Frequency domain near-infrared spectroscopy (FD-NIRS) has proven to be a reliable method for quantification of tissue absolute optical properties. We present a full-sampling direct analog-to-digital conversion FD-NIR ...
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Frequency domain near-infrared spectroscopy (FD-NIRS) has proven to be a reliable method for quantification of tissue absolute optical properties. We present a full-sampling direct analog-to-digital conversion FD-NIR imager. While we developed this instrument with a focus on high-speed optical breast tomographic imaging, the proposed design is suitable for a wide-range of biophotonic applications where fast, accurate quantification of absolute optical properties is needed. Simultaneous dual wavelength operation at 685 and 830 nm is achieved by concurrent 67.5 and 75 MHz frequency modulation of each laser source, respectively, followed by digitization using a high-speed (180 MS/s) 16-bit A/D converter and hybrid FPGA-assisted demodulation. The instrument supports 25 source locations and features 20 concurrently operating detectors. The noise floor of the instrument was measured at <1.4 pW/root Hz, and a dynamic range of 115+ dB, corresponding to nearly six orders of magnitude, has been demonstrated. Titration experiments consisting of 200 different absorption and scattering values were conducted to demonstrate accurate optical property quantification over the entire range of physiologically expected values. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
A four-leg distribution static compensator (FL-DSTATCOM) has been employed for improving the quality of power in electric distribution system using field-programmable gate array (FPGA). A predictive current control te...
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A four-leg distribution static compensator (FL-DSTATCOM) has been employed for improving the quality of power in electric distribution system using field-programmable gate array (FPGA). A predictive current control technique without any modulation scheme is utilised to generate switching pulses for FL-DSTATCOM using a cost function defining the preferred system behaviour. This technique makes the supply current as balanced and sinusoidal by exactly tracking the reference current, compensating the reactive power, maintaining near unity power factor and nullifies the current flow in neutral conductor during steady and dynamic state conditions. Synchronous reference frame theory is effectively implemented for extracting the reactive and harmonic part of load current which are the primary components in the evaluation of reference currents. The predictive current control scheme implemented in FPGA has been deployed to evaluate the behaviour of the FL-DSTATCOM under multifarious loading conditions.
The efficiency of the hardware implementations of fractional Kalman filter (FKF) heavily relies on the efficiency of realising the fractional-order derivative operator. In this paper, a generic software and hardware i...
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The efficiency of the hardware implementations of fractional Kalman filter (FKF) heavily relies on the efficiency of realising the fractional-order derivative operator. In this paper, a generic software and hardware implementation of the FKF based on the Grunwald-Letnikov approximation is proposed and verified on a field-programmable gate array. The main advantage of this particular realisation is its resource saving capability on both software and hardware components (e.g. digital signal processing chips), and this enables fast and efficient real-time computation of the FKF. Furthermore, the performance of the FKF is evaluated via studying the mean square boundedness of the filtering error. At last, a mass-spring-damper example is implemented via Matlab simulations to verify the applicability of the proposed approach and moreover, a hardware experiment is also conducted to demonstrate the advantage of resource saving.
IN 2003, THE AUTHORS PREDICTED THAT RECONFIGURABLE SYSTEMS WOULD EMERGE WITHIN 10 YEARS. THEY EXPECTED THE ARRIVAL OF A "HOLY GRAIL" MEMORY COMPONENT AND CHIP AND WAFER STACKING. THESE ADVANCES DID NOT OCCUR...
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IN 2003, THE AUTHORS PREDICTED THAT RECONFIGURABLE SYSTEMS WOULD EMERGE WITHIN 10 YEARS. THEY EXPECTED THE ARRIVAL OF A "HOLY GRAIL" MEMORY COMPONENT AND CHIP AND WAFER STACKING. THESE ADVANCES DID NOT OCCUR. RECONFIGURABLE SYSTEMS SHOULD APPEAR IN MARKETS WHERE EQUIPMENT HAS ACCESS TO CONTINUOUS POWER, BUT FINANCIAL INCENTIVES AND THE LACK OF A VERSATILE MEMORY COMPONENT INHIBIT THEIR EMERGENCE IN MOBILE SYSTEMS.
Blind signal modulation recognition is an essential block for designing a cognitive radio. Different algorithms are developed in the literature, but few are given with detailed implementation. This study proposes a so...
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Blind signal modulation recognition is an essential block for designing a cognitive radio. Different algorithms are developed in the literature, but few are given with detailed implementation. This study proposes a software-defined radio based implementation of blind signal modulation recogniser (BSMR) on field-programmable gate array (FPGA), which works without any prior knowledge of the received signal. The algorithm estimates carrier frequency offset, symbol rate, symbol timing offset, and corrects the signal for these offsets to extract constellation points. It uses clustering structure formed by constellation signature in I/Q plane to detect the modulation for different orders of ASK, PSK, and QAM. The proposed algorithm is deployed on FPGA, using LabVIEW, for a reliable and reconfigurable platform. The algorithm is optimised to use minimum hardware resources and facilitate future up-gradation. The system developed by implementing the algorithm on NI-FlexRIO-7975 FPGA module with NI-5791 adapter detects modulation type in real time without any training. Signals for testing are generated using NI-PXIe-5673 (RF transmitter), and BSMR identifies the modulation type in 81.451ms under additive white Gaussian noise channel.
Homogeneous charge compression ignition has proven to be both highly efficient and to operate with ultra-low NOx raw emissions. However, homogeneous charge compression ignition combustion is a dynamic process due to s...
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Homogeneous charge compression ignition has proven to be both highly efficient and to operate with ultra-low NOx raw emissions. However, homogeneous charge compression ignition combustion is a dynamic process due to strong cycle-to-cycle coupling effects caused mainly by the residual gas. Compared to conventional spark-ignited and diesel engines, the lack of direct mixture composition and ignition control increases the challenge of combustion instabilities, especially at boundary conditions. To stabilize the combustion process, real-time in-cylinder combustion diagnostics and control are often used. In this study, for the first time, ion current detection technology and direct water injection are combined for homogeneous charge compression ignition combustion control. By analyzing the return map of the crank angle at 50% cumulative heat release under unstable conditions, it was identified that a misfire or incomplete combustion is usually followed by knocking-like early combustion with high cylinder pressure gradients. Through the correlation analysis between ion current and combustion, a cycle-to-cycle closed-loop control strategy was developed and implemented on a rapid control prototyping engine control unit. Real-time calculated ion current parameters were used to predict the 50% cumulative heat release position of the next cycle and prevent early combustion by direct water injection. The calculation results and controller performance were validated on a single-cylinder research engine. With the controller activated, the standard deviation of 50% cumulative heat release and dynamic programming to the max could be reduced by 19% and 11%, respectively. The coefficient of variation of indicated mean effective pressure was reduced by 12%. A slight increase in indicated mean effective pressure after activating the controller also shows the potential for efficiency improvement. Moreover, not only early combustion is controlled, but also late combustion is significantl
We present a hardware architecture for real-time digital video stabilization for high-performance embedded systems. The stabilization algorithm analyzes the current and past video frames and obtains a motion estimatio...
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We present a hardware architecture for real-time digital video stabilization for high-performance embedded systems. The stabilization algorithm analyzes the current and past video frames and obtains a motion estimation vector, which is then filtered to isolate unwanted camera movements from intentional panning. The vector is then used to correct the output video frame. The paper describes our hardware architecture for motion estimation, filtering and correction and its implementation on a Xilinx Spartan-6 LX45 FPGA. We evaluate our results on several benchmark video sequences, both visually and using the Inter-frame Transformation Fidelity index (ITF). Running on the 640 x 480-pixel video output of an infrared camera, our FPGA implementation successfully compensates involuntary camera motion at a maximum throughput of 104.15 frames per second with a 100 MHz clock. The power consumption added to the FPGA by the image stabilization core is only 24.16 mW. (C) 2015 Elsevier B.V. All rights reserved.
This paper presents a finite control set model predictive control (FCS-MPC) approach for two induction machines driven by a nine-switch inverter (NSI). In the traditional approach, two separate voltage source inverter...
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This paper presents a finite control set model predictive control (FCS-MPC) approach for two induction machines driven by a nine-switch inverter (NSI). In the traditional approach, two separate voltage source inverters are necessary to drive the independent induction motors. In the proposed method, the nine-switch inverter is used to control the separate motors with a reduced number of switching devices compared to traditional method. A robust control strategy that eliminates the interactions between separate mechanical loads is required to achieve a proper independent speed and torque control for two induction machines through the NSI. To ensure the reliability of the machine operation, the indirect-field oriented control-based model predictive control strategy is proposed. The proposed control strategy is experimentally validated across the 3.2 kW SiC-based NSI prototype. The control algorithm is performed on an Altera Cyclone IV field-programmable gate array. The experimental results demonstrate that the proposed dual-model predictive control method provides a good and robust motor control operation under different loading conditions. Two induction motors are successfully controlled, and the independent speed and torque control are achieved.
Deep neural networks (DNNs) have demonstrated super performance in most learning tasks. However, a DNN typically contains a large number of parameters and operations, requiring a high-end processing platform for high-...
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Deep neural networks (DNNs) have demonstrated super performance in most learning tasks. However, a DNN typically contains a large number of parameters and operations, requiring a high-end processing platform for high-speed execution. To address this challenge, hardware-and-software co-design strategies, which involve joint DNN optimization and hardware implementation, can be applied. These strategies reduce the parameters and operations of the DNN, and fit it into a low-resource processing platform. In this paper, a DNN model is used for the analysis of the data captured using an electrochemical method to determine the concentration of a neurotransmitter and the recoding electrode. Next, a DNN miniaturization algorithm is introduced, involving combined pruning and compression, to reduce the DNN resource utilization. Here, the DNN is transformed to have sparse parameters by pruning a percentage of its weights. The Lempel-Ziv-Welch algorithm is then applied to compress the sparse DNN. Next, a DNN overlay is developed, combining the decompression of the DNN parameters and DNN inference, to allow the execution of the DNN on a FPGA on the PYNQ-Z2 board. This approach helps avoid the need for inclusion of a complex quantization algorithm. It compresses the DNN by a factor of 6.18, leading to about 50% reduction in the resource utilization on the FPGA.
Neuromorphic architectures are systems that aim at using the principles of biological neural functions as their basis of operation. One of the most significant challenges in neuromorphic studies, which play an importa...
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Neuromorphic architectures are systems that aim at using the principles of biological neural functions as their basis of operation. One of the most significant challenges in neuromorphic studies, which play an important role in information processing, is the investigation of astrocytes in neuronal models. This paper presents an efficient FPGA-based digital implementation of a spiking neuron model, known as the 2D Hindmarsh-Rose model, and neuron-astrocyte model. To avoid costly computations, the astrocyte and 2D Hindmarsh-Rose models were approximated. The approximation was performed based on multiple method such as the piecewise linear model (PWL) and the particle swarm optimization (PSO) method. As known, noisy mechanisms are stochastic processes which help to improve information processing in nonlinear dynamical systems, including neural systems, and results in more realistic behaviors. Therefore, we presented the noise implications for the approximated neuron-astrocyte models. By introducing two networks consisting of ten 2D Hindmarsh-Rose neurons, the role of the approximated astrocyte in regulation of the neural activities and noise tolerance of the neural networks was investigated. Accordingly, the feasibility of the digital implementation for the proposed 2D Hindmarsh-Rose neuron and the neuron-astrocyte models was studied. Experimental findings of the hardware synthesis and physical implementation on a field-programmable gate array (FPGA) were expounded for the modified spiking neuron model and the approximated astrocyte models with maximum clock frequencies of 247.35 MHz and 279.28 MHz, respectively, showed an increase by about 3.5 times in the frequency in both approximated models. The number of slice registers decreased by 22% and 20% in the proposed 2D Hindmarsh-Rose and astrocyte models, respectively. Also, the networks in the original and approximated 2D Hindmarsh-Rose neurons and astrocyte were synthesized on an FPGA platform. Maximum clock frequenci
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