The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

Radio frequency identification (RFID) is a rapidly developing technology, and RFID sensors have become important components in many common technology applications. The passive ultra-high frequency (UHF) tags used in RFID sensors have a higher data transfer rate and longer read range and usually come in unique small and portable application designs. However, these tags suffer from significant frequency interference when mounted on metallic materials or placed near liquid surfaces. This paper presents the recent advancements made in passive UHF-RFID tag designs proposed to resolve the interference problems. We focus on those designs that are intended to improve antenna read range as well as scalability designs for miniaturized applications.

A Radio mean labeling of a connected graph G is an injective function h from the vertex set, V(G), to the set of natural numbers N such that for any two distinct vertices x and y of G, ⌈(h(x)+h(y))/2⌉ ≥ diam + 1 - d(x,y). The radio mean number of h, rmn(h), is the maximum number assigned to any vertex of G. The radio mean number of G, rmn(G), is the minimum value of rmn(h), taken over all radio mean labeling h of G. This work has three contributions. The first one is proving two theorems which find the radio mean number for cycles and paths. The second contribution is proposing an approximate algorithm which finds an upper bound for radio mean number of a given graph. The third contribution is that we introduce a novel integer linear programing formulation for the radio mean problem. Finally, the experimental results analysis and statistical test proved that the Integer Linear Programming Model overcame the proposed approximate algorithm according to CPU time only. On the other hand, both the Integer Linear Programming Model and the proposed approximate algorithm had the same upper bound of the radio mean number of G.

We investigate a novel nonlinear consensus from the extreme points of doubly stochastic quadratic operators (EDSQO), based on majorization theory and Markov chains for time-varying multi-agent distributed systems. We describe a dynamic system that has a local interaction network among agents. EDSQO has been applied for distributed agent systems, on a finite dimensional stochastic matrix. We prove that multi-agent systems converge at a center (common value) via the extreme waited value of doubly stochastic quadratic operators (DSQO), which are only 1 or 0 or 1/2$\frac{1}{2}$ if the exchanges of each agent member has no selfish communication. Applying this rule means that the consensus is nonlinear and low-complexity computational for fast time convergence. The investigated nonlinear model of EDSQO follows the structure of the DeGroot linear (DGL) consensus model. However, EDSQO is nonlinear and faster convergent than the DGL model and is of lower complexity than DSQO and cubic stochastic quadratic operators (CSQO). The simulation result and theoretical proof are illustrated.

This article explores nonlinear convergence to limit the effects of the consensus problem that usually occurs in multi-agent systems. Most of the existing research essentially considers the outline of linear protocols, using complex mathematical equations in various orders. In this work, however, we designed and developed an alternative nonlinear protocol based on simple and effective mathematical approaches. The designed protocol in this sense was modified from the Doubly Stochastic Quadratic Operators (DSQO) and was aimed at resolving consensus problems. Therefore, we called it Modified Doubly Stochastic Quadratic Operators (MDSQO). The protocol was derived in the context of coordinated systems to overcome the consensus issue related to multi-agent systems. In the process, we proved that by using the proposed nonlinear protocol, the consensus could be reached via a common agreement among the agents (average consensus) in a fast and easy fashion without losing any initial status. Moreover, the investigated nonlinear protocol of MDSQO realized the reaching consensus always as well as DSQO in some cases, which could not reach consensus. Finally, simulation results were given to prove the validity of the theoretical analysis.

This paper evaluates the limit behavior for symmetry interactions networks of set points for nonlinear mathematical models. Nonlinear mathematical models are being increasingly applied to most software and engineering machines. That is because the nonlinear mathematical models have proven to be more efficient in processing and producing results. The greatest challenge facing researchers is to build a new nonlinear model that can be applied to different applications. Quadratic stochastic operators (QSO) constitute such a model that has become the focus of interest and is expected to be applicable in many biological and technical applications. In fact, several QSO classes have been investigated based on certain conditions that can also be applied in other applications such as the Extreme Doubly Stochastic Quadratic Operator (EDSQO). This paper studies the behavior limitations of the existing 222 EDSQ operators on two-dimensional simplex (2DS). The created simulation graph shows the limit behavior for each operator. This limit behavior on 2DS can be classified into convergent, periodic, and fixed.