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hilbert
No full textWe report a photonic microwave and RF fractional Hilbert transformer based on an integrated Kerr micro-comb source. The micro-comb source has a free spectral range (FSR) of 50GHz, generating a large number of comb lines that serve as a high-performance multi-wavelength source for the transformer. By programming and shaping the comb lines according to calculated tap weights, we achieve both arbitrary fractional orders and a broad operation bandwidth. We experimentally characterize the RF amplitude and phase response for different fractional orders and perform system demonstrations of real-time fractional Hilbert transforms. We achieve a phase ripple of < 0.15 rad within the 3-dB pass-band, with bandwidths ranging from 5 to 9 octaves, depending on the order. The experimental results show good agreement with theory, confirming the effectiveness of our approach as a new way to implement high-performance fractional Hilbert transformers with broad processing bandwidth, high reconfigurability, and greatly reduced size and complexity
Digital filter on FPGA for subcellular resolution electrophysiology using a high-density CMOS-based microelectrode array
Get PDFCMOS-based microelectrode arrays (MEAs) are used to record the electrical activity in neural tissues down to micron-scale cellular structures at high spatiotemporal resolution. Continuous recording of extracellular voltages would, however generate large datasets with very sparse spatial and temporal information. Towards an efficient strategy, we propose here a Field Programmable Gate Array (FPGA) which filters the continuous CMOS MEA data stream sampled at 28 kHz and extracts electrophysiological relevant information. In a first step, sensors of interest are selected based on the electrical label-free identification of those sensors covered by the neural tissue via adhesion noise spectroscopy. The adhesion noise-based electrical imaging is validated against light microscopic images. The FPGA finite impulse response (FIR)-filtered data is validated against software-based post-processed data. In a second step, we implement a spike-triggered average (STA) algorithm to identify and visualize electrical activity at subcellular resolution in retinal neurons, which allows for the tracking of axonal signal propagation within the neural tissue. This label-free, non-invasive method enables the localization of sensors of interest for electrophysiological recordings and the extraction of neuronal signals. It represents a significant advancement in neuroscience tools, which facilitates the study of neuronal network dynamics at unprecedented spatiotemporal resolution
On design, analysis, and hybrid manufacturing of microstructured blade-like geometries
No full textWith the evolution of new manufacturing technologies such as multi-material 3D printing, one can think of new type of objects that consist of considerably less, yet heterogeneous, material, consequently being porous, lighter and cheaper, while having the very same functionality as the original object when manufactured from one single solid material. We aim at questioning five decades of traditional paradigms in geometric CAD and focus at new generation of CAD objects that are not solid, but contain heterogeneous free-form internal microstructures. We propose a unified manufacturing pipeline that involves all stages, namely design, optimization, manufacturing, and inspection of microstructured free-form geometries. We demonstrate our pipeline on an industrial test case of a blisk blade that sustains the desired pressure limits, yet requires significantly less material when compared to the solid counterpart
A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 2: Atomic polarizabilities
No full textOpenCOSMO-RS is an open-source predictive thermodynamic model that can be applied to a broad range of systems in various chemical and biochemical engineering domains. This study focuses on improving openCOSMO-RS by introducing a new dispersion term based on atomic polarizabilities. We evaluate different methods for processing polarizability data, including scaling and combining it to compute segment-segment dispersion interaction energies, with a focus on halocarbon systems. The results demonstrate that the modified model outperforms our previous method developed in the first part of this work Grigorash et al. (2024), while at the same time requiring fewer adjustable parameters. The approach was applied to a broad dataset of over 50,000 data points, consistently increasing the accuracy across a variety of data types. These findings suggest that atomic polarizability is a valuable descriptor for refining dispersion interactions in predictive thermodynamic models
Reciprocal learning in human–machine collaboration: a systematic literature review and implications for production and logistics
No full textThe integration of automation technologies and artificial intelligence into production and logistics is transforming work organisation for human and machine agents. Beyond the scope of classical collaborative task allocation problems, studies on social aspects, especially the mutual learning of humans and intelligent machines during interactions, remain scarce. By enhancing cyber-physical production and logistics systems to become intelligent, learnable, and social, human–machine symbiosis can be fostered to enhance their complementary strengths. Despite studies addressing the potential of this bidirectional learning process in the form of reciprocal human–machine learning (RHML), this concept remains ambiguous and lacks a comprehensive knowledge base in production and logistics. Therefore, in this study, a systematic literature review was conducted to gather and categorise the existing knowledge on RHML in different disciplines. Further, efforts were made to (i) consolidate existing design components of RHML into a taxonomy; (ii) describe current design patterns of RHML with classified RHML archetypes; and (iii) apply the resulting taxonomy and archetypes to discuss the potential of RHML concepts in production and logistics. This interdisciplinary approach aims to extend the existing design concepts in cyber-physical production and logistics systems. In addition, initial discussions on the future research agenda are provided
DRDST: Low-Latency DAG Consensus Through Robust Dynamic Sharding and Tree-Broadcasting for IoV
No full textThe Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Addressing blockchain’s scalability challenges posed by the growing number of nodes and transactions calls for innovative solutions, among which sharding stands out as a pivotal approach to significantly enhance blockchain throughput. However, existing schemes still face challenges related to a) the impact of vehicle mobility on blockchain consensus, especially for cross-shard transaction; and b) the strict requirements of low latency consensus in a highly dynamic network. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we first develop a standard for evaluating the network stability of nodes, combined with the nodes’ trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle cross-shard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in latency, throughput, consensus success rate, and node traffic load
Increased purity and refolding yield of bacterial inclusion bodies by recursive high pressure homogenization
Get PDFTherapeutic proteins are often produced intracellularly in the bacterium Escherichia coli in aggregated form, known as inclusion bodies, which must be solubilized and refolded to recover the biologically active product. However, the industrially established refolding method of batch dilution is still carried out at low protein concentrations, leading to high water consumption, large equipment footprints, and poor sustainability. An underexplored way to increase refolding yields and thereby enable higher product concentrations after refolding is the optimization of cell lysis and inclusion body washing. In this study, we developed and evaluated a “recursive high pressure homogenization” approach, wherein wash steps and additional homogenization cycles were combined to enhance the final purity of inclusion bodies before solubilization. The degree of cell lysis, nucleic acid release and final inclusion body purity were compared to conventional “linear washing”, where all homogenization cycles are completed prior to the washing of inclusion bodies. In total, seven process variations were compared and the resulting batches of inclusion bodies were solubilized and refolded to investigate the effect of early downstream processing on the product concentration in the final refolds. For the most efficient process variant, the recursive high pressure homogenization protocol led to the highest measured product concentration of 855 mg/L after refolding. Thus, the proposed recursive high pressure homogenization approach led to an estimated 18 % reduction of CO₂ footprint caused by urea, and increased the product yield per biomass from 5.17 g/kg to 7.84 g/kg compared to its linear wash counterpart — without introducing non-standard equipment or chemicals
