An Efficient Data Aggregation Algorithm for Cluster-based Sensor Network

Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energy-efficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation framework for functional analysis of WSN applications taking our proposed algorithm as an exampl

Suppression of Quantization-Induced Limit Cyclesin Digitally Controlled DC-DC Converters by Dyadic Digital Pulse Width Modulation

Quantization-induced limit cycle oscillations (LCOs) in digitally controlled DC-DC converters are addressed in this paper. The novel Dyadic Digital PWM (DDPWM) is proposed to increase the effective pulse-width-modulator (PWM) resolution, as required for LCO free operation, at low cost, without sacrificing DC accuracy and with no detrimental effects on the ripple voltage. Experimental results on a synchronous buck validate the approach highlighting effective LCOs suppression and DC accuracy enhancement at 5x reduced output voltage ripple compared to thermometric dithering for the same resolution increase

A distributed combustion solver for engine simulations on grids

AbstractMulti-dimensional models for predictive simulations of modern engines are an example of multi-physics and multi-scale mathematical models, since lots of thermofluiddynamic processes in complex geometrical configurations have to be considered. Typical models involve different submodels, including turbulence, spray and combustion models, with different characteristic time scales. The predictive capability of the complete models depends on the accuracy of the submodels as well as on the reliability of the numerical solution algorithms. In this work we propose a multi-solver approach for reliable and efficient solution of the stiff Ordinary Differential Equation (ODE) systems arising from detailed chemical reaction mechanisms for combustion modeling. Main aim was to obtain high-performance parallel solution of combustion submodels in the overall procedure for simulation of engines on distributed heterogeneous computing platforms. To this aim we interfaced our solver with the CHEMKIN-II package and the KIVA3V-II code and carried out multi-computer simulations of realistic engines. Numerical experiments devoted to test reliability of the simulation results and efficiency of the distributed combustion solver are presented and discussed

Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors

Human detection, identification, and monitoring are essential for many applications aiming to make smarter the indoor environments, where most people spend much of their time (like home, office, transportation, or public spaces). The capacitive sensors can meet stringent privacy, power, cost, and unobtrusiveness requirements, they do not rely on wearables or specific human interactions, but they may need significant on-board data processing to increase their performance. We comparatively analyze in terms of overall processing time and energy several data processing implementations of multilayer perceptron neural networks (NNs) on board capacitive sensors. The NN architecture, optimized using augmented experimental data, consists of six 17-bit inputs, two hidden layers with eight neurons each, and one four-bit output. For the software (SW) NN implementation, we use two STMicroelectronics STM32 low-power ARM microcontrollers (MCUs): one MCU optimized for power and one for performance. For hardware (HW) implementations, we use four ultralow-power field-programmable gate arrays (FPGAs), with different sizes, dedicated computation blocks, and data communication interfaces (one FPGA from the Lattice iCE40 family and three FPGAs from the Microsemi IGLOO family). Our shortest SW implementation latency is 54.4 µs and the lowest energy per inference is 990 nJ, while the shortest HW implementation latency is 1.99 µs and the lowest energy is 39 nJ (including the data transfer between MCU and FPGA). The FPGAs active power ranges between 6.24 and 34.7 mW, while their static power is between 79 and 277 µW. They compare very favorably with the static power consumption of Xilinx and Altera low-power device families, which is around 40 mW. The experimental results show that NN inferences offloaded to external FPGAs have lower latency and energy than SW ones (even when using HW multipliers), and the FPGAs with dedicated computational blocks (multiply-accumulate) perform best

A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes - as is the case in biological networks - due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications

Advanced numerical treatment of an accurate SPH method

The summation of Gaussian kernel functions is an expensive operation frequently encountered in scientific simulation algorithms and several methods have been already proposed to reduce its computational cost. In this work, the Improved Fast Gauss Transform (IFGT) [1] is properly applied to the Smoothed Particle Hydrodynamics (SPH) method [2] in order to speed up its efficiency. A modified version of the SPH method is considered in order to overcome the loss of accuracy of the standard formulation [3]. A suitable use of the IFGT allows us to reduce the computational effort while tuning the desired accuracy into the SPH framework. This technique, coupled with an algorithmic design for exploiting the performance of Graphics Processing Units (GPUs), makes the procedure promising as shown by preliminary numerical simulations

Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis

Abstract Summary: Elucidation of molecular targets of a compound [mode of action (MoA)] and its off-targets is a crucial step in drug development. We developed an online collaborative resource (MANTRA 2.0) that supports this process by exploiting similarities between drug-induced transcriptional profiles. Drugs are organized in a network of nodes (drugs) and edges (similarities) highlighting 'communities' of drugs sharing a similar MoA. A user can upload gene expression profiles before and after drug treatment in one or multiple cell types. An automated processing pipeline transforms the gene expression profiles into a unique drug 'node' embedded in the drug-network. Visual inspection of the neighbouring drugs and communities helps in revealing its MoA and to suggest new applications of known drugs (drug repurposing). MANTRA 2.0 allows storing and sharing user-generated network nodes, thus making MANTRA 2.0 a collaborative ever-growing resource. Availability and implementation: The web tool is freely available for academic use at http://mantra.tigem.it. Contact: [email protected]

Regional anticoagulation with heparin of an extracorporeal CO 2 removal circuit: A case report

Background: Extracorporeal carbon dioxide removal is an increasingly used respiratory support technique. As is true of all extracorporeal techniques, extracorporeal carbon dioxide removal needs proper anticoagulation. We report a case of a patient at risk of bleeding complications who was treated with extracorporeal carbon dioxide removal and anticoagulated with a regional technique. Case presentation: A 56-year-old Caucasian man with a history of chronic obstructive pulmonary disease exacerbation required extracorporeal carbon dioxide removal for severe hypercapnia and acidosis despite mechanical ventilation. The extracorporeal circuit was anticoagulated using a regional heparin technique to limit the patient's risk of bleeding due to a low platelet count. The patient underwent 96 h of effective extracorporeal carbon dioxide removal without any adverse events. He was successfully weaned from extracorporeal carbon dioxide removal. During the treatment, no bleeding complications or unexpected circuit clotting was observed. Conclusions: The use of regional heparin anticoagulation technique seems to be feasible and safe during extracorporeal carbon dioxide removal