$K^4$: Online Log Anomaly Detection Via Unsupervised Typicality Learning

Existing Log Anomaly Detection (LogAD) methods are often slow, dependent on error-prone parsing, and use unrealistic evaluation protocols. We introduce $K^4$, an unsupervised and parser-independent framework for high-performance online detection. $K^4$ transforms arbitrary log embeddings into compact four-dimensional descriptors (Precision, Recall, Density, Coverage) using efficient k-nearest neighbor (k-NN) statistics. These descriptors enable lightweight detectors to accurately score anomalies without retraining. Using a more realistic online evaluation protocol, $K^4$ sets a new state-of-the-art (AUROC: 0.995-0.999), outperforming baselines by large margins while being orders of magnitude faster, with training under 4 seconds and inference as low as 4 $\mu$s.

Real time ridge orientation estimation for fingerprint images

Fingerprint verification is an important bio-metric technique for personal identification. Most of the automatic verification systems are based on matching of fingerprint minutiae. Extraction of minutiae is an essential process which requires estimation of orientation of the lines in an image. Most of the existing methods involve intense mathematical computations and hence are performed through software means. In this paper a hardware scheme to perform real time orientation estimation is presented which is based on pipelined architecture. Synthesized circuits proved the functionality and accuracy of the suggested method.