A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices.

In this work, we propose a novel metaheuristic algorithm that evolved from a conventional particle swarm optimization (PSO) algorithm for application in miniaturized devices and systems that require low energy consumption. The modifications allowed us to substantially reduce the computational complexity of the PSO algorithm, translating to reduced energy consumption in hardware implementation. This is a paramount feature in the devices used, for example, in wireless sensor networks (WSNs) or wireless body area sensors (WBANs), in which particular devices have limited access to a power source.

Impact of Hepatic Artery Variations and Reconstructions on the Outcome of Orthotopic Liver Transplantation.

Background: Donor variational arteries often require complex reconstruction.

Methods: We analysed the incidence of different variations, types of arterial reconstructions and their impact on post-operative results from 409 patients undergoing liver transplantation at Karolinska Institute between 2007 and 2015.

Results: A total of 292 (71.

First European Case of Simultaneous Liver and Pancreas Transplantation as Treatment of Wolcott-Rallison Syndrome in a Small Child.

Background: The concept of organ transplantation as treatment for complex genetic conditions, including Wolcott-Rallison syndrome (WRS), continues to show promise. Liver transplantation is essential for survival of patients with WRS, and pancreas transplantation cures their type I diabetes mellitus.

Methods: The recipient, a 3-year-old girl weighing 14 kg at the time of transplantation, suffered from major complications of WRS, including repetitive liver failure episodes and poorly controlled diabetes.

Novel techniques for a wireless motion capture system for the monitoring and rehabilitation of disabled persons for application in smart buildings.

Background: Due to the problem of aging societies, there is a need for smart buildings to monitor and support people with various disabilities, including rheumatoid arthritis.

Objective: The aim of this paper is to elaborate on novel techniques for wireless motion capture systems for the monitoring and rehabilitation of disabled people for application in smart buildings.

Methods: The proposed techniques are based on cross-verification of distance measurements between markers and transponders in an environment with highly variable parameters.

Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology.

This paper presents a programmable analog current-mode circuit used to calculate the distance between two vectors of currents, following two distance measures. The Euclidean (L2) distance is commonly used. However, in many situations, it can be replaced with the Manhattan (L1) one, which is computationally less intensive, whose realization comes with less power dissipation and lower hardware complexity.

Parallel programmable asynchronous neighborhood mechanism for Kohonen SOM implemented in CMOS technology.

We present a new programmable neighborhood mechanism for hardware implemented Kohonen self-organizing maps (SOMs) with three different map topologies realized on a single chip. The proposed circuit comes as a fully parallel and asynchronous architecture. The mechanism is very fast.

A programmable triangular neighborhood function for a Kohonen self-organizing map implemented on chip.

An efficient transistor level implementation of a flexible, programmable triangular function (TF) that can be used as a triangular neighborhood function (TNF) in ultra-low power, self-organizing maps (SOMs) realized as application-specific integrated circuit (ASIC) is presented. The proposed TNF block is a component of a larger neighborhood mechanism, whose role is to determine the distance between the winning neuron and all neighboring neurons. Detailed simulations carried out for the software model of such network show that the TNF forms a good approximation of the gaussian neighborhood function (GNF), while being implemented in a much easier way in hardware.