al. detected cloned Android applications based on program dependence graphs [6]. Wang et al. identified software theft based on application birthmark that represents the unique character of the runtime behavior [7]. Based on an observation that some critical runtime values are hard to be replaced or eliminated, Jhi et al. detected application plagiarism based on runtime values [8]. Similarly, Huang et al. proposed a new mobile application repackaging detection framework to resilience all the obfuscation algorithms based on static analysis of Dalvik bytecode [9]. https://codeshoppy.com/android-project-with-source-code-students.html In addition, Zhou et al. made use of similarity from the visual content to detect Android application repackaging [10]. They dynamically traversed each interface included in the Android application to generate the feature fingerprint of the application and then identified the repacking application according to the similarity of the fingerprint. Zhou et al.identified Android application repackaging combining graph feature vector detection algorithm based on code features and resource picture file similarity algorithm [11]. But, current detection methods are dependent on third-party platforms.