Review of bio-particle manipulation using dielectrophoresis

During the last decade, large and costly instruments are being replaced by system based on microfluidic devices. Microfluidic devices hold the promise of combining a small analytical laboratory onto a chip-sized substrate to identify, immobilize, separate, and purify cells, bio-molecules, toxins, and other chemical and biological materials. Compared to conventional instruments, microfluidic devices would perform these tasks faster with higher sensitivity and efficiency, and greater affordability. Dielectrophoresis is one of the enabling technologies for these devices. It exploits the differences in particle dielectric properties to allow manipulation and characterization of particles suspended in a fluidic medium. Particles can be trapped or moved between regions of high or low electric fields due to the polarization effects in non-uniform electric fields. By varying the applied electric field frequency, the magnitude and direction of the dielectrophoretic force on the particle can be controlled. Dielectrophoresis has been successfully demonstrated in the separation, transportation, trapping, and sorting of various biological particles.Singapore-MIT Alliance (SMA

Intelligent Graph Convolutional Neural Network for Road Crack Detection

This paper presents a novel intelligent system based on graph convolutional neural networks to study road crack detection in intelligent transportation systems. The visual features of the input images are first computed using the well-known Scale-Invariant Feature Transform (SIFT) extraction algorithm. Then, a correlation between SIFT features of similar images is analyzed and a series of graphs are generated. The graphs are trained on a graph convolutional neural network, and a hyper-optimization algorithm is developed to supervise the training process. A case study of road crack detection data is analyzed. The results show a clear superiority of the proposed framework over state-of-the-art solutions. In fact, the precision of the proposed solution exceeds 70%, while the precision of the baseline methods does not exceed 60%.acceptedVersio

Sensor data fusion for the industrial artificial intelligence of things

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.publishedVersio

OBLIQUE PROJECTION METHODS FOR LARGE-SCALE MODEL-REDUCTION

Published versio

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutions.publishedVersio

X-ray imaging with amorphous silicon active matrix flat-panel imagers (AMFPIs)

Recent advances in thin-film electronics technology have opened the way for the use of flat-panel imagers in a number of medical imaging applications. These novel imagers offer real time digital readout capabilities ( ∼ 30(∼30 frames per second), radiation hardness (>106 cGy),(>106cGy), large area (30×40 cm2)(30×40cm2) and compactness ( ∼ 1 cm).(∼1cm). Such qualities make them strong candidates for the replacement of conventional x-ray imaging technologies such as film-screen and image intensifier systems. In this report, qualities and potential of amorphous silicon based active matrix flat-panel imagers are outlined for various applications such as radiation therapy, radiography, fluoroscopy and mammography. © 1997 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87833/2/241_1.pd

Organometallic nucleoside analogues: effect of hydroxyalkyl linker length on cancer cell line toxicity

A new series of chiral ferrocene derivatives containing both a hydroxyalkyl group and a thyminyl group on one cyclopentadienyl ring have been synthesised to probe structure–activity relationships in cancer cell line cytotoxicities. The stereoisomers of enantiomeric pairs of these so-called ferronucleosides have been studied and characterised by a combination of chiral analytical HPLC and single-crystal X-ray diffraction. Biological activity studies revealed that changing the length of the hydroxyalkyl group had marked effects on IC50 values, with compounds having shorter arms that more closely resemble endogenous nucleosides exhibiting lower cytotoxicities. The lipophilicities and electrochemical properties of this compound series have been studied to rationalise these trends and indicate future directions of study

Identifying the Independent Inertial Parameter Space of Robot Manipulators

This paper presents a new approach to the problem of finding the minimum number of inertial parameters of robot manipulator dynamic equations of motion. Based upon the energy difference equation, it is equally applica ble to serial link manipulators as well as graph structured manipulators. The method is conceptually simple, compu tationally efficient, and easy to implement. In particular, the manipulator kinematics and the joint positions and velocities are the only inputs to the algorithm. Applica tions to a serial link and a graph structured manipulator are illustrated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67982/2/10.1177_027836499101000606.pd