A linear 60 GHz 65 nm-CMOS power amplifier realization and characterization for OFDM signal

A millimeter-wave Power Amplifier (PA) based on a 65nm CMOS technology from STMicroelectronics has been designed. The targeted feature is the unlicensed band around 60 GHz suitable for wireless personal area network application (WPAN). To optimize the linearity, the PA is designed under class A biasing to have an output compression point (OCP₁) close to its saturated Power (P _sat). S-parameters and large signal measurement results are demonstrated and compared with electromagnetic simulations. The PA offers a P _sat of 8.3 dBm, an OCP₁ of 6 dBm and a gain of 6.7 dB. The die area is 0.29 mm² with pads. Considering those results, one-tone simulations are not sufficient to characterize the linearity performances of the PA in its real conditions of use. Consequently, two-tone simulations are firstly performed. After, linearity figures of merit (FoM) are discussed applying an orthogonal frequency-division multiplexing (OFDM) modulated signal. The PA offers an adjacent channel power ratio (ACPR) of 15 dB and an error vector magnitude (EVM) of 20% at PA compression operating mode.     

Analysis of the musculoskeletal system of the hand and forearm during a cylinder grasping task

Musculoskeletal disorders of the hand are mostly due to repeated or awkward manual tasks in the work environment and are considered a public health issue. To prevent their development, it is necessary to understand and investigate the biomechanical behavior of the musculoskeletal system during the movement. In this study a biomechanical analysis of the upper extremity during a cylinder grasping task is conducted by using a parameterized musculoskeletal model of the hand and forearm. The proposed model is composed of 21 segments, 28 musculotendon units, and 20 joints providing 24 degrees of freedom. Boundary conditions of the model are defined by the three-dimensional coordinates of 43 external markers fixed to bony landmarks of the hand and forearm and tracked with an optoelectronic motion capture system. External marker positions from five healthy participants were used to test the model. A task consisting of closing and opening fingers around a cylinder 25 mm in diameter was investigated. Based on experimental kinematic data, an inverse dynamics process was performed to calculate output data of the model (joint angles, musculotendon unit shortening and lengthening patterns). Finally, based on an optimization procedure, joint loads and musculotendon forces were computed in a forward dynamics simulation. Results of this study assessed reproducibility and consistency of the biomechanical behavior of the musculoskeletal hand system.     

A comparative study of hardware architectures for lightweight block ciphers

A hardware-based performance comparison of lightweight block ciphers is conducted in this paper. The DESL, DESXL, CURUPIRA-1, CURUPIRA-2, HIGHT, PUFFIN, PRESENT and XTEA block ciphers have been employed in this comparison. Our objective is to survey what ciphers are suitable for security in Radio Frequency Identification (RFID) and other security applications with demanding area restrictions. A general architecture option has been followed for the implementation of all ciphers. Specifically, a loop architecture has been used, where one basic round is used iteratively. The basic performance metrics are the area, power consumption and hardware resource cost associated with the implementation resulting throughput of each cipher. The most compact cipher is the 80-bit PRESENT block cipher with a count of 1704 GEs and 206.4 Kbps, while the largest in area cipher is the CURUPIRA-1. The CURUPIRA-1 cipher consumes the highest power of 118.1 μW, while the PRESENT cipher consumes the lowest power of 20 μW. All measurements have been taken at a 100 kHz clock frequency.     

Motorcyclists’ speed and “looked-but-failed-to-see” accidents

Previous research on motorcycle crashes has shown the frequency and severity of accidents in which a non-priority road user failed to give way to an approaching motorcyclist without seeing him/her, even though the road user had looked in the approaching motorcycle's direction and the motorcycle was visible. These accidents are usually called “looked-but-failed-to-see” (LBFS) accidents. This article deals with the effects that the motorcyclist's speed has in these accidents. It is based on the in-depth study and precise kinematic reconstruction of 44 accident cases involving a motorcyclist and another road user, all occurring in intersections. The results show that, in urban environments, the initial speeds of motorcyclists involved in “looked-but-failed-to-see” accidents are significantly higher than in other accidents at intersections. In rural environments, the difference in speed between LBFS accidents and other accidents is not significant, but further investigations would be necessary to draw any conclusions. These results suggest that speed management, through road design or by other means, could contribute to preventing “looked-but-failed-to-see” motorcycle accidents, at least in urban environments.     

DETONATION CLASSIFICATION FROM ACOUSTIC SIGNATURE WITH THE RESTRICTED BOLTZMANN MACHINE

We compare the recently proposed Discriminative Restricted Boltzmann Machine (DRBM) to the classical Support Vector Machine (SVM) on a challenging classification task consisting in identifying weapon classes from audio signals. The three weapon classes considered in this work (mortar, rocket, and rocket‐propelled grenade), are difficult to reliably classify with standard techniques because they tend to have similar acoustic signatures. In addition, specificities of the data available in this study make it challenging to rigorously compare classifiers, and we address methodological issues arising from this situation. Experiments show good classification accuracy that could make these techniques suitable for fielding on autonomous devices. DRBMs appear to yield better accuracy than SVMs, and are less sensitive to the choice of signal preprocessing and model hyperparameters. This last property is especially appealing in such a task where the lack of data makes model validation difficult.     

Modelling concentration gradients in fed-batch cultivations of E. coli – towards the flexible design of scale-down experiments

The cover image is based on the Research Article Modelling concentration gradients in fed-batch cultivations of E. coli - towards the flexible design of scale-down experiments by Emmanuel Anane et al., DOI: 10.1002/jctb.5798 .

     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.