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The challenges of the Internet of Things (IoT) in an urban environment are driven by smart vehicles which need to be able to efficiently sense and communicate with other nearby vehicles. Systemon-chip (SoC) applications in the automotive market have strict circuit performances and reliability requirements for a temperature range of up to 175 o C. This work proposes an analysis of latchedcomparators performance considering process variability and temperature variation. State-of-the-art StrongArm and Double-Tail comparators are designed using an XH018 technology. Post-layout simulation results are drawn in order to validate the proposed temperatureaware analysis. Besides the known advantages of the Double-Tail comparator, this work demonstrates that such a comparator has a serious drawback under harsh environments. At 175 o C, the Double-Tail presents a 3.1 ns worst case delay and 1.4 mV offset, while StrongArm shows 2.7 ns and 2.7 mV respectively. Moreover, the Double-Tail's input-referred noise achieves worst-case levels of 0.89 mV, the StrongArm's noise is below 0.4 mV. Therefore, the Double-Tail proved to be less reliable than the StrongArm and also foretells critical failure conditions in harsh environments. CCS CONCEPTS• Hardware Process, voltage and temperature variations; Analog and mixed-signal circuit synthesis;

In passive sonar, the detection and tracking of contacts based on the energy of Direction of Arrival (DoA) is a challenge for the spatiotemporal clusterization algorithms. Typically, the tracking of spatiotemporal data involves two steps, first a clusterization of a temporal frame to reduce the data to micro-cluster and a second step tracking the micro-cluster moving. That tracking approach has problems to predict the motion and resolve merge and split of clusters. In this paper, we propose, analyze e test an algorithm to explore the characteristics of passive sonar` signals and improve the tracking performance. To resolve the bearing crossing we proposed an automatic parameter extraction on Low-Frequency Analysis and Recording (LOFAR) and Detection of Envelope Modulation on Noise (DEMON) with the same tracking abording to extract the contact acoustic signature and in case of crossing reassociate the contact based on his signature. The proposed algorithm is analyzed in a theoretical dataset to parameters tuning and evaluated in a real dataset collected by a flank array in a navigation channel.

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