A general adaptive unsupervised feature selection with auto-weighting.

Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required to obtain labels for data in the real world. Most of the existing embedded UFS utilize a sparse projection matrix for FS.

Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications.

For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities.

Contrast in Haze Removal: Configurable Contrast Enhancement Model Based on Dark Channel Prior.

Conventional haze-removal methods are designed to adjust the contrast and saturation, and in so doing enhance the quality of the reconstructed image. Unfortunately, the removal of haze in this manner can shift the luminance away from its ideal value. In other words, haze removal involves a tradeoff between luminance and contrast.

Quantification assessment of bradykinesia in Parkinson's disease based on a wearable device.

Bradykinesia is one of the primary characteristic symptoms of Parkinson's disease (PD). Ten-second whole-hand-grasps action was chosen to assess bradykinesia severity in this study. A quantification assessment system based on a self-developed wearable device was proposed to assess the severity of the parkinsonian bradykinesia.

An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation.

Microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally.

Run-length chain coding and scalable computation of a shape's moments using reconfigurable optical buses.

The main contribution of this paper is the design of several efficient algorithms for modified run-length chain coding and for computing a shape's moments on arrays with reconfigurable optical buses. The proposed algorithms are based on the boundary representation of an object. Instead of using chain code, the boundary can be represented by a modified run-length chain code, where each entity represents a line segment (two adjacent corner pixels).

Parallel algorithms for arbitrary dimensional Euclidean distance transforms with applications on arrays with reconfigurable optical buses.

In this paper, we present algorithms for computing the Euclidean distance transform (EDT) of a binary image on the array with reconfigurable optical buses (AROB). First, we develop a parallel algorithm termed as Algorithm Expander which can be implemented in O(1) time on an AROB with N x Ndelta processors, where delta = 1/k, k is a constant and a positive integer. Algorithm Expander is designed to compute a higher dimensional EDT based on the computed lower dimensional EDT.