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Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors’ computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.

Remote sensing images are usually contaminated by cloud and corresponding shadow regions, making cloud and shadow detection one of the essential prerequisites for processing and translation of remote sensing images. Edge-precise cloud and shadow segmentation remains challenging due to the inherent high-level semantic acquisition of current neural segmentation fashions. We, therefore, introduce the Refined UNet series to partially achieve edge-precise cloud and shadow detection, including two-stage Refined UNet, v2 with a potentially efficient gray-scale guided Gaussian filter-based CRF, and v3 with an efficient multi-channel guided Gaussian filter-based CRF. However, it is visually demonstrated that the locally linear kernel used in v2 and v3 is not sufficiently sensitive to potential edges in comparison with Refined UNet. Accordingly, we turn back to the investigation of an end-to-end UNet-CRF architecture with a Gaussian-form bilateral kernel and its relatively efficient approximation. In this paper, we present Refined UNet v4, an end-to-end edge-precise segmentation network for cloud and shadow detection, which is capable of retrieving regions of interest with relatively tight edges and potential shadow regions with ambiguous edges. Specifically, we inherit the UNet-CRF architecture exploited in the Refined UNet series, which concatenates a UNet backbone of coarsely locating cloud and shadow regions and an embedded CRF layer of refining edges. In particular, the bilateral grid-based approximation to the Gaussian-form bilateral kernel is applied to the bilateral message-passing step, in order to ensure the delineation of sufficiently tight edges and the retrieval of shadow regions with ambiguous edges. Our TensorFlow implementation of the bilateral approximation is relatively computationally efficient in comparison with Refined UNet, attributed to the straightforward GPU acceleration. Extensive experiments on Landsat 8 OLI dataset illustrate that our v4 can achieve edge-precise cloud and shadow segmentation and improve the retrieval of shadow regions, and also confirm its computational efficiency.

Quantum based security solutions for low resource platforms. Learn and prepare for breaches with a pre-emptive stance and not just when there is an imminent threat.

Wireless Body Area Networks (WBANs) is a revolutionary achievement in the field of health services. The field is envisioned to play an important role in medical, psychological, and even in non-medical applications. Different routing protocols are being designed in WBAN to enhance its performance, focusing on delay, energy efficiency, throughput, and network lifetime. Line-of-sight (LoS) and Non-Line-of-Sight (NLoS) communications and clustering are the least focused areas in the literature. This paper presents a routing protocol called Dual Sink approach using Clustering in Body Area Network (DSCB) which is more reliable in terms of network stability, and energy efficient in comparison to its counterparts. This protocol enhances network life-time by introducing the concept of clustering while using two sink nodes. The proposed scheme is compared with existing protocols named as SIMPLE and DARE. The cost function is established for selection of forwarder node, based on nodal distance from the sink, residual energy, and transmission power. Simulation results depict that the proposed protocol achieves significant performance improvement in network throughput by attaining 55% and 22% more efficient results compared to SIMPLE and DARE respectively. Furthermore, the results show improved performance of DSCB protocol, in terms of network stability and end-to-end delay performance metrics.

In the existing literature about cognitive radio opportunistic relaying (CR-OR) systems, the first-order statistics such as outage probability are investigated widely. However, for the second-order statistics, such as average outage duration (AOD) and average outage rate (AOR), there is not open works, still. To obtain a comprehensive cognition on the behaviour of mobile communication systems, this study focuses on the second-order statistical properties of CR-OR systems. There are two CR-OR schemes considered, in which the canonical amplify-and-forward (AF) and reactive decode-and-forward (RDF) are employed, respectively. Since the equivalent end-to-end signal-to-noise ratio (SNR) of AF CR-OR is complicated such that it is very difficult to obtain the closed-form solution to AOR of AF CR-OR schemes, the high SNR approximation in AF CR-OR schemes is employed. For the two schemes, first the closed-form solutions to AORs and AODs are obtained by using appropriate mathematical proof. Based on the derivations, the comparison analyses about AORs and AODs of the two schemes are provided. The comparison results show that, under high SNR approximation, the AF CR-OR scheme achieves the same AOR and AOD as RDF CR-OR. Finally, the impact of system parameters on AORs and AODs is provided.

The closed-form results for delay and capacity in mobile ad hoc networks are important for the performance analysis of different transmission protocols. Most existing works focus on independent and identically distributed mobility model, which is always regarded as an idealized global model. In this paper, we extend the investigation to the random walk model, which characterizes practical situations more accurately. Some local movements cause a series of complicated probabilistic problem, we develop a method to calculate the meeting probability between two randomly selected nodes under random walk mobility model. Targeting at the most commonly used routing schemes which are modeled by 2HR- f algorithm, we obtain the closed-form solutions for delay and capacity, where the wireless interference and medium access contention among nodes are considered. Extensive simulations demonstrate the accuracy of our theoretical results.

In this study, the authors consider molecular communication between two nanomachines placed in a fluid medium for three different amplitude modulation schemes. The number of molecules transmitted represents the amplitude levels for these schemes. Each molecule released by the transmitter travels with Brownian motion and a positive drift to reach the receiver nanomachine. They consider a time slotted channel, where the information in every slot is corrupted by stray molecules from the previous slots. The capacity of such a molecular communication channel is investigated for all the three modulation schemes. Analytical expressions for the end-to-end symbol error probability are derived, considering maximum likelihood detection at the receiver. Numerical results indicate that arbitrarily low probabilities of error can be achieved for high drift velocities. An increase in the slot length further improves the performance, albeit at the cost of data rate. The results also demonstrate the improvements offered by the amplitude modulation schemes over the previously proposed time modulation schemes.

In this study, the authors evaluate the performance of space diversity systems with the Nth-best antenna selection scheme in the presence of interference and outdated channel information (OC1). The Nth-best antenna selection scheme is efficient in situations where the second or even the Nth best antenna is mistakenly selected by the destination instead of the first best antenna for data reception. In this study, they first derive the cumulative distribution function (CDF) of the end-to-end (e2e) signal-to-interference plus noise ratio at the selection scheme combiner output. This CDF is then used to derive exact closed-form expressions for the e2e outage probability and symbol error probability (SEP) of the system. In the analysis, the channels of the desired user and the interferers are assumed to follow Rayleigh distribution. Furthermore, to obtain more about system insights, the performance is studied at the high signal-to-noise ratio (SNR) regime where the diversity order and coding gain are derived. Monte Carlo simulations are provided to validate the derived analytical and asymptotic expressions. Main results illustrate that with an interference power that is not scaling with SNR, the system can still achieve diversity gain when more receive antennas are used. Also, findings show that the diversity order of the system is linearly decreasing with increasing the order of the antenna, and linearly increasing with decreasing it. Furthermore, results illustrate that as the higher the correlation coefficient between the SNRs of antennas at the selection and transmission time instances, the better the achieved performance.