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Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to analog-neural-networks (ANNs). Although it is difficult to efficiently train SNNs, many…

Deep neural networks (DNNs) have delivered a remarkable performance in many tasks of computer vision. However, over-parameterized representations of popular architectures dramatically increase their computational complexity and storage…

Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, which precludes the sharing of data between researchers and…

Intermittently Connected Delay-Tolerant Wireless Sensor Networks (ICDT-WSNs), a branch of Wireless Sensor Networks (WSNs), have features of WSNs and the intermittent connectivity of Delay-Tolerant Networks (DTNs). The applications of…

Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, and virtual reality, etc. However,…

Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behavior expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends. However, many contemporary…

The aim of this project is to implement and design arobust synthetic speech classifier for the IEEE Signal ProcessingCup 2022 challenge. Here, we learn a synthetic speech attributionmodel using the speech generated from various…

Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their applications to real-life problems. This paper introduces tn4ml, a…

It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originating from quantum…

One of the main pervasive problems Wireless Sensor Networks (WSN) encounter is to maintain flawless communication sharing and cooperative processing between sensors via radio links to ensure a reliable treatment of information. Many…

IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and…

Clouds gather a vast volume of telemetry from their networked systems which contain valuable information that can help solve many of the problems that continue to plague them. However, it is hard to extract useful information from such raw…

Current designs of future In-Vehicle Networks (IVN) prepare for switched Ethernet backbones, which can host advanced LAN technologies such as IEEE Time-Sensitive Networking (TSN) and Software-Defined Networking (SDN). In this paper, we…

The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in…

In pursuance of integrating Wireless Sensor Networks (WSNs) with other systems, the use of techniques from other fields, such as machine learning and information processing, are becoming more common. Therefore, we faced the problem of…

Wireless sensor networks (WSNs) are employed across a wide range of industrial applications where ultra-low power consumption is a critical prerequisite. At the same time, these systems must maintain a certain level of determinism to ensure…

Connectivity has a major role in the current transformation of smart manufacturing. 5G is foreseen as an integral part of an end-to-end networking infrastructure supporting smart manufacturing operation. The integration of 5G with…

Convergence of time-sensitive machine control networks as part of the operational technology (OT) with the ubiquitous information technology (IT) networks is an essential requirement for the ongoing digitalization of production. In this…

Temporal Neural Networks (TNNs) are spiking neural networks that use time as a resource to represent and process information, similar to the mammalian neocortex. In contrast to compute-intensive deep neural networks that employ separate…

In modern applications such as in the prospective smart factory, timely and faultfree communication is one of the main concerns. Communication failures may lead to huge economic losses. Moreover, they can even endanger human life.…