Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction

Determining and modeling the possible behaviour and actions of molecules requires investigating the basic structural features and physicochemical properties that determine their behaviour during chemical, physical, biological, and environmental processes. Computational approaches such as machine learning methods are alternatives to predicting the physiochemical properties of molecules based on their structures. However, the limited accuracy and high error rates of such predictions restrict their use. In this paper, a novel technique based on a deep learning convolutional neural network (CNN) for the prediction of chemical compounds’ bioactivity is proposed and developed. The molecules are represented in the new matrix format Mol2mat, a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. To evaluate the performance of the proposed methods, a series of experiments were conducted using two standard datasets, namely the MDL Drug Data Report (MDDR) and Sutherland, datasets comprising 10 homogeneous and 14 heterogeneous activity classes. After analysing the eight fingerprints, all the probable combinations were investigated using the five best descriptors. The results showed that a combination of three fingerprints, ECFP4, EPFP4, and ECFC4, along with a CNN activity prediction process, achieved the highest performance of 98% AUC when compared to the state-of-the-art ML algorithms NaiveB, LSVM, and RBF

Parallel Implementation of Kvazaar HEVC on Multicore ARM Processor

International audience—The emergence of the new standard HEVC (High Efficiency Video Coding) is accompanied with serious problems related to resource consumption and encoding time. The proposal of new tools and optimizations is strongly recommended to ensure the integration of this new encoder in various platforms and multimedia applications. In this context, Kvazaar HEVC encoder is introduced to overcome the problems related to HEVC test model (HM) reference software. This academic open-source is tailored to fit the programmer's needs by enabling high-level parallel processing. In this context, this paper presents different parallel implementations of the Kvazaar HEVC encoder on a powerful Octa-core CubieBoard4 platform including two quad-core ARM A7 and ARM A15 for efficient power and high performance in a single chip. A performance comparison of different parallelization strategies is performed. For single-threaded implementation, experimental results show that the high speed preset (RD1) can save up to 48% and 91% of encoding time for Random Access (RA) and All-Intra (AI) configurations respectively. When moving to multi-threaded implementation, time saving is about 65% to 75% for AI configuration. Moreover, experiments show that Wavefront Parallel Processing (WPP) outperforms tiles-level parallelization in terms of encoding speed without inducing video quality degradation or bitrate increase

FPGA implementation of a wireless sensor node with a built-in ADALINE neural network coprocessor for vibration analysis and fault diagnosis in machine condition monitoring

International audienceIndustry is a very attractive research field for wireless sensor network (WSN) applications. This is demonstrated by the creation of a special category of these networks dedicated to industrial applications, called industrial wireless sensor networks (IWSN). The sensor node, the main component of the network, must have several characteristics, such as a very high processing speed, reliable results, communication capabilities, and reduced transmission time. In this article, we outline the results of replacing the fast Fourier transform (FFT) processing of vibrational signals with an artificial adaptive linear element (ADALINE) neural network in order to extract the harmonics of the signals and thus detect faults in rotating machines. In addition, a MicroBlaze soft-core processor and an nRF24L01+ transmitter was chosen to manage various tasks within the node and the data exchange between the nodes of the network. A Digilent Cmod A7 platform with an Artix-7 FPGA circuit from Xilinx was selected to implement the different blocks that constitute the wireless node. Practical tests showed that the choice of the ADALINE enabled us to achieve the desired results by reducing the processing time to 7.478 ms, which is a reduction of time of approximately 85% compared to results obtained in scientific research. In addition, we have reduced the number of transmitted packets to only two. These results will have a positive impact on the performance of the node, with measurements using a periodic measurement methodology showing that the lifetime of the node can reach up to 17 h