Thematische Bibliographien / MOBILedit

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Auswahl der wissenschaftlichen Literatur zum Thema „MOBILedit“

Autor: Grafiati

Veröffentlicht am 25. Juni 2021

Zuletzt aktualisiert am 5. Februar 2022

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Zeitschriftenartikel zum Thema "MOBILedit"

Bintang, Rauhulloh Noor, Rusydi Umar und Anton Yudhana. „Assess of Forensic Tools on Android Based Facebook Lite with the NIST Method“. Scientific Journal of Informatics 8, Nr. 1 (10.05.2021): 1–9. http://dx.doi.org/10.15294/sji.v8i1.26744.

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The increase in social media use of Facebook lite by using Android-based smartphones is quite high. Activities when communicating through the social media network Facebook Lite Facebook lite can send a text message, image, or Video. Not a few users of Facebook lite social media abusing this app to commit fraud crimes, pornographic acts, or defamation actions from social media users Facebook lite. In such cases, it can be a digital forensic benchmark to get results from digital evidence from the Facebook lite application. In this investigation, National Institute of Standards and Technology NIST research methods with various stages, namely Collection, Examination, Analysis, and Reporting. While the forensic tools to be used are Magnet Axiom Forensic and MOBILedit Forensic Express Pro. Comparison and results of data conducted with forensic tools Magnet Axiom Forensic and MOBILedit Forensic Express Pro in the form of parameter data specified. Axiom Forensic Magnet data is 57.14% while MOBILedit Forensic Express Pro data is 85.71%. This data is the data of the performance results of both forensic tool applications in obtaining digital evidence on Facebook lite application.

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Imam Riadi, Sunardi und Panggah Widiandana. „Investigating Cyberbullying on WhatsApp Using Digital Forensics Research Workshop“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, Nr. 4 (20.08.2020): 730–35. http://dx.doi.org/10.29207/resti.v4i4.2161.

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Cyberbullying in group conversations in one of the instant messaging applications is one of the conflicts that occur due to social media, specifically WhatsApp. This study conducted digital forensics to find evidence of cyberbullying by obtaining work in the Digital Forensic Research Workshop (DFRWS). The evidence was investigated using the MOBILedit Forensic Express tool as an application for evidence submission and the Cosine Similarity method to approve the purchase of cyberbullying cases. This research has been able to conduct procurement to reveal digital evidence on the agreement in the Group's features using text using MOBILedit. Identification using the Cosine method. Similarities have supported actions that lead to cyberbullying with different levels Improved Sqrt-Cosine (ISC) value, the largest 0.05 and the lowest 0.02 based on conversations against requests.

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Imam Riadi, Rusydi Umar und Muhammad Irwan Syahib. „Akuisisi Bukti Digital Viber Messenger Android Menggunakan Metode National Institute of Standards and Technology (NIST)“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, Nr. 1 (14.02.2021): 45–54. http://dx.doi.org/10.29207/resti.v5i1.2626.

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Viber is one of the most popular social media in the Instant Messenger application category that can be used to send text messages, make voice calls, send picture messages and video messages to other users. As many as 260 million people around the world have used this application. Increasing the number of viber users certainly brings positive and negative impacts, one of the negative impacts of this application is the use of digital forensic crime. This research simulates and removes digital crime evidence from the viber application on Android smartphones using the National Institute of Standards Technology (NIST) method, which is a method that has work guidelines on forensic policy and process standards to ensure each investigator follows the workflow the same so that their work is documented and the results can be accounted for. This study uses three forensic tools, MOBILedit Forensic Express, Belkasoft and Autopsy. The results in this study show that MOBILedit Forensic Express gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. Belkasoft gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. For Autopsy does not give the expected results in the extraction process, in other words the Autopsy application gives zero results. It can be concluded that MOBILedit Forensic Express and Belkasoft have a good performance compared to Autopsy and thus this research has been completed and succeeded in accordance with the expected goals.

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Nasirudin, Nasirudin, Sunardi Sunardi und Imam Riadi. „Analisis Forensik Smartphone Android Menggunakan Metode NIST dan Tool MOBILedit Forensic Express“. Jurnal Informatika Universitas Pamulang 5, Nr. 1 (31.03.2020): 89. http://dx.doi.org/10.32493/informatika.v5i1.4578.

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Technological advances are growing rapidly, including mobile device technology, one of which is an Android smartphone that is experiencing rapid progress with a variety of features so that it can spoil its users, with the rapid development of smartphone technology, many users benefit, but many are disadvantaged by the growing smartphone. technology, so that many perpetrators or persons who commit crimes and seek profits with smartphone facilities. Case simulation by securing Samsung Galaxy A8 brand android smartphone evidence using the MOBILedit forensic express forensic tool with the National Institute of Standards and Technology (NIST) method which consists of four stages of collection, examination, analysis and reporting. The results of testing the Samsung Galaxy A8 android smartphone are carried out with the NIST method and the MOBILedit Forensic Express tool obtained by data backup, extraction and analysis so that there are findings sought for investigation and evidence of crimes committed by persons using android smartphone facilities.

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Zuhriyanto, Ikhsan, Anton Yudhana und Imam Riadi. „Comparative analysis of Forensic Tools on Twitter applications using the DFRWS method“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, Nr. 5 (30.10.2020): 829–36. http://dx.doi.org/10.29207/resti.v4i5.2152.

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Current crime is increasing, one of which is the crime of using social media, although no crime does not leave digital evidence. Twitter application is a social media that is widely used by its users. Acts of crime such as fraud, insults, hate speech, and other crimes lately use many social media applications, especially Twitter. This research was conducted to find forensic evidence on the social media Twitter application that is accessed using a smartphone application using the Digital Forensics Research Workshop (DFRWS) method. These digital forensic stages include identification, preservation, collection, examination, analysis, and presentation in finding digital evidence of crime using the MOBILedit Forensic Express software and Belkasoft Evidence Center. Digital evidence sought on smartphones can be found using case scenarios and 16 variables that have been created so that digital proof in the form of smartphone specifications, Twitter accounts, application versions, conversations in the way of messages and status. This study's results indicate that MOBILedit Forensic Express digital forensic software is better with an accuracy rate of 85.75% while Belkasoft Evidence Center is 43.75%.

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Ardiningtias, Syifa Riski Ardiningtias, Sunardi Sunardi und Herman Herman. „INVESTIGASI DIGITAL PADA FACEBOOK MESSENGER MENGGUNAKAN NATIONAL INSTITUTE OF JUSTICE“. Jurnal Informatika Polinema 7, Nr. 4 (31.08.2021): 19–26. http://dx.doi.org/10.33795/jip.v7i4.709.

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Perkembangan teknologi memudahkan masyarakat dalam berbagi informasi dan berkomunikasi. Facebook Messenger merupakan salah satu instant messanger yang memiliki kelebihan multi-platform yang dapat digunakan oleh pengguna dalam pengiriman pesan teks, gambar, pesan suara, dan video. Selain digunakan sebagai hal untuk kegiatan positif, namun fasilitas dalam teknologi ini juga dapat digunakan untuk melakukan kegiatan negatif. Penelitian ini melakukan investigasi forensik pada simulasi adanya tindakan kejahatan dalam penyebaran konten pornografi menggunakan Facebook Messenger sebagai media komunikasi pada smartphone Android. Pelaku berkomunikasi dan mengirimkan konten pornografi berupa percakapan, audio, dan video kepada pengguna dan kemudian menghapusnya dengan tujuan menghilangkan jejak. Namun, setiap tindak kejahatan dapat meninggalkan barang bukti sehingga selama menyelesaikan masalah ini perlu melakukan investigasi forensik digital. Perangkat berupa smartphone yang dapat digunakan selama objek untuk menemukan bukti digital. Pengangkatan barang bukti dalam penelitian ini menggunakan tools forensik MOBILEdit Forensics dan Wondershare Dr. fone dengan menggunakan kerangka kerja National Institute of Justice (NIJ). Penelitian ini dengan hasilnya kemudian dapat digunakan sebagai bukti oleh investigator atau penyidik dalam menangani sebuah kasus kejahatan dengan hasil yang didapatkan berupa versi aplikasi, akun, email, percakapan, waktu kejadian, gambar, audio, dan video. MobilEdit Forensics memiliki kelebihan dalam mendapatkan barang bukti sebesar 85,71% dibanding Wondershare Dr. fone yang hanya mendapatkan barang bukti hanya 28,57%.

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Riadi, Imam, Sunardi Sunardi und Sahiruddin Sahiruddin. „Analisis Forensik Recovery pada Smartphone Android Menggunakan Metode National Institute Of Justice (NIJ)“. Jurnal Rekayasa Teknologi Informasi (JURTI) 3, Nr. 1 (28.06.2019): 87. http://dx.doi.org/10.30872/jurti.v3i1.2292.

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keberadaan smartphone saat ini dianggap sangat membantu aktifitas manusia dalam melakukan pekerjaan sehari- hari. Berkembangnya fitur-fitur yang ada pada smartphone memudahkan para penggunanya beraktifitas seperti melakukan pekerjaan kantor, bisnis, e-banking, dan untuk berinteraksi dengan pengguna lain di media sosial. Perkembangan smartphone tidak hanya memberikan dampak positif tetapi bisa berdampak negatif ketika perkembangan tersebut dimanfaatkan untuk melakukan tindakan kejahatan. Saat ini terdapat banyak kasus penghapusan barang bukti kejahatan yang dilakukan oleh tersangka untuk mengilangkan bukti kejahatan yang dilakukan oleh seorang pelaku. Hal ini menjadi tantangan bagi forensika teknologi informasi dan penegak hukum melakukan penyelidikan secara forensik terhadap smartphone dari tersangka dalam sebuah kasus kejahatan untuk mendapatkan kembali bukti digital yang akan dijadikan sebagai barang bukti dalam sebuah persidangan. Penelitian ini menggunakan tools MOBILedit Forensic, Wondershare dr. Fone for Android, dan Belkasoft Evidence Center untuk memperoleh bukti digital serta menggunakan metode National Institute of Justice (NIJ) yaitu dengan mengidentifikasi, mengusulkan solusi, melakukan uji solusi yang ditawarkan, mengevaluasi dan melaporkan hasil. Dari hasil pengujian tool forensik yang peneliti gunakan, tool MOBILedit Forensic tidak bisa mengembalikan data yang sudah dihapus, tool Wondershare dr. Fone For Android berhasil mengembalikan data kontak, log panggilan,dan pesan yang sudah dihapus, sementara tool Belkasoft Evidence Center hanya bisa mengembalikan data kontak, dan log panggilan yang sudah dihapus.

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Mahendra, Kadek Dwi Oka, und I. Komang Ari Mogi. „Digital Forensic Analysis Of Michat Application On Android As Digital Proof In Handling Online Prostitution Cases“. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, Nr. 3 (18.02.2021): 381. http://dx.doi.org/10.24843/jlk.2021.v09.i03.p09.

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Smartphone technology and the Internet are very popular lately, especially with various features, one of which is instant messaging. But behind it all, instant messaging like MiChat is very vulnerable to becoming a crime, one of which is Online Prostitution. To follow up on Online Prostitution activities, it is necessary to carry out mobile forensics to find evidence which is then given to be given to the authorities. This study uses the MiChat application as an online prostitution media, and uses the National Institute of Justice (NIJ) method which has five basic stages is, preparation, collection, examination, analysis, and reporting. This research uses MOBILedit Forensic Express, and SysTools SQLite Viewer.

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Riadi, Imam, Anton Yudhana und Mushab Al Barra. „Forensik Mobile pada Layanan Media Sosial LinkedIn“. JISKA (Jurnal Informatika Sunan Kalijaga) 6, Nr. 1 (20.01.2021): 9–20. http://dx.doi.org/10.14421/jiska.2021.61-02.

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The research explores mobile forensic on LinkedIn social media. Forensic mobile finds digital evidence of job hoax cases in LinkedIn, investigation using the NIST (National Institute of Standard and Technology) method. Data collection techniques using Andriller tools in investigations. Data examination using tools Root Browser, Autopsy in the forensic process. data analysis using tools MOBILedit in the forensic process. The investigation found digital evidence of log activity, a status update on LinkedIn. Other results found in the investigation are 17 WiFi password, 117 download history, 263 phone calls, 1 file deleted, 1 file hidden, and 1 file raised, the research has reached the expected target.

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Pawitradi, Gede, und I. Ketut Gede Suhartana. „Acquisition of LINE Digital Social Media Evidence Using the National Institute of Justice (NIJ) Method“. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, Nr. 2 (08.01.2020): 129. http://dx.doi.org/10.24843/jlk.2019.v08.i02.p04.

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Nowadays the use of social media has developed very rapidly over time. With very easy to use and also higher security than ordinary messaging services, making one of the factors of social media is very often used in today's world. But behind it all, social media such as LINE is very vulnerable to become one of the crime facilities, one of which is cyberbullying. To follow up on the cyberbullying activity, a forensic cellphone needs to be carried out to find evidence which is then useful to send to court. This study uses the LINE application as cyberbullying crime media, as well as using the National Institute of Justice (NIJ) method. The National Institute of Justice (NIJ) method has five basic stages namely, preparation, collection, examination, analysis, and reporting. In this study using the MOBILedit Forensic tool, and DB Browser for SQLite. It is hoped that the research carried out can help in solving cyberbullying on social media LINE on mobile forensics

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Dissertationen zum Thema "MOBILedit"

Shakir, Amer, Muhammad Hammad und Muhammad Kamran. „Comparative Analysis & Study of Android/iOS MobileForensics Tools“. Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44797.

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This report aims to draw a comparison between two commercial mobile forensics and recovery tools, Magnet AXIOM and MOBILedit. A thorough look at previously done studies was helpful to know what aspects of the data extractions must be compared and which areas are the most important ones to focus upon. This work focuses on how the data extracted from one tool compares with another and provides comprehensive extraction based on different scenarios, circumstances, and aspects. Performances of both tools are compared based on various benchmarks and criteria. This study has helped establish that MOBILedit has been able to outperform Magnet AXIOM on more data extraction and recovery aspects. It is a comparatively better tool to get your hands on.

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Oelhafen, Markus. „SNMP Application for the MINT Router (Walkstation II project)“. Thesis, KTH, Teleinformatik, 1994. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98767.

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The purpose of the Walkstation II project is to create a testbed for a wireless communication system with access to the Internet. The Mobile INTernet (MINT) router is a core element of this project. Most of the routers and many other nodes of today's Internet support network management. This includes the remote control of various parameters which caracterize the status of physical interfaces, protocols and many other objects. The Simple Network Management Protocol (SNMP) which was used in the current work is a standard protocol of the TCP/IP suite. All the managed nodes of the Internet have a common set of management capabilities. Their implementation is available in software development packages. The management of mobile stations requires a new set of management capabilities. They will allow for the control of mobility support and the radio links. The current work consists in adapting the set of standard SNMP capabilities to the MINT router and of the creation of a set of new capabilities specific to the MINT.

I do not know the number of university credits, but entered it as 30 ECTS. This was an exchange student and I do not know if they were actually registered at KTH.

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Hussain, Ishfaq. „Scalable Device Mobility – Mobile DCXP“. Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-23324.

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The continuously increasing Internet coverage and its availability has give rise to an issue that was once considered not important to take into consideration. Today a number of applications use the Internet to de-liver time critical messages. The usage of wireless Access Points involves a considerable percentage to connect mobile devices to the Internet pro-vider. However, these relatively cheaper Internet Access Points have their own disadvantages as compared to the GSM and ADSL. The access points cover a very limited area and thus in order to cover a wider area multiple access points must needs to be installed. In other words, as the user moves he/she is supposed to switch between access points. Never-theless, the basic problem in such cases is involves packet loss during handover. In today’s technological advancements these issues, though very small, are no more insignificant but are required to be handled prop-erly. So protocols such as MobileIP, LISP, HOST have been proposed and are currently being used for such a purpose. Furthermore, in this thesis a mechanism to reduce such packet losses has been studied and proposed in relation to the SensibleThings Internet-of-things platform. A workaround solution known as Mobile DCXP has been proposed and implemented and comparisons with the existing system have been carried out. In addition, a generic solution has been discussed in detail and compared with the Mobile DCXP. However, the implementation of the generic solution has been deferred to the future. The concept of Mobile DCXP has been illustrated with proof-of-concept apps and an implementation of a simple Android Application known as IChat has been conduct. The IChat is a simple chat app that is used in the experi-ment to determine out the packet lost during handover and to carry out a comparison. Finally, based on the data collected from IChat, an evaluation of Mobile DCXP has been presented and a performance comparison with Mobile Proxy DCXP has been illustrated with charts. Furthermore, in the conclusion Mobile DCXP could minimize packet loss as compared to the existing system.

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Soncini, Filippo. „Classificazione di documenti tramite reti neurali“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20509/.

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Questo elaborato è stato proposto con l’obbiettivo di affrontare il problema della classificazione di documenti utilizzando sia contenuti visivi che testuali, cercando di analizzare diverse reti e diverse combinazioni di esse per poi sviluppare un modello personalizzato.

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Nyström, Joakim, und Mikael Seppälä. „Experimental Study of GPRS/WLAN Systems Integration“. Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1750.

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The convergence of future networks relies on the evolution of technology that enables seamless roaming abilities across non-heterogenous networks for mobile clients. This thesis presents an experimental study of a GPRS-WLAN integration scenario where the objective is to analyze various aspects of the issues related to charging, mobility, roaming and security between GPRS and WLAN networks. The mainly discussed integration scenario in this thesis is loosely coupled systems working on RADIUS platforms, which together with MobileIP and IPSec provides the mobile client with a secure and access-technology independent network access platform.

In order to accommodate GPRS client authentication for WLAN operators, there is a prominent need for the incorporation of necessary GPRS functionality into present AAA servers. RADIUS has been studied as the initial target for the implementation of a GPRS interface towards SMS-Cs and HLRs.The authentication of a mobile client is performed against a HLR/AuC in a GPRS network, either over SS7 links or through the incorporation of SIGTRAN protocols over SCTP. SIGTRANsolutions has the ability to join WLAN networks in a SS7 resource sharing model where the SS7 authentication signalling traffic is transported over IP networks to a Signalling Gateway acting as the logical interface against SS7 networks.

GPRS-WLAN accounting may be solved through direct roaming agreements between mobile operators and in such a case transport CDR’s over FTP between their billing systems. If roaming agreements does not exist, it may be viable to establish relationships between WLANs and brokers as well as mobile operators and brokers. The brokering model provides a scalable model that allows easier exchange of charging and billing information on an infrastructure based on WLAN and GPRS billing systems. Standardised transmission protocols for accounting information such as GTP’/TAP3 may be utilised in order to provide a generic billing exchange format between billing systems and operators.

Furthermore, different network architectures may have different requirements in order to accommodate GPRS clients with WLAN access. A few network architectures has been analysed, and the developed GPRS AAA Interface Daemon (GAID) has been put into context in order to present a generic GPRS-WLAN systems integration solution for WLAN operators.

The analysed solutions in this thesis give various possibilities for WLAN operators to setup wireless services for bypassing mobile clients. The implementational work provides a RADIUS platform, which can be enhanced with functionality that enables communication over any interface in the future.

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Cotugno, Giosuè. „Dall’IA all’olio: come affinare i sistemi di classificazione della qualità attraverso tecniche di machine learning con l’utilizzo di reti neurali“. Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20837/.

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Sino ad oggi, per verificare se un olio è extravergine d’oliva o contiene dei difetti, non abbiamo potuto usufruire di molti supporti tecnologici, infatti la tecnica maggiormente utilizzata per la classificazione degli oli è il panel test che consiste nell’assaggio di questi da un gruppo di persone esperte. Recentemente a seguito di analisi oggettive delle mo- lecole costituenti l’olio, sono state introdotte ulteriori metodologie per la classificazione grazie all’utilizzo di analisi statistiche. Questa tesi, dunque, propone un ulteriore me- todo di classificazione grazie all’utilizzo delle reti neurali, ossia modelli computazionali composti da neuroni "artificiali" disposti su più livelli che si ispirano ad una rete neurale biologica. Utilizzando i dati sensoriali forniti dal panel test ed i dati molecolari ricava- ti da analisi chimiche, siamo stati in grado di creare un modello basato sulle reti neurali, capace di predire, con una certa accuratezza, la classe di appartenenza di un olio data la sua composizione molecolare. Le reti su cui si è basata questa sperimentazione sono le seguenti: resnet18, resnet50, mobilenet ed infine una rete neurale costruita ad–hoc per questo esperimento. Le prime tre sono reti convoluzionali, dunque specializzate nel riconoscimento di immagini, mentre la rete personalizzata ha la struttura di una classica rete neurale.L’utilizzo di tutte queste reti ha permesso di comprendere quale sia la miglior tipologia di rete su cui basare ulteriori ricerche nell’ambito della classificazione dell’olio e di comprendere quali siano le loro potenzialità. I risultati ottenuti hanno permesso di concludere che per riuscire a separare differenti classi di olio è sufficiente utilizzare reti neurali classiche e che la ricerca è sulla buona strada per riuscire a trovare uno strumento solido utile a questo scopo.

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Jackman, Simeon. „Football Shot Detection using Convolutional Neural Networks“. Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157438.

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In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architectures, different sampling methods are evaluated as well. This thesis shows that amongst the three evaluated methods, the ap- proach using MobileNetV2 as a feature extractor works best. However, when applying the networks to a video stream there are a multitude of challenges, such as false positives and incorrect annotations that inhibit the potential of detecting shots.

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Michelini, Mattia. „Barcode detection by neural networks on Android mobile platforms“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21080/.

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Lo scopo di questa esperienza di tesi è stato quello di fare un confronto sul tema delle reti neurali e, in particolare, sul modo di portare la potenza inferenziale di questi modelli nel mondo mobile. Il caso di studio interessa i codici a barre e, nello specifico, l’obiettivo è stato quello di riuscire a identificare la zona in cui questi si trovavano, in modo poi da avere una zona minore da indagare con un altro algoritmo specifico per la decodifica (cosa che esula dallo scopo della tesi). Questo è chiaramente un problema di object detection e, per risolverlo, ho esplorato due differenti tipi di approccio: in prima istanza ho indagato la funzione di object detection di ML Kit, per poi passare ad utilizzare direttamente un modello a mia scelta tramite TensorFlow Lite.

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Matula, Tomáš. „Využití aproximovaných aritmetických obvodů v neuronových sítí“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399179.

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Táto práca sa zaoberá využitím aproximovaných obvodov v neurónových sieťach so zámerom prínosu energetických úspor. K tejto téme už existujú štúdie, avšak väčšina z nich bola príliš špecifická k aplikácii alebo bola demonštrovaná v malom rozsahu. Pre dodatočné preskúmanie možností sme preto skrz netriviálne modifikácie open-source frameworku TensorFlow vytvorili platformu umožňujúcu simulovať používanie approximovaných obvodov na populárnych a robustných neurónových sieťach ako Inception alebo MobileNet. Bodom záujmu bolo nahradenie väčšiny výpočtovo náročných častí konvolučných neurónových sietí, ktorými sú konkrétne operácie násobenia v konvolučnách vrstvách. Experimentálne sme ukázali a porovnávali rozličné varianty a aj napriek tomu, že sme postupovali bez preučenia siete sa nám podarilo získať zaujímavé výsledky. Napríklad pri architektúre Inception v4 sme získali takmer 8% úspor, pričom nedošlo k žiadnemu poklesu presnosti. Táto úspora vie rozhodne nájsť uplatnenie v mobilných zariadeniach alebo pri veľkých neurónových sieťach s enormnými výpočtovými nárokmi.

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Giambi, Nico. „Sperimentazione di tecniche di Deep Learning per l'Object Detection“. Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21557/.

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Il lavoro svolto in questa tesi ruota intorno alla sperimentazione di tecniche di Deep Learning per l'Object Detection, ovvero la costruzione di un Object Detector a la YOLO partendo da zero testando per ogni parte della costruzione più alternative possibili per verificarne la praticità e correttezza, estrapolando per le varie fasi le soluzioni migliori, sia dal punto di vista funzionale sia per quanto riguarda la semplicità. In questa tesi è stato creato un Object Detector sfruttando MobileNet (una Convolutional Neural Network molto veloce) associata ad un algoritmo in stile YOLO (principalmente YOLOv2) e allenata sul dataset COCO (Common Objects in COntext). Le prove effettuate spaziano in tutti i campi, dalla scelta di usare un modello pre-allenato su un altro dataset alla decisione di alcuni parametri da usare come threshold in fase di post-processing. All'interno della tesi verranno spiegati brevemente i temi principali toccati dall'argomento e tutte le prove svolte, spiegando quali di ognuna di queste sia risultata migliore.

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Xiao, Yang, Hrsg. WiMAX/MobileFi. Auerbach Publications, 2007. http://dx.doi.org/10.1201/9781420043525.

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Building Dynamic WAP Applications with MobileDev. Sams, 2001.

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WiMAX/MobileFi: Advanced Research and Technology. AUERBACH, 2007.

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Yang, Xiao, Hrsg. WiMAX/MobileFI: Advanced research and technology. Boca Raton: Auerbach Publications, 2008.

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Buchteile zum Thema "MOBILedit"

Koonce, Brett. „MobileNet v1“. In Convolutional Neural Networks with Swift for Tensorflow, 87–97. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6168-2_8.

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Koonce, Brett. „MobileNet v2“. In Convolutional Neural Networks with Swift for Tensorflow, 99–107. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6168-2_9.

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Handreke, Thomas, und Ingo Thiem. „Mit MOBILEIT-S integrierte Fertigungsleitsysteme flexibel modellieren“. In Informatik ’97 Informatik als Innovationsmotor, 427–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60831-5_55.

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Fieger, A., J. Diederich und M. Zitterbart. „Optimierung von Subnetzwechseln mit MobileIP“. In Kommunikation in Verteilten Systemen (KiVS), 20–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60111-8_5.

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Akbar, Agus Subhan, Chastine Fatichah und Nanik Suciati. „Modified MobileNet for Patient Survival Prediction“. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 374–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72087-2_33.

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Hung, Phan Duy, und Nguyen Ngoc Kien. „SSD-Mobilenet Implementation for Classifying Fish Species“. In Advances in Intelligent Systems and Computing, 399–408. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33585-4_40.

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Potluri, Tejaswi, Somavarapu Jahnavi und Ravikanth Motupalli. „Mobilenet V2-FCD: Fake Currency Note Detection“. In Communications in Computer and Information Science, 274–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3660-8_26.

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Xie, Yuxuan, Bing Liu, Lei Feng, Xipeng Li und Danyin Zou. „A FPGA-Oriented Quantization Scheme for MobileNet-SSD“. In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 95–103. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9710-3_10.

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Cao, Jianzhao, Renning Pang, Ruwei Ma und Yuanwei Qi. „Face Mask Recognition Based on MTCNN and MobileNet“. In Advances in Intelligent Systems and Computing, 433–42. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4575-1_41.

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Su, Jiang, Julian Faraone, Junyi Liu, Yiren Zhao, David B. Thomas, Philip H. W. Leong und Peter Y. K. Cheung. „Redundancy-Reduced MobileNet Acceleration on Reconfigurable Logic for ImageNet Classification“. In Applied Reconfigurable Computing. Architectures, Tools, and Applications, 16–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78890-6_2.

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Konferenzberichte zum Thema "MOBILedit"

Kukka, Hannu, und Timo Ojala. „mobileDOK“. In the 3rd international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1292331.1292379.

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Buddhikot, Milind, Adiseshu Hari, Kundan Singh und Scott Miller. „MobileNAT“. In the 1st ACM international workshop. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/941326.941338.

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Hwang, Jee Yeon. „MobileAct“. In Proceeding of the seventh ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1640233.1640310.

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Sinha, Debjyoti, und Mohamed El-Sharkawy. „Thin MobileNet: An Enhanced MobileNet Architecture“. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2019. http://dx.doi.org/10.1109/uemcon47517.2019.8993089.

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Wu, Wanmin, Raoul Rivas, Ahsan Arefin, Shu Shi, Renata M. Sheppard, Bach D. Bui und Klara Nahrstedt. „MobileTI“. In the seventeen ACM international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631272.1631437.

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Qin, Zheng, Zhaoning Zhang, Xiaotao Chen, Changjian Wang und Yuxing Peng. „Fd-Mobilenet: Improved Mobilenet with a Fast Downsampling Strategy“. In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451355.

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Sinha, Debjyoti, und Mohamed El-Sharkawy. „Ultra-thin MobileNet“. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2020. http://dx.doi.org/10.1109/ccwc47524.2020.9031228.

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Chen, Hong-Yen, und Chung-Yen Su. „An Enhanced Hybrid MobileNet“. In 2018 9th International Conference on Awareness Science and Technology (iCAST). IEEE, 2018. http://dx.doi.org/10.1109/icawst.2018.8517177.

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Zhu, Jing, Aidong Deng, Shun Zhang, Xue Ding und Shuo Xue. „Rolling Bearing Fault Diagnosis and Prediction Based on VMD-CWT and MobileNet“. In 7th International Conference on Advances in Computer Science and Information Technology (ACSTY 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110305.

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When deep learning is used for rolling bearing fault diagnosis, there are problems of high model complexity, time-consuming, and large memory. In order to solve this problem. This paper presents an intelligent diagnosis method of rolling bearings based on VMD-CWT feature extraction and MobileNet, VMD is used to extract the signal features, and then wavelet transform is used to extract the timefrequency features. After the image is enhanced, the MobileNet network is trained. In order to accelerate the convergence speed, this paper adds transfer learning in the network training process, and migrates the weights of the first several layers pretrained to the corresponding network. Experimental results based on bearing fault data sets show that after adopting VMD-CWT, the accuracy of mobilenet increased from 68.7% to 94%, and its network parameters were reduced by an order of magnitude compared with CNN.

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Rabano, Stephenn L., Melvin K. Cabatuan, Edwin Sybingco, Elmer P. Dadios und Edwin J. Calilung. „Common Garbage Classification Using MobileNet“. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM). IEEE, 2018. http://dx.doi.org/10.1109/hnicem.2018.8666300.

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