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Weaknesses have recently been found in the widely used cryptographic hash functions SHA-1 and MD5. A potential alternative for these algorithms is the Whirlpool hash function, which has been standardized by ISO/IEC and evaluated in the European research project NESSIE. In this paper we present a Whirlpool hashing hardware core suited for devices in which low cost is desired. The core constitutes of a novel 8-bit architecture that allows compact realizations of the algorithm. In the Xilinx Virtex-II Pro XC2VP40 FPGA, our implementation consumes 376 slices and achieves the throughput of 81.5 Mbit/s. The resource utilization of our design is one fourth of the smallest Whirlpool implementation presented to date.

Low energy consumption and load balancing are required for enhancing lifetime at Wireless Sensor Networks (WSN). In addition, network dynamics and different delay, throughput, and reliability requirements demand costaware traffic adaptation. This paper presents a novel capacity optimization algorithm targeted at locally synchronized, low-duty cycle WSN MACs. The algorithm balances the traffic load between contention and contention free channel access. The energy-inefficient contention access is avoided, whereas the more reliable contention free access is preferred. The algorithm allows making cost-aware trade-off between delay, energy-efficiency, and throughput guided by routing layer. Analysis results show that the algorithm has 10% to 100% better energy-efficiency than IEEE 802.15.4 LR-WPAN in a typical sensing application, while providing comparable goodput and delay.

Abstract-IP-XACT is a standard for describing intellectual property metadata for System-on-Chip (SoC) integration. Recently researchers have proposed visualizing and abstracting IP-XACT objects using structural UML2 model elements and diagrams. Despite the number of proposals at conceptual level, experiences on utilizing this representation in practical SoC development environments are very limited. This paper presents how UML2 models of IP-XACT features can be utilized to efficiently design and implement a multiprocessor SoC prototype on FPGA. The main contribution of this paper is the experimental development of a multiprocessor platform on FPGA using UML2 design capture, IP-XACT compatible components, and design automation tools. In addition, modeling concepts are improved from earlier work for the utilized integration methodology.

Registration-based methods are commonly used in the anatomical segmentation of magnetic resonance (MR) brain images. However, they are sensitive to the presence of deforming brain pathologies that may interfere with the alignment of the atlas image with the target image. Our goal was to develop an algorithm for automated segmentation of the normal and injured rat hippocampus. We implemented automated segmentation using a U-Net-like Convolutional Neural Network (CNN). of sham-operated experimental controls and rats with lateral-fluid-percussion induced traumatic brain injury (TBI) on MR images and trained ensembles of CNNs. Their performance was compared to three registration-based methods: single-atlas, multi-atlas based on majority voting and Similarity and Truth Estimation for Propagated Segmentations (STEPS). Then, the automatic segmentations were quantitatively evaluated using six metrics: Dice score, Hausdorff distance, precision, recall, volume similarity and compactness using cross-validation. Our CNN and multi-atlas -based segmentations provided excellent results (Dice scores > 0.90) despite the presence of brain lesions, atrophy and ventricular enlargement. In contrast, the performance of singe-atlas registration was poor (Dice scores < 0.85). Unlike registration-based methods, which performed better in segmenting the contralateral than the ipsilateral hippocampus, our CNN-based method performed equally well bilaterally. Finally, we assessed the progression of hippocampal damage after TBI by applying our automated segmentation tool. Our data show that the presence of TBI, time after TBI, and whether the location of the hippocampus was ipsilateral or contralateral to the injury explained hippocampal volume (p=0.029, p< 0.001, and p< 0.001 respectively).