Low Throughput Research Articles

Design and performance analysis of asynchronous network on chip for streaming data transmission on FPGA

The majority of the system on chip (SoC) uses the network on chip (NoC) as routing ports for data transfer from node-to-node with minimal power consumption and low latency and high throughput. This paper concentrates on the ability to model the asynchronous NoCs on the asynchronous circuits on field programmable gate arrays (FPGAs). A 3×3 NoC and its universal asynchronous receiver transmitter (UART) protocol is designed and its simulation of the Verilog hardware description language (VHDL) code is done and tested on the Artix-7 FPGA kit, the testing processes in done using the Chipscope tool. In order to meet target requirements in terms of power consumption and latency, the label switching (LS) technique is used as routing. The proposed LS-NoC with level-encoded dual-rail (LEDR) encoding technique provides throughput by registering the packet between the different routers and it helps to improve throughput and speed. The effectiveness of the data transfer is measured and analyzed through a synthesis summary in terms of lookup table’s (LUT’s), slice registers, flip flops’s (FF’s), latency, and packet delivery ratio (PDR) for the traffic pattern generator. The proposed NoC is designed for 8×8 and each port size is 21 bits including ID’s of source and destination routers. The results can be justified by following results: improvement of LUTs is about 12%, flip-flops are 7%, improvement of throughput is 23% and delay is reduced by 26%.

Multi‐graph representation spatio‐temporal attention networks for traffic forecasting in the cinematic metaverse

AbstractThe cinematic metaverse aims to create a virtual space with the context of a film. Users can enter this space in the form of avatars, experiencing the cinematic plot firsthand in an immersive manner. This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi‐objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks‐based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances‐based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi‐Graph Representation Spatio‐Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi‐Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio‐Temporal Attention (STAtt) module is then proposed to extract spatio‐temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state‐of‐the‐art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.

Development and validation of Mayaro virus with luciferase reporter genes as a tool for antiviral assays

Arboviruses are etiological agents in an extensive group of emerging diseases with great clinical relevance in Brazil, due to the wide distribution of their vectors and the favorable environmental conditions. Among them, the Mayaro virus (MAYV) has drawn attention since its emergence as the etiologic agent of Mayaro fever, a highly debilitating disease. To study viral replication and identify new drug candidates, traditional antiviral assays based on viral antigens and/or plaque assays have been demonstrating low throughput, making it difficult to carry out larger-scale assays. Therefore, we developed and characterized two DNA-launched infectious clones reporter viruses based on the MAYV strain BeAr 20290 containing the reporter genes of firefly luciferase (FLuc) and nanoluciferase (NLuc), designated as MAYV-firefly and MAYV-nanoluc, respectively. The viruses replicated efficiently with similar properties to the parental wild-type MAYV, and luminescence expression levels reflected viral replication. Reporter genes were also preserved during passage in cell culture, remaining stably expressed for one round of passage for MAYV-firefly and three rounds for MAYV-nanoluc. Employing the infectious clone, we described the effect of Rimantadine, an FDA-approved Alzheimer's drug, as a repurposing agent for MAYV but with a broad-spectrum activity against Zika virus infection. Additionally, we validated MAYV-nanoluc as a tool for antiviral drug screening using the compound EIDD-2749 (4′-Fluorouridine), which acts as an inhibitor of alphavirus RNA-dependent RNA polymerase.

Design and performance evaluation of a novel parallel kinematic micromanipulator

The targeted placement of selected carbon nanotubes is associated with low throughput rates. Hence, this paper presents a novel stage design for the fully automatic assembly of carbon nanotube field-effect transistors (CNTFETs) via mechanical dry transfer. It constitutes the core module of an assembly machine for the precise deployment of nanotubes onto a prefabricated wafer. The mechanical dry transfer approach allows high level of carbon nanotube selectivity with the aim of enabling a high-volume fabrication of ultra-clean devices. The previous production rate of such ultra-clean devices is less than one per hour, which offers a high potential for improvement. The stage consists of a parallel kinematic mechanism (PKM) with 3 degrees of freedom (X2Y2C1) carrying two additional stacked axes (Z1C2). The PKM is suspended on a vacuum preloaded aerostatic bearing, voice coil motors (VCM) and flexure hinges. Together with a combination of high precision and resolution touch probe measurement systems close to its tool center point (TCP), high accuracy and low settling time can be achieved. Based on realistic manufacturing tolerances, a sensitivity analysis of the mechanism’s pseudo rigid body model (PRBM) suggests a theoretical closed-loop error below 0.15μm over the entire XY workspace of 20mm×2.8mm. Measurements of positioning motions show that the settling time can be decreased by the compensation of friction resistances inside the internal bearings of the VCMs.

Optimal 4QAM backscatter modulation for passive UHF CRFID tags

With the development of the Internet of Things (IoT), the amount of transmitted data is getting huge. Traditional passive UHF CRFID Tag currently adopts 2ASK or 2PSK backscatter modulation, and each symbol conveys 1 bit of information. Still, this modulation has the problems of low throughput, poor real-time, and low energy utilization. This paper proposes an optimal 4 Quadrature Amplitude Modulation (4QAM) backscatter scheme at 920 MHz, which can increase the throughput and simultaneously improve energy utilization. In this optimal scheme, the reflection coefficient modulus |Γ| is closely related to the energy. The low power of the passive tag requires that the modulation circuit’s design consider how to choose |Γ|opt (optimal reflection coefficient modulus). Therefore, we build the energy model of CRFID to explore the |Γ|opt at different symbol rates and use the |Γ|opt to design a more reasonable constellation diagram for passive tags. Finally, this paper analyzes the decoding of multinary backscatter signals, and an improved Kmeans algorithm can solve the cluster offset problem at high symbol rates. After experiments, |Γ|opt is around 0.67, the maximum throughput of 4QAM backscatter modulation can reach 16 Mbit/s, and the power consumption is 4.38 mW. Compared with the traditional binary modulation, the tag in this paper can double the throughput at the same symbol rate.

Benchmarking short-, long- and hybrid-read assemblers for metagenome sequencing of complex microbial communities.

Metagenome community analyses, driven by the continued development in sequencing technology, is rapidly providing insights in many aspects of microbiology and becoming a cornerstone tool. Illumina, Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are the leading technologies, each with their own advantages and drawbacks. Illumina provides accurate reads at a low cost, but their length is too short to close bacterial genomes. Long reads overcome this limitation, but these technologies produce reads with lower accuracy (ONT) or with lower throughput (PacBio high-fidelity reads). In a critical first analysis step, reads are assembled to reconstruct genomes or individual genes within the community. However, to date, the performance of existing assemblers has never been challenged with a complex mock metagenome. Here, we evaluate the performance of current assemblers that use short, long or both read types on a complex mock metagenome consisting of 227 bacterial strains with varying degrees of relatedness. We show that many of the current assemblers are not suited to handle such a complex metagenome. In addition, hybrid assemblies do not fulfil their potential. We conclude that ONT reads assembled with CANU and Illumina reads assembled with SPAdes offer the best value for reconstructing genomes and individual genes of complex metagenomes, respectively.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The ‘CMRxRecon’ dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

COL: a pipeline for identifying putatively functional back-splicing.

Circular RNAs (circRNAs) are a class of generally non-coding RNAs produced by back- splicing. Although the vast majority of circRNAs are likely to be products of splicing error and thereby confer no benefits to organisms, a small number of circRNAs have been found to be functional. Identifying other functional circRNAs from the sea of mostly non-functional circRNAs is an important but difficult task. Because available experimental methods for this purpose are of low throughput or versality and existing computational methods have limited reliability or applicability, new methods are needed. We hypothesize that functional back- splicing events that generate functional circRNAs (i) exhibit substantially higher back-splicing rates than expected from the total splicing amounts, (ii) have conserved splicing motifs, and (iii) show unusually high back-splicing levels. We confirm these features in back-splicing shared among human, macaque, and mouse, which should enrich functional back-splicing. Integrating the three features, we design a computational pipeline named COL for identifying putatively functional back-splicing. Different from the methods that require multiple samples, COL can predict functional back-splicing using a single sample. Under the same data requirement, COL has a lower false positive rate than that of the commonly used method that is based on the back- splicing level alone. We conclude that COL is an efficient and versatile method for rapid identification of putatively functional back-splicing and circRNAs that can be experimentally validated. COL is available at https://github.com/XuLabSJTU/COL .

Optimized design of macro‐microreactor for scale‐up of liquid–liquid chemical processes

AbstractThe scale‐up of microreactor‐based flow chemical process represents a grand challenge in chemical engineering. The small characteristic size (<1000 μm) of a typical microreactor leads to not only microscale effect (process intensification) but also low throughput. Here, we report macro‐microreactor to achieve scale‐up of liquid–liquid chemical operation with process intensification. By incorporating the designed internals based on computational fluid dynamics, the characteristic size of the macro‐microreactor is expanded into 3000–4000 μm. The optimized design of macro‐microreactor with helical‐shaped internal exhibits both similar or even stronger microscale effect and high throughput in contrast to the typical microreactor. For the liquid–liquid chemical process, seven times higher mass transfer coefficient and about half reduction of the pressure drop are realized. These macro‐microreactors would find further applications in industrial chemical manufacturing.

Discrete Element Modeling of the Breakage of Single Polyhedral Particles in the Rotary Offset Crusher

Innovation in comminution is expected to continue unabated to address the inefficiencies that are inherent in comminution circuits. The rotary offset crusher (ROC) is a new comminution device with a promising performance potential in terms of throughput due to the enhanced speed of transportation induced by the centrifugal force of the discs. However, the processes driving the comminution of particles trapped in the conical space between the two discs of the crusher are not fully understood. To gain a better insight into the comminution process in this device, discrete element modeling (DEM) simulations were conducted to study the breakage of a single particle for the crusher operated under two different dynamic conditions, i.e., (1) a stationary top disc and (2) both discs rotating at the same speed. For both scenarios, the speed of the discs was varied between 550 and 2350 rpm. Experimental testwork was also conducted with the laboratory prototype to generate the data that were used to calibrate the breakage parameters of the Ab × t10 breakage model. Simulations were performed using polyhedral UG2 ore particles that were generated with the in-built particle generator in the DEM simulator. The simulated ROC, which is operated with both discs rotating, outperformed the ROC with a stationary top disc in terms of the specific input energy and throughput. The crusher with a stationary top disc is characterized by high shear forces (suggesting a higher wear rate), specific input energies greater than 1 kWh/t, and low throughputs (<50 kg/h). The ROC operated with a stationary disc is not recommended for hard rock applications due to expected excessive wear of crushing surfaces and higher energy consumption. The freewheeling discs are recommended, but there is scope to optimize the crusher performance in terms of the power draw, size reduction, and throughput by manipulating the difference between the speeds of the discs. There is also scope to optimize the crusher performance when it is simulated with many particles. Once the full performance potential of the ROC is established, it will then be important to benchmark it against the existing crushers in the minerals industry as well as other industries where crushers are used.

High throughput cell mechanotyping of cell response to cytoskeletal modulations using a microfluidic cell deformation system.

Cellular mechanical properties influence cellular functions across pathological and physiological systems. The observation of these mechanical properties is limited in part by methods with a low throughput of acquisition or with low accessibility. To overcome these limitations, we have designed, developed, validated, and optimized a microfluidic cellular deformation system (MCDS) capable of mechanotyping suspended cells on a population level at a high throughput rate of ∼300 cells pers second. The MCDS provides researchers with a viable method for efficiently quantifying cellular mechanical properties towards defining prognostic implications of mechanical changes in pathology or screening drugs to modulate cytoskeletal integrity.

An Online Native Mass Spectrometry Approach for Fast, Sensitive, and Quantitative Assessment of Adeno-Associated Virus Capsid Content Ratios.

Adeno-associated viruses (AAVs) have emerged as a leading platform for in vivo therapeutic gene delivery and offer tremendous potential in the treatment and prevention of human disease. The fast-paced development of this growing class of therapeutics, coupled with their intrinsic structural complexity, places a high demand on analytical methods capable of efficiently monitoring product quality to ensure safety and efficacy, as well as to support manufacturing and process optimization. Importantly, the presence and relative abundance of both empty and partially filled AAV capsid subpopulations are of principal concern, as these represent the most common product-related impurities in AAV manufacturing and have a direct impact on therapeutic potential. For this reason, the capsid content, or ratio of empty and partial capsids to those packaged with the full-length therapeutic genome, has been identified by regulatory agencies as a critical quality attribute (CQA) that must be carefully controlled to meet clinical specifications. Established analytical methods for the quantitation of capsid content ratios often suffer from long turnaround times, low throughput, and high sample demands that are not well-suited to the narrow timelines and limited sample availability typical of process development. In this study, we present an integrated online native mass spectrometry platform that aims to minimize sample handling and maximize throughput and robustness for rapid and sensitive quantitation of AAV capsid content ratios. The primary advantages of this platform for AAV analysis include the ability to perform online buffer exchange under low flow conditions to maintain sample stability with minimal sample dilution, as well as the ability to achieve online charge reduction via dopant-modified desolvation gas. By exploiting the latter, enhanced spectral resolution of signals arising from empty, partial, and full AAV capsids was accomplished in the m/z domain to facilitate improved spectral interpretation and quantitation that correlated well with the industry standard analytical ultracentrifugation (AUC) method for capsid content ratio determination. The utility of this approach was further demonstrated in several applications, including the rapid and universal screening of different AAV serotypes, evaluation of capsid content for in-process samples, and the monitoring of capsid stability when subjected to thermal stress conditions.

Optimal transport for automatic alignment of untargeted metabolomic data.

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

A High-Aspect-Ratio Deterministic Lateral Displacement Array for High-Throughput Fractionation.

Future industrial applications of microparticle fractionation with deterministic lateral displacement (DLD) devices are hindered by exceedingly low throughput rates. To enable the necessary high-volume flows, high flow velocities as well as high aspect ratios in DLD devices have to be investigated. However, no experimental studies have yet been conducted on the fractionation of bi-disperse suspensions containing particles below 10 µm with DLD at a Reynolds number (Re) above 60. Furthermore, devices with an aspect ratio of more than 4:1, which require advanced microfabrication, are not known in the DLD literature. Therefore, we developed a suitable process with deep reactive ion etching of silicon and anodic bonding of a glass lid to create pressure-resistant arrays. With a depth of 120 µm and a gap of 23 µm between posts, a high aspect ratio of 6:1 was realized, and devices were investigated using simulations and fractionation experiments. With the two-segmented array of 3° and 7° row shifts, critical diameters of 8 µm and 12 µm were calculated for low Re conditions, but it was already known that vortices behind the posts can shift these values to lower critical diameters. Suspensions with polystyrene particles in different combinations were injected with an overall flow rate of up to 15 mL/min, corresponding to Re values of up to 90. Suspensions containing particle combinations of 2 µm with 10 µm as well as 5 µm with 10 µm were successfully fractionated, even at the highest flow rate. Under these conditions, a slight widening of the displacement position was observed, but there was no further reduction in the critical size as it was for Re = 60. With an unprecedented fractionation throughput of nearly 1 L per hour, entirely new applications are being developed for chemical, pharmaceutical, and recycling technologies.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Dispatcher: Resource-aware Nakamoto Blockchain via Hierarchical Topology and Adaptive Incentives

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.

Separation of full, empty, and partial adeno-associated virus capsids via anion-exchange chromatography with continuous recycling and accumulation

The field of recombinant adeno-associated virus (rAAV) gene therapy has attracted increasing attention over decades. Within the ongoing challenges of rAAV manufacturing, the co-production of impurities, such as empty and partial capsids containing no or truncated transgenes, poses a significant challenge. Due to their potential impact on drug efficacy and clinical safety, it is imperative to conduct comprehensive monitoring and characterization of these impurities prior to the release of the final gene therapy product. Nevertheless, existing analytical techniques encounter notable limitations, encompassing low throughput, long turnaround times, high sample consumption, and/or complicated data analysis. Chromatography-based analytical methods are recognized for their current Good Manufacturing Practice (cGMP) alignment, high repeatability, reproducibility, low limit of detection, and rapid turnaround times. Despite these advantages, current anion exchange high pressure liquid chromatography (AEX-HPLC) methods struggle with baseline separation of partial capsids from full and empty capsids, resulting in inaccurate full-to-empty capsid ratio, as partial capsids are obscured within peaks corresponding to empty and full capsids. In this study, we present a unique analytical AEX method designed to characterize not only empty and full capsids but also partial capsids. This method utilizes continuous N-Rich chromatography with recycling between two identical AEX columns for the accumulation and isolation of partial capsids. The development process is comprehensively discussed, covering the preparation of reference materials representing full (rAAV-LacZ), partial (rAAV-GFP), and empty (rAAV-empty) capsids, N-rich method development, fraction analysis, determination of fluorescence response factors between capsid variants, and validation through comparison with other comparative techniques.

Low complexity, high throughput, energy efficient, pipelined and reconfigurable ASIC realization architecture for multi-layer perceptron models

There has been continuously growing research interest in Artificial Neural Networks (ANNs) due to their vast acceptance in many real-world applications. This has led to the appearance of a variety of realization methods for multilayer perceptron (MLP) inference models. In this paper, we propose a generalized 3-stage, configurable, multiplier-less, massive parallel realization architecture for MLP inference models. We use offset binary coded distributive arithmetic (OBC-DA), which uses LUTs to reduce hardware requirements, to realize internal computing units of MLPs. The designing efforts to achieve a reduction in resource requirements are two-fold; (i) reduction in LUT size itself, which is achieved by exploiting symmetry induced by OBC-DA and thereby reducing LUT size by 50%, and (ii) reduction in total number of LUTs which is attained by sharing the LUTs. The resource efficiency of the proposed architecture is further improved by decomposing the LUTs into parallel and smaller LUTs. Moreover, the resource efficiency is also enhanced by utilizing the symmetry among the inputs in the MLP layer. The ASIC synthesis results for underlying MLPs establish that the proposed method for the realization of MLPs is much more efficient than the earlier reported methods and coincides with the findings of the generalized hardware-timing complexities.

Lightweight Hardware-Based Cache Side-Channel Attack Detection for Edge Devices (Edge-CaSCADe)

Cache Side-Channel Attacks (CSCAs) have been haunting most processor architectures for decades now. Existing approaches to mitigation of such attacks have certain drawbacks, namely software mishandling, performance overhead, and low throughput due to false alarms. Hence, “mitigation only when detected” should be the approach to minimize the effects of such drawbacks. We propose a novel methodology of fine-grained detection of timing-based CSCA using a hardware-based detection module. We discuss the design, implementation, and use of our proposed detection module in processor architectures. Our approach successfully detects attacks that flush secret victim information from cache memory like Flush+Reload, Flush+Flush, Prime+Probe, Evict+Probe, and Prime+Abort, commonly known as cache timing attacks. Detection is on time with minimal performance overhead. The parameterizable number of counters used in our module allows detection of multiple attacks on multiple sensitive locations simultaneously. The fine-grained nature ensures negligible false alarms, severely reducing the need for any unnecessary mitigation. The proposed work is evaluated by synthesizing the entire detection algorithm as an attack detection block, Edge-CaSCADe, in a RISC-V processor as a target example. The detection results are checked under different workload conditions with respect to the number of attackers and the number of victims having RSA-, AES-, and ECC-based encryption schemes like ECIES, and on benchmark applications like MiBench and Embench. More than 98% detection accuracy within 2% of the beginning of an attack can be achieved with negligible false alarms. The detection module has an area and power overhead of 0.9% to 2% and 1% to 2.1% for the targeted RISC-V processor core without cache for one to five counters, respectively. The detection module does not affect the processor critical path and hence has no impact on its maximum operating frequency.

Theoretical substantiation of the parameters of roller and flattening modes

ABSTRACT BACKGROUND. The proposed design of the flattening device will ensure an increase in the quality of harvested grass feeds, since the time for drying it is reduced, and the adhesive introduced increases the feed value, in addition, the loss of leaf and small-stemmed parts of plants is reduced by gluing them with stems in the total mass of the roll. AIMS. The complex of technological techniques used for the preparation of a particular feed from plants should ensure the maximum possible preservation of their nutritious, physiologically useful qualities. The purpose of this study is to improve the quality of harvested grass feeds by increasing the feed value and reducing mechanical losses. METHODS. The object of research is a mower with devices for flattening and processing grass with an adhesive-nutrient, a centrifugal-type cutting mechanism, a flattening machine with two ribbed rollers. The main condition for the stable operation of the roller apparatus is to ensure the capture of stems by rollers without unloading and clogging. RESULTS. The principle of operation of the mower differs from the prototype in that the mown grass is flattened not by rolling between two rollers, which is effective with low grass yields and low throughput of the device, but by dragging the plants between adjacent surfaces of the protrusion of one roller and the groove of another roller, and the adhesive is applied by spraying onto the grass not before mowing, and after flattening and forming a loose roll forming device. CONCLUSIONS. The analysis of the work was carried out and the theoretical justification of the parameters of the rollers and their modes during the flattening of grass was carried out. The main parameters of the flattening apparatus must satisfy both the condition of capturing the mown grass and its rolling between the rollers at a certain compression force.