Accidents in inland waterways in developing countries are a regular phenomenon throughout the year causing deaths, injuries, monetary loss, and a significant amount of missing people. In consequence, a lot of families are losing their dear ones leading to much misery. The above context demands an intelligent, safe, and reliable water transport system for the developing countries. The concept of Intelligent Transport System (ITS) can be applied to develop such system; however, there are issues with ITS and Internet of Things (IoT) unlocks a new way of developing it. This paper proposes a model to transform the water transport system into an intelligent system based on IoT. IPv6 based machine-to-machine (M2M) protocol, 3G telecommunication technology, and IEEE 802.15.4 network standard play a significant role in this proposed IoT based system.
Deep learning has celebrated resounding successes in many application areas of relevance to the Internet of Things (IoT), such as computer vision and machine listening. These technologies must ultimately be brought directly to the edge to fully harness the power of deep leaning for the IoT. The obvious challenge is that deep learning techniques can only be implemented on strictly resource-constrained edge devices if the models are radically downsized. This task relies on different model compression techniques, such as network pruning, quantization, and the recent advancement of XNOR-Net. This study examines the suitability of these techniques for audio classification on microcontrollers. We present an application of XNOR-Net for end-to-end raw audio classification and a comprehensive empirical study comparing this approach with pruning-and-quantization methods. We show that raw audio classification with XNOR yields comparable performance to regular full precision networks for small numbers of classes while reducing memory requirements 32-fold and computation requirements 58-fold. However, as the number of classes increases significantly, performance degrades, and pruning-and-quantization based compression techniques take over as the preferred technique being able to satisfy the same space constraints but requiring approximately 8x more computation. We show that these insights are consistent between raw audio classification and image classification using standard benchmark sets. To the best of our knowledge, this is the first study to apply XNOR to end-to-end audio classification and evaluate it in the context of alternative techniques. All codes are publicly available on GitHub.
As stateless software applications run based on request and response to and from a server, it depends on the server processes to manage concurrent requests and its session state. In the cases, where there are modifications to the session state, the server will have to handle the requests synchronously to prevent many issues relating to race conditions. The server components usually have a maximum execution timeout for requests to finish its execution. It is possible to extend this limit but it may open a security threat for the application as it is now more susceptible to denial of service attacks. With these limitations, we propose a pattern to allow for time-intensive processes to run in a stateless application and the ability to monitor their progress in real time with other capabilities such as stopping, pausing and resuming the background task.
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