Search Results (25)

Complex nonlinear feedback is a key factor in the generation of chaos. In many cases, complex nonlinear functions have a higher probability for chaos producing, and correspondingly new bifurcations may be triggered in the dynamical system. Due to the difficulty in circuit implementation of complex nonlinear feedback, researchers often introduce simple nonlinear constraints to study the occurrence and evolution of chaos. In fact, the impact of complex nonlinear feedback on chaotic dynamics deserves further investigation. In this work, complex nonlinear feedback is introduced into an offset-boostable chaotic system as an example to observe and analyze its regulatory effect on the dynamics. Complex nonlinear feedback may destroy the property of symmetry of a system; therefore, we examine the evolution of chaotic attractors under the corresponding feedback and the functional transformation between bifurcation and non-bifurcation parameters as well. By fully utilizing the flexible configuration advantages of Field Programmable Analog Array (FPAA), arbitrary complex nonlinear functions are implemented to verify the chaotic dynamics. Full article

This effort describes a low-power analog MP3 psychoacoustic model designed, implemented, and demonstrated using a large-scale Field-Programmable Analog Array (FPAA) to allocate bits for MP3 compression. Although MP3 encoders are assumed to be a digital algorithm, this effort looks to create analog algorithm versions. This block could enable low-power real-time MP3 encoding of microphone input signals for higher-quality acoustic transmissions, significantly improving the audio quality of the compressed transmitted signal from battery-powered devices. An exponentially spaced filterbank enables a low-power representation consistent with human hearing transduction. An analog psychoacoustic model uses signal masking to determine channel bit rates. These designs would enable a fully integrated MP3 encoder when implementing the straightforward aspects of the MDCT. Full article

Silicon neurons are bioinspired circuits with the capability to reproduce the modulation through pulse-frequency observed in real neurons. They are of particular interest in closed-loop schemes to encode the control signal into pulses. This paper proposes the analog realization of neuromorphic silicon neurons with fractional dynamics. In particular, the fractional-order (FO) operator is introduced into classical neurons with the intention of reproducing the adaptation that has been observed experimentally in real neurons, which is the variation in the firing frequency even when considering a constant or periodic incoming stimulus. For validation purposes, simulations using a field-programmable analog array (FPAA) are performed to verify the behavior of the circuits. Full article

The frequency response of the fractional-order power-law filter can be approximated by different techniques, which eventually affect the expected performance. Fractional-order control systems introduce many benefits for applications like compensators to achieve robust frequency and additional degrees of freedom in the tuning process. This paper is a comparative study of five of these approximation techniques. The comparison focuses on their magnitude error, phase error, and implementation complexity. The techniques under study are the Carlson, continued fraction expansion (CFE), Padé, Charef, and MATLAB curve-fitting tool approximations. Based on this comparison, the recommended approximation techniques are the curve-fitting MATLAB tool and the continued fraction expansion (CFE). As an application, a low-pass power-law filter is realized on a field-programmable analog array (FPAA) using two techniques, namely the curve-fitting tool and the CFE. The experiment aligns with and validates the numerical results. Full article

The design of analog computing systems requires significant human resources and domain expertise due to the lack of automation tools to enable these highly energy-efficient, high-performance computing nodes. This work presents the first automated tool flow from a high-level representation to a reconfigurable physical device. This tool begins with a high-level algorithmic description, utilizing either our custom Python framework or the XCOS GUI, to compile and optimize computations for integration into an Integrated Circuit (IC) design or a Field Programmable Analog Array (FPAA). An energy-efficient embedded speech classifier benchmark illustrates the tool demonstration, automatically generating GDSII layout or FPAA switch list targeting. Full article

Because of high conductivity, acceptable cost and good screen-printing process performance, silver pastes have been extensively used for making flexible electronics. However, there are few reported articles focusing on high heat resistance solidified silver pastes and their rheological properties. In this paper, a fluorinated polyamic acids (FPAA) is synthesized by polymerization of the 4,4′-(hexafluoroisopropylidene) diphthalic anhydride and 3,4′-diaminodiphenylether as monomers in the diethylene glycol monobutyl. The nano silver pastes are prepared by mixing the obtained FPAA resin with nano silver powder. The agglomerated particles caused by nano silver powder are divided and the dispersion of nano silver pastes are improved by three-roll grinding process with low roll gaps. The obtained nano silver pastes possess excellent thermal resistance with 5% weight loss temperature higher than 500 °C. The volume resistivity of cured nano silver paste achieves 4.52 × 10⁻⁷ Ω·m, when the silver content is 83% and the curing temperature is 300 °C. Additionally, the nano silver pastes have high thixotropic performance, which contributes to fabricate the fine pattern with high resolution. Finally, the conductive pattern with high resolution is prepared by printing silver nano pastes onto PI (Kapton-H) film. The excellent comprehensive properties, including good electrical conductivity, outstanding heat resistance and high thixotropy, make it a potential application in flexible electronics manufacturing, especially in high-temperature fields. Full article

Debris flow susceptibility mapping (DFSM), which has proven to be one of the most effective tools for risk management, faces a variety of problems. To realize the rational use of debris flow sample resources and improve the modeling efficiency, a unified model based on transfer learning was established for cross-regional DFSM. First, samples with 10 features collected from two debris flow-prone areas were separately used to perform factor prediction ability analysis (FPAA) based on the information gain ratio (IGR) method and then develop traditional machine learning models based on random forests (RF). Secondly, two feature matrices representing different areas were projected into a common latent feature space to obtain two new feature matrices. Then, the samples with new features were used together for FPAA and developing a unified machine learning model. Finally, the performance of the models was obtained and compared based on the area under curves (AUC) and some statistical results. All the conditioning factors played different roles in debris flow prediction in the two study areas, based on which two traditional models and a unified model were established. The unified model based on feature transferring realized efficient cross-regional modeling, solved the unconvincing problem of limited sample modeling, and enabled more accurate identification of some debris flow samples. Full article

In this paper, an approach for precise determination of the thermal conductivity of novel lead-free perovskite thin films by 3ω method, realized with a field programmable analog array circuit, is presented. The objective of the work is to study the relation between the thermal conductivity of the photoelectric perovskites and the thermal stability of the solar cells, in which they are incorporated. It is found that the solar cells’ long-term stability under different exploitation conditions, such as continuous illumination and elevated temperatures, is affected to a different extent, according to the thermal conductivity. The developed setup for implementation of the 3ω method is adapted for thin-film samples and can be applied to all layers involved in the solar cell, thus defining their individual contribution to the overall device thermal degradation. According to the conducted measurements, the coefficients of thermal conductivity for the novel materials are as follows: for the iodine-based perovskite film, it is 0.14 W/mK and for the chlorine-based perovskite film, it is 0.084 W/mK. As a result, the thermal instability and degradation rate at continuous illumination are, respectively, 10.6% and 200 nV/min for the iodine-based perovskite solar cell, and 6.5% and 20 nV/min for the chlorine-based cell. At elevated temperatures up to 54 °C, the corresponding instability values are 15 µV/°C with a degradation rate of an average of 2.2 µV/min for the cell with iodine-containing perovskite and 300 nV/°C with a degradation rate of 66 nV/min for the cell with chlorine-containing perovskite. Full article

The memristor is the fourth fundamental element in the electronic circuit field, whose memory and resistance properties make it unique. Although there are no electronic solutions based on the memristor, interest in application development has increased significantly. Nevertheless, there are only numerical Matlab or Spice models that can be used for simulating memristor systems, and designing is limited to using memristor emulators only. A memristor emulator is an electronic circuit that mimics a memristor. In this way, a research approach is to build discrete-component emulators of memristors for its study without using the actual models. In this work, two reconfigurable hardware architectures have been proposed for use in the prototyping of a non-linearity memristor emulator: the FPAA (Field Programing Analog Arrays) and the FPGA (Field Programming Gate Array). The easy programming and reprogramming of the first architecture and the performance, high area density, and parallelism of the second one allow the implementation of this type of system. In addition, a detailed comparison is shown to underline the main differences between the two approaches. These platforms could be used in more complex analog and/or digital systems, such as neural networks, CNN, digital circuits, etc. Full article

Large-scale field-programmable analog arrays (FPAA) have the potential to handle machine inference and learning applications with significantly low energy requirements, potentially alleviating the high cost of these processes today, even in cloud-based systems. FPAA devices enable embedded machine learning, one form of physical mixed-signal computing, enabling machine learning and inference on low-power embedded platforms, particularly edge platforms. This discussion reviews the current capabilities of large-scale field-programmable analog arrays (FPAA), as well as considering the future potential of these SoC FPAA devices, including questions that enable ubiquitous use of FPAA devices similar to FPGA devices. Today’s FPAA devices include integrated analog and digital fabric, as well as specialized processors and infrastructure, becoming a platform of mixed-signal development and analog-enabled computing. We address and show that next-generation FPAAs can handle the required load of 10,000–10,000,000,000 PMAC, required for present and future large fielded applications, at orders of magnitude of lower energy levels than those expected by current technology, motivating the need to develop these new generations of FPAA devices. Full article

A hyperjerk system described by a single fourth-order ordinary differential equation of the form $\stackrel{⃜}{x}=f(\stackrel{⃛}{x},\ddot{x},\dot{x},x)$ has been referred to as a snap system. A damping-tunable snap system, capable of an adjustable attractor dimension ($D_L$) ranging from dissipative hyperchaos ($D_L<4$) to conservative chaos ($D_L=4$), is presented for the first time, in particular not only in a snap system, but also in a four-dimensional (4D) system. Such an attractor dimension is adjustable by nonlinear damping of a relatively simple quadratic function of the form $A{x}^2$, easily tunable by a single parameter A. The proposed snap system is practically implemented and verified by the reconfigurable circuits of field programmable analog arrays (FPAAs). Full article

A simple and direct procedure for implementing fractional-order filters with transfer functions that contain Laplace operators of different fractional orders is presented in this work. Based on a general fractional-order transfer function that describes fractional-order low-pass, high-pass, band-pass, band-stop and all-pass filters, the introduced concept deals with the consideration of this function as a whole, with its approximation being performed using a curve-fitting-based technique. Compared to the conventional procedure, where each fractional-order Laplace operator of the transfer function is individually approximated, the main offered benefit is the significant reduction in the order of the resulting rational function. Experimental results, obtained using a field-programmable analog array device, verify the validity of this concept. Full article

The chemical modification of amino acids plays an important role in the modulation of proteins or peptides and has useful applications in the activation and stabilization of enzymes, chemical biology, shotgun proteomics, and the production of peptide-based drugs. Although chemoselective modification of amino acids such as lysine and arginine via the insertion of respective chemical moieties as citraconic anhydride and phenyl glyoxal is important for achieving desired application objectives and has been extensively reported, the extent and chemoselectivity of the chemical modification of specific amino acids using specific chemical agents (blocking or modifying agents) has yet to be sufficiently clarified owing to a lack of suitable assay methodologies. In this study, we examined the utility of a fluorogenic assay method, based on a fluorogenic tripeptide substrate (FP-AA1-AA2-AA3) and the proteolytic enzyme trypsin, in determinations of the extent and chemoselectivity of the chemical modification of lysine or arginine. As substrates, we used two fluorogenic tripeptide probes, MeRho-Lys-Gly-Leu(Ac) (lysine-specific substrate) and MeRho-Arg-Gly-Leu(Ac) (arginine-specific substrate), which were designed, synthesized, and evaluated for chemoselective modification of specific amino acids (lysine and arginine) using the fluorogenic assay. The results are summarized in terms of half-maximal inhibitory concentrations (IC₅₀) for the extent of modification and ratios of IC₅₀ values (IC₅₀arginine/IC₅₀lysine and IC₅₀lysine/IC₅₀arginine) as a measure of the chemoselectivity of chemical modification for amino acids lysine and arginine. This novel fluorogenic assay was found to be rapid, precise, and reproducible for determinations of the extent and chemoselectivity of chemical modification. Full article

Floating-gate (FG) transistors are a primary means of providing nonvolatile digital memory in standard CMOS processes, but they are also key enablers for large-scale programmable analog systems, as well. Such programmable analog systems are often designed for battery-powered and resource-constrained applications, which require the memory cells to program quickly and with low infrastructural overhead. To meet these needs, we present a four-transistor analog floating-gate memory cell that offers both voltage and current outputs and has linear programming characteristics. Furthermore, we present a simple programming circuit that forces the memory cell to converge to targets with 13.0 bit resolution. Finally, we demonstrate how to use the FG memory cell and the programmer circuit in array configurations. We show how to program an array in either a serial or parallel fashion and demonstrate the effectiveness of the array programming with an application of a bandpass filter array. Full article

The wireless sensor nodes used in a growing number of remote sensing applications are deployed in inaccessible locations or are subjected to severe energy constraints. Audio-based sensing offers flexibility in node placement and is popular in low-power schemes. Thus, in this paper, a node architecture with low power consumption and in-the-field reconfigurability is evaluated in the context of an acoustic vehicle detection and classification (hereafter “AVDC”) scenario. The proposed architecture utilizes an always-on field-programmable analog array (FPAA) as a low-power event detector to selectively wake a microcontroller unit (MCU) when a significant event is detected. When awoken, the MCU verifies the vehicle class asserted by the FPAA and transmits the relevant information. The AVDC system is trained by solving a classification problem using a lexicographic, nonlinear programming algorithm. On a testing dataset comprising of data from ten cars, ten trucks, and 40 s of wind noise, the AVDC system has a detection accuracy of 100%, a classification accuracy of 95%, and no false alarms. The mean power draw of the FPAA is 43 $\mathsf{\mu }$ W and the mean power consumption of the MCU and radio during its validation and wireless transmission process is 40.9 mW. Overall, this paper demonstrates that the utilization of an FPAA-based signal preprocessor can greatly improve the flexibility and power consumption of wireless sensor nodes. Full article