Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

Microfluidic inverse phase ELISA via manipulation of magnetic beads

We report a new technique for conducting immuno-diagnostics on a microfluidic platform. Rather than handling fluid reagents against a stationary solid phase, the platform manipulates analyte-coated magnetic beads through stationary plugs of fluid reagents to detect an antigenic analyte. These isolated but accessible plugs are pre-encapsulated in a microchannel by capillary force. We call this platform microfluidic inverse phase enzyme-linked immunosorbent assay (μIPELISA). μIPELISA has distinctive advantages in the family of microfluidic immunoassay. In particular, it avoids pumping and valving fluid reagents during assaying, thus leading to a lab-on-a-chip format that is free of instrumentation for fluid actuation and control. We use μIPELISA to detect digoxigenin-labeled DNA segments amplified from E. coli O157:H7 by polymerase chain reaction (PCR), and compare its detection capability with that of microplate ELISA. For 0.259 ng μl⁻¹ of digoxigenin-labeled amplicon, μIPELISA is as responsive as the microplate ELISA. Also, we simultaneously conduct μIPELISA in two parallel microchannels.     

A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

In this paper, we consider a Hopfield like Chaotic Neural Networks which have both self-coupling and non-invertible activation functions. We show that the interactions between neurons can be used as a means of chaos generation or suppression to neuron’s outputs when more adaptability or stability is required. Furthermore, a new set of sufficient conditions based on coupling weights is proposed so that the synchronization of all neuron’s outputs with each other is guaranteed, when all neuron’s have identical activation functions. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on three illustrative examples.     

Controlled gates for multi-level quantum computation

Multi-level (ML) quantum logic can potentially reduce the number of inputs/outputs or quantum cells in a quantum circuit which is a limitation in current quantum technology. In this paper we propose theorems about ML-quantum and reversible logic circuits. New efficient implementations for some basic controlled ML-quantum logic gates, such as three-qudit controlled NOT, Cycle, and Self Shift gates are proposed. We also propose lemmas about r-level quantum arrays and the number of required gates for an arbitrary n-qudit ML gate. An equivalent definition of quantum cost (QC) of binary quantum gates for ML-quantum gates is introduced and QC of controlled quantum gates is calculated.     

Analysis of frequency‐selective impedance loading of transmission lines for dual‐band couplers

This article proposes an analytical design methodology for dual‐band hybrid couplers and baluns structures for any arbitrary frequency ratio using a stub‐loaded transmission line. An analysis of changing the impedance behavior of the stub, is carried out for the two bands of operation, which along with a dispersive analysis, emphasizes certain conditions where the existing methodology is not applicable. In addition, an extra degree of freedom has been included to increase the solutions for a given frequency ratio, thus providing greater flexibility and feasibility of the proposed structure. The design methodology is experimentally validated with the design and fabrication of dual‐band branch‐line and rat‐race couplers for various commercial frequency bands. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

An ant swarm-inspired energy-aware routing protocol for wireless ad-hoc networks

Primitive routing protocols for ad-hoc networks are “power hungry” and can therefore consume considerable amount of the limited amount of battery power resident in the nodes. Thus, routing in ad-hoc networks is very much energy-constrained. Continuous drainage of energy degrades battery performance as well. If a battery is allowed to intermittently remain in an idle state, it recovers some of its lost charge due to the charge recovery effect, which, in turn, results in prolonged battery life.In this paper, we use the ideas of naturally occurring ants’ foraging behavior (Dorigo and Stuetzle, 2004) [1] and based on those ideas, we design an energy-aware routing protocol, which not only incorporates the effect of power consumption in routing a packet, but also exploits the multi-path transmission properties of ant swarms and, hence, increases the battery life of a node. The efficiency of the protocol with respect to some of the existing ones has been established through simulations. It has been observed that the energy consumed in the network, the energy per packet in the case of EAAR are 60% less compared to MMBCR and the packets lost is only around 12% of what we have in AODV, in mobility scenarios.     

Adaptive popularity-driven replica placement in hierarchical data grids

Data grids support access to widely distributed storage for large numbers of users accessing potentially many large files. Efficient access is hindered by the high latency of the Internet. To improve access time, replication at nearby sites may be used. Replication also provides high availability, decreased bandwidth use, enhanced fault tolerance, and improved scalability. Resource availability, network latency, and user requests in a grid environment may vary with time. Any replica placement strategy must be able to adapt to such dynamic behavior. In this paper, we describe a new dynamic replica placement algorithm, Popularity Based Replica Placement (PBRP), for hierarchical data grids which is guided by file “popularity”. Our goal is to place replicas close to clients to reduce data access time while still using network and storage resources efficiently. The effectiveness of PBRP depends on the selection of a threshold value related to file popularity. We also present Adaptive-PBRP (APBRP) that determines this threshold dynamically based on data request arrival rates. We evaluate both algorithms using simulation. Results for a range of data access patterns show that our algorithms can shorten job execution time significantly and reduce bandwidth consumption compared to other dynamic replication methods.     

RUSHES—an annotation and retrieval engine for multimedia semantic units

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. In this paper, we present a summary of the most relevant achievements of the RUSHES project, focusing on specific approaches for automatic annotation as well as the main features of the final RUSHES search engine.     

Fuzzy grey relational analysis for software effort estimation

Accurate and credible software effort estimation is a challenge for academic research and software industry. From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach. Accuracy of such a model depends on the characteristics of the dataset, which is subject to considerable uncertainty. The inherent uncertainty in software attribute measurement has significant impact on estimation accuracy because these attributes are measured based on human judgment and are often vague and imprecise. To overcome this challenge we propose a new formal EA model based on the integration of Fuzzy set theory with Grey Relational Analysis (GRA). Fuzzy set theory is employed to reduce uncertainty in distance measure between two tuples at the k ^th continuous feature ( | ( x_o(k) - x_i(k) | ) \left( {\left| {\left( {{x_o}(k) - {x_i}(k)} \right.} \right|} \right) .GRA is a problem solving method that is used to assess the similarity between two tuples with M features. Since some of these features are not necessary to be continuous and may have nominal and ordinal scale type, aggregating different forms of similarity measures will increase uncertainty in the similarity degree. Thus the GRA is mainly used to reduce uncertainty in the distance measure between two software projects for both continuous and categorical features. Both techniques are suitable when relationship between effort and other effort drivers is complex. Experimental results showed that using integration of GRA with FL produced credible estimates when compared with the results obtained using Case-Based Reasoning, Multiple Linear Regression and Artificial Neural Networks methods.     

Hardware/software co-design of a real-time kernel based tracking system

The probabilistic visual tracking methods using color histograms have been proven to be robust to target model variations and background illumination changes as shown by the recent research. However, the required computational cost is high due to intensive image data processing. The embedded solution of such algorithms become challenging due to high computational power demand and algorithm complexity. This paper presents a hardware/software co-design architecture for implementation of the well-known kernel based mean shift tracking algorithm. The design uses color histogram of the target as tracking feature. The target is searched in the consecutive images by maximizing the statistical match of the color distributions. The target localization is based on gradient based iterative search instead of exhaustive search which makes the system capable of achieving frame rate up to hundreds of frames per second while tracking multiple targets. The design, which is fully standalone, is implemented on a low-cost medium-size field programmable gate array (FPGA) device. The hardware cost of the design is compared with some other tracking systems. The performance of the system in terms of speed is evaluated and compared with the software based implementation. It is expected that the proposed solution will find its utility in applications like embedded automatic video surveillance systems.