An Intelligent Online Vehicle Tyre Pressure Monitoring System

This paper aims at designing an intelligent online tyre pressure monitoring system (TPMS). The objective of this work is to display the tyre pressure of the tyre and also give the indication about the quality. The data corresponding to the tyre pressure is obtained with a high precision MEMS pressure sensor. The output of the MEMS pressure sensor is amplified and transmitted to the processing unit placed on the dash of the vehicle using wireless communication (RF). The processing is carried on using fuzzy logic algorithms on LabVIEW platform. The output pressure is displayed along with the indicator representing the quality. Indicator is green when the tyre pressure is in the desired range specified by the manufacturer. Yellow when the pressure has dropped and need to be inflated. Red indicates tyre pressure is below the safety driving conditions. After testing and validating the entire system using LabVIEW. The entire code is converted to verilog code and dumped on to FPGA chip (Spartan 3E) using FPGA module of LabVIEW with CompactRIO, for implementation of FPGA chip on real time system.DOI:http://dx.doi.org/10.11591/ijece.v2i3.23

CRISPR/Cas9-mediated lipoxygenase gene-editing in yellow pea leads to major changes in fatty acid and flavor profiles

IntroductionAlthough pulses are nutritious foods containing high amounts of protein, fiber and phytochemicals, their consumption and use in the food industry have been limited due to the formation of unappealing flavors/aromas described as beany, green, and grassy. Lipoxygenase (LOX) enzymes are prevalent among pulse seeds, and their activity can lead to the formation of specific volatile organic compounds (VOCs) from certain polyunsaturated fatty acids (PUFAs). As a widespread issue in legumes, including soybean, these VOCs have been linked to certain unappealing taste perception of foods containing processed pulse seeds.MethodsTo address this problem in pea and as proof of principle to promote the wider use of pulses, a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) construct was designed to create null alleles (knockouts) of PsLOX2 which had been implicated in the generation of VOCs in peas.Results and discussionSuccessful CRISPR/Cas9-mediated LOX gene editing of stable transgenic pea lines (TGP) was confirmed by DNA sequencing of the wild type (WT) and TGP pslox2 mutant lines. These lines were also assessed for LOX activity, PUFA levels, and VOCs. Compared to WT peas, the TGP lines showed a significant reduction (p < 0.05) in LOX activity and in the concentration of key VOCs, including hexanal, 2-hexenal, heptanal, (E)-2-heptenal, (E,E)-2,4-heptadienal, 1-octen-3-ol, octanal, (E)-2-octenal (E,E)-2,4-nonadienal and furan-2-pentyl. The content of two essential PUFAs, linoleic and α-linolenic acids, the known substrates of LOX in plants, was higher in TGP flours, indicating the efficacy of the CRISPR-mediated gene editing in minimizing their oxidation and the further modification of PUFAs and their products. The collection of VOCs from the headspace of ground pea seeds, using a portable eNose also distinguished the TGP and WT lines. Multiple regression analysis showed that LOX activity correlated with the two VOCs, heptanal and (E,E)-2,4-heptadienal in pea flours. Partial Least Squares Regression (PLS-R) plot for selected PUFAs, VOCs, and sensor responses in WT and TGP lines showed distinct clusters for WT and TGP lines. Together this data demonstrates the utility of CRISPR mediated mutagenesis of PsLOX2 to quickly improve aroma and fatty acid (FA) profiles of pea seeds of an elite Canadian variety

Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (<i>Linum usitatissimum</i>)

Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 °C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm2, respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds’ root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth

Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (Linum usitatissimum)

Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 &deg;C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm2, respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds&rsquo; root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth

Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities

HARP: Hierarchical Attention Oriented Region-Based Processing for High-Performance Computation in Vision Sensor

Cameras are widely adopted for high image quality with the rapid advancement of complementary metal-oxide-semiconductor (CMOS) image sensors while offloading vision applications’ computation to the cloud. It raises concern for time-critical applications such as autonomous driving, surveillance, and defense systems since moving pixels from the sensor’s focal plane are expensive. This paper presents a hardware architecture for smart cameras that understands the salient regions from an image frame and then performs high-level inference computation for sensor-level information creation instead of transporting raw pixels. A visual attention-oriented computational strategy helps to filter a significant amount of redundant spatiotemporal data collected at the focal plane. A computationally expensive learning model is then applied to the interesting regions of the image. The hierarchical processing in the pixels’ data path demonstrates a bottom-up architecture with massive parallelism and gives high throughput by exploiting the large bandwidth available at the image source. We prototype the model in field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) for integrating with a pixel-parallel image sensor. The experiment results show that our approach achieves significant speedup while in certain conditions exhibits up to 45% more energy efficiency with the attention-oriented processing. Although there is an area overhead for inheriting attention-oriented processing, the achieved performance based on energy consumption, latency, and memory utilization overcomes that limitation

HARP: Hierarchical Attention Oriented Region-Based Processing for High-Performance Computation in Vision Sensor

Cameras are widely adopted for high image quality with the rapid advancement of complementary metal-oxide-semiconductor (CMOS) image sensors while offloading vision applications’ computation to the cloud. It raises concern for time-critical applications such as autonomous driving, surveillance, and defense systems since moving pixels from the sensor’s focal plane are expensive. This paper presents a hardware architecture for smart cameras that understands the salient regions from an image frame and then performs high-level inference computation for sensor-level information creation instead of transporting raw pixels. A visual attention-oriented computational strategy helps to filter a significant amount of redundant spatiotemporal data collected at the focal plane. A computationally expensive learning model is then applied to the interesting regions of the image. The hierarchical processing in the pixels’ data path demonstrates a bottom-up architecture with massive parallelism and gives high throughput by exploiting the large bandwidth available at the image source. We prototype the model in field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) for integrating with a pixel-parallel image sensor. The experiment results show that our approach achieves significant speedup while in certain conditions exhibits up to 45% more energy efficiency with the attention-oriented processing. Although there is an area overhead for inheriting attention-oriented processing, the achieved performance based on energy consumption, latency, and memory utilization overcomes that limitation

A Two-Step Method for Obtaining Highly Pure Cas9 Nuclease for Genome Editing, Biophysical, and Structural Studies

Cas9 is a site-specific RNA-guided endonuclease (RGEN) that can be used for precise genome editing in various cell types from multiple species. Ribonucleoprotein (RNP) complexes, which contains the Cas9 protein in complex with a guide RNA, are sufficient for the precise editing of genomes in various cells. This DNA-free method is more specific in editing the target sites and there is no integration of foreign DNA into the genome. Also, there are ongoing studies into the interactions of Cas9 protein with modified guide RNAs, as well as structure-activity studies of Cas9 protein and its variants. All these investigations require highly pure Cas9 protein. A single-step metal affinity enrichment yielding impure Cas9 is the most common method of purification described. This is sufficient for many gene editing applications of this protein. However, to obtain Cas9 of higher purity, which might be essential for biophysical characterization, chemical modifications, and structural investigations, laborious multi-step protocols are employed. Here, we describe a two-step Cas9 purification protocol that uses metal affinity enrichment followed by cation exchange chromatography. This simple method can yield a milligram of highly pure Cas9 protein per liter of culture in a single day

Latest biotechnology tools and targets for improving abiotic stress tolerance in protein legumes

International audienceProtein legumes are among the most important crops for sustainable agriculture and global food security for decades to come. Unfortunately, they are subject to several abiotic stresses that severely limit their productivity, and this phenomenon is increasing with climate change. New Plant Breeding Technologies (NPBTs) offer novel alternatives to improve the plant performance of crops against such environmental constraints. However, the recalcitrance to transgenesis and in vitro regeneration has delayed such advances for protein legumes. This article reviews recent advances in legume crop biotechnological approaches to improve their tolerance to abiotic stresses including drought, high salinity, heat and cold, and heavy metal stress. In addition to these improvements, obtained mainly through transgenesis, we surveyed the application of tools such as CRISPR/Cas and RNA interference in legumes in a context of abiotic stress tolerance, and suggested a path to follow for gene control by these tools in legume plants, organs, or cells. Furthermore, we also discussed promising molecular targets, perspectives, and the way ahead for enhancing abiotic stress tolerance