Microbial mats are found in a variety of modern environments, with evidence for their presence as old as the Archean. There is much debate about the rates and conditions of processes that eventually lithify and preserve mats as microbialites. Here, we apply novel tracer experiments to quantify both mat biomass addition and the formation of CaCO3. Microbial mats from Little Hot Creek (LHC), California, contain calcium carbonate that formed within multiple mat layers, and thus constitute a good test case to investigate the relationship between the rate of microbial mat growth and carbonate precipitation. The laminated LHC mats were divided into four layers via color and fabric, and waters within and above the mat were collected to determine their carbonate saturation states. Samples of the microbial mat were also collected for 16S rRNA analysis of microbial communities in each layer. Rates of carbonate precipitation and carbon fixation were measured in the laboratory by incubating homogenized samples from each mat layer with δ13C-labeled HCO3- for 24 h. Comparing these rates with those from experimental controls, poisoned with NaN3 and HgCl2, allowed for differences in biogenic and abiogenic precipitation to be determined. Carbon fixation rates were highest in the top layer of the mat (0.17% new organic carbon/day), which also contained the most phototrophs. Isotope-labeled carbonate was precipitated in all four layers of living and poisoned mat samples. In the top layer, the precipitation rate in living mat samples was negligible although abiotic precipitation occurred. In contrast, the bottom three layers exhibited biologically enhanced carbonate precipitation. The lack of correlation between rates of carbon fixation and biogenic carbonate precipitation suggests that processes other than autotrophy may play more significant roles in the preservation of mats as microbialites.

Over the past two decades, globalized outsourcing in the semiconductor supply chain has lowered manufacturing costs and shortened the time-to-market for original equipment manufacturers (OEMs). However, such outsourcing has rendered the printed circuit boards (PCBs) vulnerable to malicious activities and alterations on a global scale. In this article, we take an in-depth look into one such attack, called the “Big Hack,” that was recently reported by Bloomberg Buisnessweek. The article provides background on the Big Hack from three perspectives: an attacker, a security investigator, and the societal impacts. This study provides details on vulnerabilities in the modern PCB supply chain, the possible attacks, and the existing and emerging countermeasures. The necessity for novel visual inspection techniques for PCB assurance is emphasized throughout the article. Further, a review of various imaging modalities, image analysis algorithms, and open research challenges are provided for automated visual inspection.

In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.

A Bill of Materials (BoM) is the list of all components present on a Printed Circuit Board (PCB). BoMs are useful for multiple forms of failure analysis and hardware assurance. In this paper, we build upon previous work and present an updated framework to automatically extract a BoM from optical images of PCBs in order to keep up to date with technological advancements. This is accomplished by revising the framework to emphasize the role of machine learning and by incorporating domain knowledge of PCB design and hardware Trojans. For accurate machine learning methods, it is critical that the input PCB images are normalized. Hence, we explore the effect of imaging conditions (e.g. camera type, lighting intensity, and lighting color) on component classification, before and after color correction. This is accomplished by collecting PCB images under a variety of imaging conditions and conducting a linear discriminant analysis before and after color checker profile correction, a method commonly used in photography. This paper shows color correction can effectively reduce the intraclass variance of different PCB components, which results in a higher component classification accuracy. This is extremely desirable for machine learning methods, as increased prior knowledge can decrease the number of ground truth images necessary for training. Finally, we detail the future work for data normalization for more accurate automatic BoM extraction. Index Terms – automatic visual inspection; PCB reverse engineering; PCB competitor analysis; hardware assurance; bill of materials