Minhua Lu

Bio: Minhua Lu is an academic researcher from Shenzhen University. The author has contributed to research in topics: Imaging phantom & Indentation. The author has an hindex of 16, co-authored 63 publications receiving 952 citations. Previous affiliations of Minhua Lu include Hong Kong Polytechnic University.

Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems

Abstract: With the extensive use of information technology in AAL communication systems, a data management model has recently embodied the 3-V characteristics of big data: volume, velocity, and variety. A lot of work has been done on volume and velocity, but not as much has been reported on variety. To handle the variety of data, universal solutions with acceptable performance are usually much more cost effective than customized solutions. To achieve universality, a basic idea is to first define a universal abstraction that covers a wide range of data types, and then build a universal system for universal abstraction. Traditional database management systems commonly use a multidimensional data type, or feature vectors, as a universal abstraction. However, many new data types in AAL systems cannot be abstracted into multidimensional space. To find a more universal data abstraction and build more universal systems, we propose the concept of big data abstraction, with metric space as a universal abstraction for AAL data types. Furthermore, to demonstrate how metricspace data abstraction works, we survey the state of the art in metric space indexing, a fundamental task in data management. Finally, open research issues are discussed.

High resolution ultrasound elastomicroscopy imaging of soft tissues: system development and feasibility

Abstract: Research in elasticity imaging typically relies on 1-10 MHz ultrasound. Elasticity imaging at these frequencies can provide strain maps with a resolution in the order of millimetres, but this is not sufficient for applications to skin, articular cartilage or other fine structures. We developed a prototype high resolution elastomicroscopy system consisting of a 50 MHz ultrasound backscatter microscope system and a calibrated compression device using a load cell to measure the pressure applied to the specimen, which was installed between a rigidly fixed face-plate and a specimen platform. Radiofrequency data were acquired in a B-scan format (10 mm wide x 3 mm deep) in specimens of mouse skin and bovine patellar cartilage. The scanning resolution along the B-scan plane direction was 50 microm, and the ultrasound signals were digitized at 500 MHz to achieve a sensitivity better than 1 microm for the axial displacement measurement. Because of elevated attenuation of ultrasound at high frequencies, special consideration was necessary to design a face-plate permitting efficient ultrasound transmission into the specimen and relative uniformity of the compression. Best results were obtained using a thin plastic film to cover a specially shaped slit in the face-plate. Local tissue strain maps were constructed by applying a cross-correlation tracking method to signals obtained at the same site at different compression levels. The speed of sound in the tissue specimen (1589.8+/-7.8 m s(-1) for cartilage and 1532.4+/-4.4 m s(-1) for skin) was simultaneously measured during the compression test. Preliminary results demonstrated that this ultrasound elastomicroscopy technique was able to map deformations of the skin and articular cartilage specimens to high resolution, in the order of 50 microm. This system can also be potentially used for the assessment of other biological tissues, bioengineered tissues or biomaterials with fine structures.

When is nearest neighbor meaningful

Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Abstract: U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

Integral transforms and their applications

Abstract: Integral transforms (Laplace, Fourier and Mellin) are introduced with their properties, the relationship between these transforms was discussed and some important applications in physics and engineering were given. ااااااا دقل مت ضارعتسإ ةساردو ل ةيلماكتلا تليوحتلا لك ، سلبل تلوحت نم روف ي ر نيليمو عم ةشقانم كلذكو ،اهنم لك صاوخ و صئاصخ ةقلعلا ةشقانم مت هذه نيب طبرلاو و ،تليوحتلا مت ميدقت تاقيبطتلا ضعب تليوحتلا هذهل ةمهملا يف تلاجم ءايزيفلا ةسدنهلاو.