In image retrieval systems, the use of multiple embedding models to capture diverse visual features often improves retrieval performance. However, traditional rank aggregation methods generally assume uniform reliability across retrieval models, neglecting the variability in model confidence across different queries. This paper introduces SkewFuse, an unsupervised rank fusion approach that dynamically weights retrieval results based on statistical skewness of similarity score distributions, effectively capturing query-specific confidence. We extend the Dowdall positional voting method by integrating these confidence weights, achieving adaptive and robust fusion. Evaluations conducted on the In-Shop Clothes Retrieval dataset demonstrate that SkewFuse consistently outperforms existing unsupervised and supervised fusion methods, notably improving early precision metrics, including a 4.7% relative gain in Recall@1 over the strongest baseline. SkewFuse offers an efficient, parameter-light solution ideal for real-world applications where top-ranked retrieval precision is critical.

Novel common-mode feedback circuits are proposed for use in pseudo-differential switched-capacitor circuits. They can be implemented by incorporating just four additional capacitors (and switches) for an integrator, and only two additional capacitors for a residue gain amplifier. The circuits are applicable to very low-voltage switched-capacitor stages realized in submicron low-voltage CMOS processes.

A voice signal contains the psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word uttered by different speakers can be very different. In this paper, two theories are developed by considering two optimization criteria applied to both the training set and the test set. The first theory is well known and uses what is called Criterion 1 here and ends up with the average of all vectors belonging to the words in the training set. The second theory is a novel approach and uses what is called Criterion 2 here, and it is used to extract the common properties of all vectors belonging to the words in the training set. It is shown that Criterion 2 is superior to Criterion 1 when the training set is of concern. In Criterion 2, the individual differences are obtained by subtracting a reference vector from other vectors, and individual difference vectors are used to obtain orthogonal vector basis by using the Gram-Schmidt orthogonalization method. The common vector is obtained by subtracting projections of any vector of the training set on the orthogonal vectors from this same vector. It is proved that this common vector is unique for any word class in the training set and independent of the chosen reference vector. This common vector is used in isolated word recognition, and it is also shown that Criterion 2 is superior to Criterion 1 for the test set. From the theoretical and experimental study, it is seen that the recognition rates increase as the number of speakers in the training set increases. This means that the common vector obtained from Criterion 2 represents the common properties of a spoken word better than the common or average vector obtained from Criterion 1.