Differences between Google NFC API and Open NFC API

本文深入探讨了Android平台下OpenNFC技术栈的优劣与使用方法,指出其作为非标准组件的特性及其对不同环境的适配性。强调了此技术栈在实现更多NFC功能方面的潜力,但同时也指出了由于其非内置性带来的安装与部署工作。建议开发者根据项目需求选择是否采用此技术栈,并概述了其与GoogleNFC堆栈之间的对比,最终得出结论,直到某手机制造商整合该技术栈,OpenNFC的吸引力有限。
link NFC Google API:[url]http://developer.android.com/resources/samples/NFCDemo/index.html[/url]

link NFC Open API: [url]http://www.open-nfc.org/opennfc_library/overview-summary.html[/url]
At the moment (Android 2.3.4), the Android platform does not integrate the Open NFC stack. This stack has several advantages: not limited to one particular NFC hardware, portable to many different environments (not only Android), many additional features compared to the current stack. On the other hand, because Open NFC is not part of the standard Android environment, it requires some work (but it is actually quite easy) to use this stack in an Android platform. The Open NFC documentation describes this process.

The Open NFC stack is parallel activity and is optional replacement to the current stack. Due to its description it overcomes some limits of the Google NFC stack implementation and makes new HW adaptions (i.e. support for new tag types) easier…

This stack is intended to by used by device manufacturers, not mobile developers, because the standard Android kernel does not support modules loading, the Open NFC stack cannot be simply installed as another application, it requires a kernel change. But once the kernel is replaced, it is quite easy to deploy and use the Open NFC stack. An application developer should probably stick to the OS features, even if more limited, since they assure the portability of the code over any NFC-enabled phone. But if you are trying to use "more" features than what comes with Android, Open NFC is a good candidate.

In Conclusion : Until some phone manufacturer will integrate the stack into device, it does not make too much sense to be interested in Open NFC stack. An app developer cannot make use of Open NFC unless it is on hardware.
GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) are both advanced natural language processing (NLP) models developed by OpenAI and Google respectively. Although they share some similarities, there are key differences between the two models. 1. Pre-training Objective: GPT is pre-trained using a language modeling objective, where the model is trained to predict the next word in a sequence of words. BERT, on the other hand, is trained using a masked language modeling objective. In this approach, some words in the input sequence are masked, and the model is trained to predict these masked words based on the surrounding context. 2. Transformer Architecture: Both GPT and BERT use the transformer architecture, which is a neural network architecture that is specifically designed for processing sequential data like text. However, GPT uses a unidirectional transformer, which means that it processes the input sequence in a forward direction only. BERT, on the other hand, uses a bidirectional transformer, which allows it to process the input sequence in both forward and backward directions. 3. Fine-tuning: Both models can be fine-tuned on specific NLP tasks, such as text classification, question answering, and text generation. However, GPT is better suited for text generation tasks, while BERT is better suited for tasks that require a deep understanding of the context, such as question answering. 4. Training Data: GPT is trained on a massive corpus of text data, such as web pages, books, and news articles. BERT is trained on a similar corpus of text data, but it also includes labeled data from specific NLP tasks, such as the Stanford Question Answering Dataset (SQuAD). In summary, GPT and BERT are both powerful NLP models, but they have different strengths and weaknesses depending on the task at hand. GPT is better suited for generating coherent and fluent text, while BERT is better suited for tasks that require a deep understanding of the context.
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