Event-Driven Deep Neural Network Hardware System for Sensor Fusion

This paper presents a real-time multi-modal spiking Deep Neural Network (DNN) implemented on an FPGA platform. The hardware DNN system, called n-Minitaur, demonstrates a 4-fold improvement in computational speed over the previous DNN FPGA system. The proposed system directly interfaces two different event-based sensors: a Dynamic Vision Sensor (DVS) and a Dynamic Audio Sensor (DAS). The DNN for this bimodal hardware system is trained on the MNIST digit dataset and a set of unique audio tones for each digit. When tested on the spikes produced by each sensor alone, the classification accuracy is around 70% for DVS spikes generated in response to displayed MNIST images, and 60% for DAS spikes generated in response to noisy tones. The accuracy increases to 98% when spikes from both modalities are provided simultaneously. In addition, the system shows a fast latency response of only 5ms

Mathematical modeling of antihypertensive therapy

Hypertension is a multifactorial disease arising from complex pathophysiological pathways. Individual characteristics of patients result in different responses to various classes of antihypertensive medications. Therefore, evaluating the efficacy of therapy based on in silico predictions is an important task. This study is a continuation of research on the modular agent-based model of the cardiovascular and renal systems (presented in the previously published article). In the current work, we included in the model equations simulating the response to antihypertensive therapies with different mechanisms of action. For this, we used the pharmacodynamic effects of the angiotensin II receptor blocker losartan, the calcium channel blocker amlodipine, the angiotensin-converting enzyme inhibitor enalapril, the direct renin inhibitor aliskiren, the thiazide diuretic hydrochlorothiazide, and the β-blocker bisoprolol. We fitted therapy parameters based on known clinical trials for all considered medications, and then tested the model’s ability to show reasonable dynamics (expected by clinical observations) after treatment with individual drugs and their dual combinations in a group of virtual patients with hypertension. The extended model paves the way for the next step in personalized medicine that is adapting the model parameters to a real patient and predicting his response to antihypertensive therapy. The model is implemented in the BioUML software and is available at https://gitlab.sirius-web.org/virtual-patient/antihypertensive-treatment-modeling

Investigation of vibratory drilling model with adaptive control. Part 2: mixed control of peak-to-peak vibration displacement and cutting continuity index

Chip segmentation is important condition for deep drilling efficiency improving. Chip segmentation could be ensured by sustaining stable axial self-excited vibrations of a drill. Vibrations are excited by regenerative effect when cutting edges move along the surface formed by previous passes. The conditions required for reliable chip segmentation could be created by using of a special vibratory head with an elastic element, providing tool additional axial flexibility. To maintain stable vibro-process with amplitude sufficient for chip segmentation, it’s suggested to use the vibratory head with a special actuator for adaptive feedback control proportional to a tool vibration velocity. Two algorithms of the feedback gain adaptation are proposed in the present paper: the adaptation by peak-to-peak displacement and the mixed adaptation by peak-to-peak displacement with cutting continuity index. The investigation of effectiveness of the proposed algorithms applicable to the model, described in [9], is also presented

Screening of Conformational Diversity of Molecules of Biologically Active Compouds Based on Nuclear Overhauser Effect Spectroscopy

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A 23μW Solar-Powered Keyword-Spotting ASIC with Ring-Oscillator-Based Time-Domain Feature Extraction

Voice-controlled interfaces on acoustic Internet-of-Things (IoT) sensor nodes and mobile devices require integrated low-power always-on wake-up functions such as Voice Activity Detection (VAD) and Keyword Spotting (KWS) to ensure longer battery life. Most VAD and KWS ICs focused on reducing the power of the feature extractor (FEx) as it is the most power-hungry building block. A serial Fast Fourier Transform (FFT)-based KWS chip [1] achieved 510nW; however, it suffered from a high 64ms latency and was limited to detection of only 1-to-4 keywords (2-to-5 classes). Although the analog FEx [2]–[3] for VAD/KWS reported 0.2μW-to-1 μW and 10ms-to-100ms latency, neither demonstrated >5 classes in keyword detection. In addition, their voltage-domain implementations cannot benefit from process scaling because the low supply voltage reduces signal swing; and the degradation of intrinsic gain forces transistors to have larger lengths and poor linearity

Clusters partition algorithm for a self-organizing map for detecting resource-intensive database inquiries in a geo-ecological monitoring system

The paper presents the research, aimed at improving the efficiency of automated software system for geo-ecological monitoring of agro-industrial sector resources. An algorithm of clusters partition in a self-organizing map was developed, in order to detect resource-intensive inquiries to databases of agricultural resources and objects. The algorithm is based on using fuzzy inference. The corresponding software for implementing the proposed algorithm was created. The carried-out experimental research has demonstrated that this algorithm allows considerably increasing the correctness of detecting resource-intensive inquiries to databases in comparison with other similar software applications. The algorithm, presented in this paper, can be recommended for practical application in an automated software system for geo-ecological monitoring of agricultural resources and objects

Promising Direction of Perfection of the Utilization Combine Cycle Gas Turbine Units

Issues of improving the efficiency of combined cycle gas turbines (CCGT) recovery type have been presented. Efficiency gas turbine plant reaches values of 45 % due to rise in temperature to a gas turbine to 1700 °C. Modern technologies for improving the cooling gas turbine components and reducing the excess air ratio leads to a further increase of the efficiency by 1-2 %. Based on research conducted at the Tomsk Polytechnic University, it shows that the CCGT efficiency can be increased by 2-3 % in the winter time due to the use of organic Rankine cycle, low-boiling substances, and air-cooled condensers (ACC). It is necessary to apply the waste heat recovery with condensation of water vapor from the flue gas, it will enhance the efficiency of the CCGT by 2-3 % to increase the efficiency of the heat recovery steam boiler (HRSB) to 10-12 %. Replacing electric pumps gas turbine engine (GTE) helps to reduce electricity consumption for auxiliary needs CCGT by 0.5-1.5 %. At the same time the heat of flue gas turbine engine may be useful used in HRSB, thus will increase the capacity and efficiency of the steam turbine

A 128-channel real-time VPDNN stimulation system for a visual cortical neuroprosthesis

With the recent progress in developing large-scale micro-electrodes, cortical neuroprotheses supporting hundreds of electrodes will be viable in the near future. We describe work in building a visual stimulation system that receives camera input images and produces stimulation patterns for driving a large set of electrodes. The system consists of a convolutional neural network FPGA accelerator and a recording and stimulation Application-Specific Integrated Circuit (ASIC) that produces the stimulation patterns. It is aimed at restoring visual perception in visually impaired subjects. The FPGA accelerator, VPDNN, runs a visual prosthesis network that generates an output used to create stimulation patterns, which are then converted by the ASIC into current pulses to drive a multi-electrode array. The accelerator exploits spatial sparsity and the use of reduced bit precision parameters for reduced computation, memory and power for portability. Experimental results from the VPDNN show that the 94.5K parameter 14-layer CNN receiving an input of 128 × 128 has an inference frame rate of 83 frames per sec (FPS) and uses only an incremental power of 0.1 W, which is at least 10× lower than that measured from a Jetson Nano. The ASIC adds a maximum delay of 2ms, however it does not impact the FPS thanks to double-buffered memory. Index Terms—Visual prosthesis, convolutional neural network, FPGA Accelerator, stimulation and recording ASI