Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

3D model retrieval using principal plane analysis and dynamic programming

Three dimensional models play an important role in many applications; the problem is how to select the appropriate models from a 3D database rapidly and accurately. In recent years, a variety of shape representations, statistical methods, and geometric algorithms have been proposed for matching 3D shapes or models. In this paper, we propose a 3D shape representation scheme based on a combination of principal plane analysis and dynamic programming. The proposed 3D shape representation scheme consists of three steps. First, a 3D model is transformed into a 2D image by projecting the vertices of the model onto its principal plane. Second, the convex hall of the 2D shape of the model is further segmented into multiple disjoint triangles using dynamic programming. Finally, for each triangle, a projection score histogram and moments are extracted as the feature vectors for similarity searching. Experimental results showed the robustness of the proposed scheme, which resists translation, rotation, scaling, noise, and destructive attacks. The proposed 3D model retrieval method performs fairly well in retrieving models having similar characteristics from a database of 3D models.     

Production of single and multi-walled carbon nanotubes using natural gas as a precursor compound

In this work, the Catalytic Chemical Vapor Deposition (CCVD) technique was used to synthesize carbon nanotubes (CNT). Natural gas (NG) was employed as a carbon source for the growing of CNT, while magnesium oxide was used as a catalyst support for the nanotubes synthesis. Two systems were utilized. The Fe–Mo/MgO system was obtained by the impregnation technique through the dispersion of iron oxide, which is the catalyst, over magnesia (with molybdenum additions). This system was tested intending to optimize the parameters for the production of single-walled carbon nanotubes (SWCNT). Moreover, Mg_1−x Fe _x MoO₄, which was prepared by the combustion synthesis method, was tested to produce multi-walled carbon nanotubes (MWCNT). The Fe-Mo/MgO tests were carried out under H₂/GN and Ar/GN atmospheres at 950 °C, whereas the Mg_1−x Fe _x MoO₄ was submitted to 1,000 °C under H₂/GN atmosphere. The Fe–Mo/MgO catalyst produced better results regarding number of CNT and their diameters under Ar/NG atmospheres than under H₂/NG atmospheres. The system Mg_1−x Fe _x MoO₄ produced MWCNT according to the expectations.     

Robust pole clustering in a good ride quality region of aircraft for matrices with structured uncertainties

This paper presents a general analysis of robust pole clustering in a good ride quality region (GRQR) of aircraft for matrices with structured uncertainties. This region is an intersection of a ring and a horizontal strip, located in the left half-plane, which is a specific non-Ω-transformable region providing good ride quality of aircraft. The paper applies the Rayleigh principle along the norm theory to analyze robust pole clustering within this region since the generalized Lyapunov theory is not valid for non-Ω-transformable regions. Concerned uncertainties are structured/parametric uncertainties, including interval matrices. The results are useful for robust control analysis and design, especially, of robust good ride quality of aircraft, shuttles, vehicles and space station, as well as some industrial systems. An example of the F-16 dynamics for which GRQR is suitable is included to illustrate the results.     

Localization by immunoelectron microscopy of Schistosoma mansoni antigens in the glomerulus of the hamster (Mesocricetus auratus) kidney

Gene therapy has the potential to provide cancer treatments based on novel mechanisms of action with potentially low toxicities. This therapy may provide more effective control of loco-regional recurrence in diseases such as non-small cell lung cancer (NSCLC), as well as systemic control of micrometastases. Despite current limitations, retroviral and adenoviral vectors can in certain circumstances provide an effective means of delivering therapeutic genes to tumour cells. Although multiple genes are involved in the process of carcinogenesis, mutations of the p53 gene are the most frequent abnormality identified in human tumours. Pre-clinical studies both in vitro and in vivo have shown that restoration of p53 function can induce apoptosis in cancer cells. Phase I clinical trials now show that p53 gene replacement therapy is feasible and safe using both retroviral and adenoviral vectors, and that it induces tumour regression in patients with advanced NSCLC and recurrent head and neck cancer. Other pre-clinical studies indicate that gene therapy may have useful synergy with cytotoxic and radiation therapy. This paper describes the different gene therapy strategies under investigation and the pre-clinical data that provides a rationale for the gene replacement approach, reviews clinical trial data and presents novel ideas for improving current vectors and gene delivery to tumours.     

Blind estimation of the PN sequence in lower SNR DS-SS signals with residual carrier

This paper presents a method of singular value decomposition (SVD) plus digital phase lock loop (DPLL) to solve the difficult problem of blind pseudo-noise (PN) sequence estimation in low signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS, DS) signals with residual carrier. Of course, the method needs to know the parameters of DS signal, such as the period and code rate of PN sequence. Firstly, the received signal is sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. Then, an autocorrelation matrix is computed and accumulated by the signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of this autocorrelation matrix. Furthermore, a DPLL is used to deal with the estimated PN sequence with residual carrier, it estimates and tracks the residual carrier, removes the residual carrier in the end. Theory analysis and computer simulation results show that this method can effectively realize the PN sequence estimation from the input DS signals with residual carrier in lower SNR.     

Register allocation for write activity minimization on non-volatile main memory for embedded systems

Non-volatile memories are good candidates for DRAM replacement as main memory in embedded systems and they have many desirable characteristics. Nevertheless, the disadvantages of non-volatile memory co-exist with its advantages. First, the lifetime of some of the non-volatile memories is limited by the number of erase operations. Second, read and write operations have asymmetric speed or power consumption in non-volatile memory. This paper focuses on the embedded systems using non-volatile memory as main memory. We propose register allocation technique with re-computation to reduce the number of store instructions. When non-volatile memory is applied as the main memory, reducing store instructions will reduce write activities on non-volatile memory. To re-compute the spills effectively during register allocation, a novel potential spill selection strategy is proposed. During this process, live range splitting is utilized to split certain long live ranges such that they are more likely to be assigned into registers. In addition, techniques for re-computation overhead reduction is proposed on systems with multiple functional units. With the proposed approach, the lifetime of non-volatile memory is extended accordingly. The experimental results demonstrate that the proposed technique can efficiently reduce the number of store instructions on systems with non-volatile memory by 33% on average.