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There is a life after breast cancer. The prerequisite is early detection. Breast cancer is curable when detected early, tiny, and has not spread – regular screening aids in early detection. Clinical Thermography and artificial intelligence are potentially a good fit for early breast cancer screening. This survey paper presents a systematic review of artificial intelligence-based breast cancer screening using thermal infrared cameras. Initially, we will present the qualitative analysis of the existing literature regarding the trend and distribution. This review manuscript will then explore the literature about infrared thermal image acquisition and storage techniques. We will then highlight various segmentation techniques used for processing infrared thermal images. This paper presents the experimental results of the traditional image processing and deep learning-based segmentation techniques available in the literature using infrared breast thermal images. We then summarize the works that have used artificial intelligence to segment and classify infrared thermal images. The existing literature shows opportunities to explore the area of explainable artificial intelligence (AI). Explainable AI will make clinical Thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

In a network of autonomous systems (ASes), prefix reachability can be affected by events such as link and node failures, router misconfiguration, route flaps and prefix hijack attacks. Furthermore, with the increase in global organizations deploying their data centres and content services in other countries that have good networking infrastructures, studying prefix reachability is of significance. Since detecting these events at the end user level is challenging, by monitoring the spatially distributed routing table features, such as AS path distributions and spatial prefix reachability distributions, the events can be detected at the control plane level. In this work, a measure and a method to detect long term events at a country level AS topology are proposed. To understand the occurrence of control plane level events, temporal pattern analysis over the distributed peer prefix announcements of a country-level AS topology was conducted. Five Asian countries are considered as a representative set to study the occurrence of events. To capture and measure the spatially occurring events in a single temporal pattern, we proposed a counting-based measure using prefixes announced by x % of n spatially distributed peers. Our method to detect events employs mean change point detection technique with normal and CUSUM test statistics over the proposed measure. Other statistical techniques such as regression estimation and K-means clustering are used in our method to quantify the impact and duration of long-term control plane events. The detected events are validated using average path pattern correlation and Fisher scores of different path length features. We also validated the events using the SEA-Me-We4 cable cut event manifestations. The comparison results with other event detection techniques demonstrate the efficacy of the mean change point technique with normal assumption used in our method. [ABSTRACT FROM AUTHOR]

With the increase in process variations and diversity in workloads, it is imperative to holistically explore optimization techniques for power and temperature from the circuit layer right up to the compiler/operating system (OS) layer. This article proposes one such holistic technique, called proactive workload aware temperature management framework for low-power chip multi-processors (ProWATCh). At the compiler level ProWATCh includes two techniques: (1) a novel compiler design for estimating the architectural parameters of a task at compile time; and (2) a model-based technique for dynamic estimation of architectural parameters at runtime. At the OS level ProWATCh integrates two techniques: (1) a workload- and temperature-aware process manager for dynamic distribution of tasks to different cores; and (2) a model predictive controlbased task scheduler for generating the efficient sequence of task execution. At the circuit level ProWATCh implements either of two techniques: (1) a workload-aware voltage manager for dynamic supply and body bias voltage assignment for a given frequency in processors that support adaptive body bias (ABB); or (2) a workload-aware frequency governor for efficient assignment of upper and lower frequency bounds for frequency scaling in processors that do not support an ABB. Employing ProWATCh (with voltage manager) on an ABB-compatible 3D OpenSPARC architecture using MiBench benchmarks resulted in an average 18% (19°C) reduction in peak temperature. Evaluating ProWATCh on an existing quad-core Intel Corei7 processor with frequency governor alone (as the processor does not support an ABB interface) resulted in 10% (8°C) reduction in peak temperature when compared to what was obtained using the native Linux 3.0 completely fair scheduler (CFS). To study the effectiveness of the proposed framework across benchmark suites, ProWATCh was evaluated on a quad-core Intel Corei7 processor using CPU SPEC 2006 benchmarks which resulted in 7°C reduction in peak temperature as compared to the native Linux 3.0 CFS. [ABSTRACT FROM AUTHOR]