ABSTRACT This paper introduces a novel, dual‐band, compact 1:4 feed network employing a parallel power divider architecture designed to operate at 0.915 and 2.44 GHz, covering both industrial, scientific, and medical and ultra high frequency bands. The design leverages the non‐linear phase characteristics of composite right/left‐handed transmission lines to achieve dual‐band functionality with high precision. Simulation results confirm the efficacy of the proposed network, which delivers quadrature‐phase outputs with a 90° phase shift and uniform power distribution across all output ports, facilitating wideband circular polarisation in array antenna applications. Compared to traditional series power dividers, the parallel power divider offers significant advantages in terms of fabrication simplicity, reduced size, and lower manufacturing costs. The design avoids the use of non‐radiating composite right/left‐handed transmission lines and addresses impedance‐matching challenges through the implementation of only three resistors, effectively isolating the output ports. The proposed architecture is highly scalable and can be easily adapted to various output port configurations, frequencies, and power division ratios, offering broad flexibility for a wide range of microwave applications.

Autonomous mobile systems increasingly rely on deep neural networks for perception and decision-making. While effective, these systems are vulnerable to adversarial machine learning attacks where small perturbations in the input could significantly impact the outcome of the system. Common countermeasures include leveraging adversarial training and/or data or network transformation. Although widely used, the main drawback of these countermeasures is that they require full and invasive access to the classifiers, which are typically proprietary. Additionally, the cost of training or retraining is often prohibitively expensive for large models. To tackle this, purification models have recently been proposed. The aim is to incorporate a "purification" layer before classification, thereby eliminating the necessity to modify the classifier. Despite their effectiveness, state-of-the-art purification methods are compute-intensive, rendering them unsuitable for mobile systems where resources are constrained and large latency is not desired.

Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user distributes the desired computation between the IoT and cloud nodes. While achieving significant speed up, the major caveat of this approach is data privacy. Privacy-preserving methods have received major attention in the past few years, mainly because they can potentially solve this issue. Among several proposals, methods based on dynamic encoding and perturbation offer flexibility and low overhead. However, they often consider a weak adversary model or overlook practical limitations, such as encoding latency and complexity. This work proposes a new privacy-preserving method to address these issues. The key contributions of this article are twofold. First, unlike state-of-the-art, it proposes a new approach based on evolutionary algorithms to systematically evaluate the robustness of the encoding algorithm against a large population of potential adversaries. Second, it develops a dynamic obfuscation strategy that balances latency requirements in a realistic IoT-cloud hybrid ecosystem and privacy demands. Additionally, our method offers a unique benefit: it can be used alone for privacy protection, or it can be integrated with most existing methods to enhance privacy and reduce latency. The applicability and effectiveness of our proposed methods are thoroughly evaluated using two popular deep neural networks in a real-world IoT-cloud setting. We study the impact of our approach on important metrics, such as accuracy and privacy. Our results show that our proposed method can improve the overall privacy of a given IoT-cloud hybrid ecosystem by more than 10% on average.

Privacy-preserving machine learning (PPML) techniques have allowed remote and private inference for resource-constrained internet-of-things (IoT) devices on the cloud. The main challenge in most of the existing PPML technologies is a severe slowdown in inference latency mainly due to the use of encryption during the computation. To combat this, an emerging method is to leverage encoding as an alternative. While this results in a significant speedup, it imposes the burden of encoding to the resource-constrained IoT/edge device. Despite being feasible for simple workloads where encoding is lightweight, devices with very limited computational capabilities face a tradeoff between latency and privacy when performing complex tasks.