arXiv subjects

Mahmood Ahmadi

Publications and source records attributed to Mahmood Ahmadi.

21 records · Page 2Linked to original sources

arXiv2022

Open Source Routers: A Survey

Variety, size and complexity of data types, services and applications in Internet is continuously growing up. This increasing of complexity needs more powerful and sophisticated equipment's. One group of these devices that has essential role are routers. Some of vendors produce some elaborate and complex products but the commercial solutions are too closed and inflexible. The term "Open Source Routers" covers a lot of implementations of free software routers. Open Source Routers are solutions to overcome commercial solutions with closed platforms. In this article, we survey the existing implementations and a wide array of past and state-of-the-art projects on open software routers followed by a discussion of major challenges in this area.

cs.NI↗

arXiv2022

Past, Present and Future of Hadoop: A Survey

In this paper, a technology for massive data storage and computing named Hadoop is surveyed. Hadoop consists of heterogeneous computing devices like regular PCs abstracting away the details of parallel processing and developers can just concentrate on their computational problem. A Hadoop cluster is made of two parts: HDFs and Mapreduce. Hadoop cluster uses HDFS for data management. HDFS provides storage for input and output data in MapReduce jobs and is designed with abilities like high-fault tolerance, high-distribution capacity, and high throughput. It is also suitable for storing Terabyte data on clusters and it runs on flexible hardware like commodity devices.

cs.NI↗

arXiv2016

evt_MNIST: A spike based version of traditional MNIST

Benchmarks and datasets have important role in evaluation of machine learning algorithms and neural network implementations. Traditional dataset for images such as MNIST is applied to evaluate efficiency of different training algorithms in neural networks. This demand is different in Spiking Neural Networks (SNN) as they require spiking inputs. It is widely believed, in the biological cortex the timing of spikes is irregular. Poisson distributions provide adequate descriptions of the irregularity in generating appropriate spikes. Here, we introduce a spike-based version of MNSIT (handwritten digits dataset),using Poisson distribution and show the Poissonian property of the generated streams. We introduce a new version of evt_MNIST which can be used for neural network evaluation.

cs.NE↗