Search results for: sparse matrix-vector multiplication

Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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Authors: Osama Abdallah Mohammed Enan, Amin Babiker A/Nabi Mustafa

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The aim of this study is to evaluate and analyze different parameters of WCDMA (wideband code division multiplication). Moreover, this study also incorporates brief yet throughout analysis of WCDMA’s components as well as its internal architecture. This study also examines different power controls. These power controls may include open loop power control, closed or inner group loop power control and outer loop power control. Different handover techniques or methods of WCDMA are also illustrated in this study. These handovers may include hard handover, inter system handover and soft and softer handover. Different duplexing techniques are also described in the paper. This study has also presented an idea about different parameters of WCDMA that leads the system towards QoS issues. This may help the operator in designing and developing adequate network configuration. In addition to this, the study has also investigated various parameters including Bit Energy per Noise Spectral Density (Eb/No), Noise rise, and Bit Error Rate (BER). After simulating these parameters, using MATLAB environment, it was investigated that, for a given Eb/No value the system capacity increase by increasing the reuse factor. Besides that, it was also analyzed that, noise rise is decreasing for lower data rates and for lower interference levels. Finally, it was examined that, BER increase by using one type of modulation technique than using other type of modulation technique.

Keywords: duplexing, handover, loop power control, WCDMA

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Rachna Jain, Sushila Madan, Bindu Garg

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Cloud computing is the key powerhouse in numerous organizations due to shifting of their data to the cloud environment. In recent years it has been observed that cloud-based-services are being used on large scale for content storage, distribution and processing. Various issues have been observed in cloud computing environment that need to be addressed. Security and privacy are found topmost concern area. In this paper, a novel security model is proposed to secure data by utilizing CDN services like image to icon conversion. CDN Service is a content delivery service which converts an image to icon, word to pdf & Latex to pdf etc. Presented model is used to convert an image into icon by keeping image secret. Here security of image is imparted so that image should be encrypted and decrypted by data owners only. It is also discussed in the paper that how server performs multiplication and selection on encrypted data without decryption. The data can be image file, word file, audio or video file. Moreover, the proposed model is capable enough to multiply images, encrypt them and send to a server application for conversion. Eventually, the prime objective is to encrypt an image and convert the encrypted image to image Icon by utilizing homomorphic encryption.

Keywords: cloud computing, user data security, homomorphic encryption, image multiplication, CDN service

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Authors: Abubakar Sadiq Mensah, Hassan Usman

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The study compared the effectiveness of Audition, Multiplication, subtraction and Division (AMSD) and Addition, subtraction, Multiplication and Division (ASMD), sequence of teaching these four basic operations in mathematics to primary one pupil’s in Katsina Local Government, Katsina State. The study determined the sequence that was more effective and mostly adopted by teachers of the operations. One hundred (100) teachers and sixty pupils (60) from primary one were used for the study. The pupils were divided into two equal groups. The researcher taught these operations to each group separately for four weeks (4 weeks). Group one was taught using the ASMD sequence, while group two was taught using ASMD sequence. In order to generate the needed data for the study, questionnaires and tests were administered on the samples. Data collected were analyzed and major findings were arrived at: (i) Two primary mathematics text books were used in all the primary schools in the area; (ii) Each of the textbooks contained the ASMD sequence; (iii) 73% of the teachers sampled adopted the ASMD sequence of teaching these operations; and (iv) Group one of the pupils (taught using AMSD sequence) performed significantly better than their counter parts in group two (taught using AMSD sequence). On the basis of this, the researcher concluded that the AMSD sequence was more effective in teaching the operations than the ASMD sequence. Consequently, the researcher concluded that primary schools teachers, authors of primary mathematics textbooks, and curriculum planner should adopt the AMSD sequence of teaching these operations.

Keywords: matematic, high school, four basic operations, effectiveness of teaching

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Authors: Agustine Melviana, Rizkita Esyanti

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Stevia plant contains steviol glycosides which is estimated to be 300 times sweeter than sucrose. However in Indonesia, conventional (in vivo) propagation of Stevia rebaudiana was not effective due to a poor result. Therefore, alternative methods to propagate S. rebaudiana plants is needed, one of it is using in vitro method. Multiplication with a large quantity of stevia biomass in relatively short period can be conducted by using TIS RITA® (Recipient for Automated Temporary Immersion System). The objective of this study was to evaluate the effect of immersion period of the medium on growth and the medium bioconversion into the production of shoot biomass. The study was conducted to determine the effect of different intensity period of medium to enhance biomass of stevia shoots. Shoot culture of S. rebaudiana was grown in full strength MS medium supplemented with 1 ppm Kinetin. RITA® bioreactors were set up with two different immersion periods, 15 min (RITA® 15) and 30 min (RITA® 30), scheduled every 6 hours and incubated for 21 days. The result indicated that immersion period affected the biomass and growth rate (µ). Thirty-minutes immersion showed greater percentage of shoot multiplication (93.44 ± 0.83%), percentage of leaf growth (85.24 ± 5.99%), growth rate (0.042 ± 0.001 g/day), and productivity (0.066 g/L medium/day) compared to that immersed in RITA® 15 min (76.90 ± 4.85%; 79.73 ± 7.76; 0.045 ± 0.004 g/day, and 0.045 g/L medium/day respectively). Enhancement of biomass in RITA® 30 reached 1,702 ± 0,114 gr, whereas in RITA® 15 only 0,953 ± 0,093 gr. Additionally, the pattern of sucrose, mineral, and inorganic compounds consumption followed the growth of plant biomass for both systems. In conclusion, the bioconversion efficiency from medium to biomass in RITA® 30 is better than RITA® 15.

Keywords: intensity period, shoot culture, Stevia rebaudiana, TIS RITA®

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: Prittesh Patel, Ramar Krishnamurthy

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In the present study, nine strains of C. falcatum obtained from different places and cultivars were characterized for sporulation, growth rate, and 18S rRNA gene sequence. All isolates had characteristic fast-growing sparse and fleecy aerial mycelia on potato dextrose agar with sickle shape conidia (length x width: varied from 20.0 X 3.89 to 25.52 X 5.34 μm) and blackish to orange acervuli with setae (length x width: varied from 112.37X 2.78 to 167.66 X 6.73 μm). They could be divided into two groups on the base of morphology; P1, dense mycelia with concentric growth and P2, sparse mycelia with uneven growth. Genomic DNA isolation followed by PCR amplification with ITS1 and ITS4 primer produced ~550bp amplicons for all isolates. Phylogeny generated by 18S rRNA gene sequence confirmed the variation in isolates and mainly grouped into two clusters; cluster 1 contained CoC671 isolates (cfNAV and cfPAR) and Co86002 isolate (cfTIM). Other isolates cfMAD, cfKAM, and cfMAR were grouped into cluster 2. Remaining isolates did not fall into any cluster. Isolate cfGAN, collected from Co86032 was found highly diverse of all the nine isolates. In a nutshell, we found considerable genetic divergence and morphological variation within C. falcatum accessions collected from different areas of south Gujarat, India and these can be used for the breeding program.

Keywords: Colletotrichum falcatum, ITS, morphology, red rot, sugarcane

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Authors: S. Gharbi, S. Labidi, M. Mars, M. Chelli, F. Ladeb

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The purpose of this study is to reduce radiation dose for chest CT examination by including Tube Current Modulation (TCM) to a standard CT protocol. A scan of an anthropomorphic male Alderson phantom was performed on a 128-slice scanner. The estimation of effective dose (ED) in both scans with and without mAs modulation was done via multiplication of Dose Length Product (DLP) to a conversion factor. Results were compared to those measured with a CT-Expo software. The size specific dose estimation (SSDE) values were obtained by multiplication of the volume CT dose index (CTDIvol) with a conversion size factor related to the phantom’s effective diameter. Objective assessment of image quality was performed with Signal to Noise Ratio (SNR) measurements in phantom. SPSS software was used for data analysis. Results showed including CARE Dose 4D; ED was lowered by 48.35% and 51.51% using DLP and CT-expo, respectively. In addition, ED ranges between 7.01 mSv and 6.6 mSv in case of standard protocol, while it ranges between 3.62 mSv and 3.2 mSv with TCM. Similar results are found for SSDE; dose was higher without TCM of 16.25 mGy and was lower by 48.8% including TCM. The SNR values calculated were significantly different (p=0.03<0.05). The highest one is measured on images acquired with TCM and reconstructed with Filtered back projection (FBP). In conclusion, this study proves the potential of TCM technique in SSDE and ED reduction and in conserving image quality with high diagnostic reference level for thoracic CT examinations.

Keywords: anthropomorphic phantom, computed tomography, CT-expo, radiation dose

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Authors: Sandeep Santosh, O. P. Sahu

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In this paper, performance comparison of wideband covariance matrix sparse representation (W-CMSR) method with other existing wideband Direction of Arrival (DOA) estimation methods has been made.W-CMSR relies less on a priori information of the incident signal number than the ordinary subspace based methods.Consider the perturbation free covariance matrix of the wideband array output. The diagonal covariance elements are contaminated by unknown noise variance. The covariance matrix of array output is conjugate symmetric i.e its upper right triangular elements can be represented by lower left triangular ones.As the main diagonal elements are contaminated by unknown noise variance,slide over them and align the lower left triangular elements column by column to obtain a measurement vector.Simulation results for W-CMSR are compared with simulation results of other wideband DOA estimation methods like Coherent signal subspace method (CSSM), Capon, l1-SVD, and JLZA-DOA. W-CMSR separate two signals very clearly and CSSM, Capon, L1-SVD and JLZA-DOA fail to separate two signals clearly and an amount of pseudo peaks exist in the spectrum of L1-SVD.

Keywords: W-CMSR, wideband direction of arrival (DOA), covariance matrix, electrical and computer engineering

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Authors: P. G. Romeo

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A Lie group is a highly sophisticated structure which is a smooth manifold whose underlying set of elements is equipped with the structure of a group such that the group multiplication and inverse-assigning functions are smooth. This structure was introduced by the Norwegian mathematician So- phus Lie who founded the theory of continuous groups. The Lie groups are well developed and have wide applications in areas including Mathematical Physics. There are several advances and generalizations for Lie groups and Lie groupoids is one such which is termed as a "many-object generalization" of Lie groups. A groupoid is a category whose morphisms are all invertible, obviously, every group is a groupoid but not conversely. Definition 1. A Lie groupoid G ⇒ M is a groupoid G on a base M together with smooth structures on G and M such that the maps α, β: G → M are surjective submertions, the object inclusion map x '→ 1x, M → G is smooth, and the partial multiplication G ∗ G → G is smooth. A bundle is a triple (E, p, B) where E, B are topological spaces p: E → B is a map. Space B is called the base space and space E is called total space and map p is the projection of the bundle. For each b ∈ B, the space p−1(b) is called the fibre of the bundle over b ∈ B. Intuitively a bundle is regarded as a union of fibres p−1(b) for b ∈ B parametrized by B and ’glued together’ by the topology of the space E. A cross-section of a bundle (E, p, B) is a map s: B → E such that ps = 1B. Example 1. Given any space B, a product bundle over B with fibre F is (B × F, p, B) where p is the projection on the first factor. Definition 2. A principal bundle P (M, G, π) consists of a manifold P, a Lie group G, and a free right action of G on P denoted (u, g) '→ ug, such that the orbits of the action coincide with the fibres of the surjective submersion π : P → M, and such that M is covered by the domains of local sections σ: U → P, U ⊆ M, of π. Definition 3. A Lie group bundle, or LGB, is a smooth fibre bundle (K, q, M ) in which each fibre (Km = q−1(m), and the fibre type G, has a Lie group structure, and for which there is an atlas {ψi: Ui × G → KUi } such that each {ψi,m : G → Km}, is an isomorphism of Lie groups. A morphism of LGB from (K, q, M ) to (K′, q′, M′) is a morphism (F, f ) of fibre bundles such that each Fm: Km → K′ is a morphism of Lie groups. In this paper, we will be discussing the Lie groupoid bundles. Here it is seen that to a Lie groupoid Ω on base B there is associated a collection of principal bundles Ωx(B, Ωx), all of which are mutually isomorphic and conversely, associated to any principal bundle P (B, G, p) there is a groupoid called the Ehresmann groupoid which is easily seen to be Lie. Further, some interesting properties of the category of Lie groupoids and bundles will be explored.

Keywords: groupoid, lie group, lie groupoid, bundle

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Authors: Tal Remez, Or Litany, Alex Bronstein

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The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.

Keywords: binary pixels, maximum likelihood, neural networks, sparse coding

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Authors: Damir Latypov

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A hybrid classical-quantum algorithm to solve boundary integral equations (BIE) arising in problems of electromagnetic and acoustic scattering is proposed. The quantum speed-up is due to a Quantum Linear System Algorithm (QLSA). The original QLSA of Harrow et al. provides an exponential speed-up over the best-known classical algorithms but only in the case of sparse systems. Due to the non-local nature of integral operators, matrices arising from discretization of BIEs, are, however, dense. A QLSA for dense matrices was introduced in 2017. Its runtime as function of the system's size N is bounded by O(√Npolylog(N)). The run time of the best-known classical algorithm for an arbitrary dense matrix scales as O(N².³⁷³). Instead of exponential as in case of sparse matrices, here we have only a polynomial speed-up. Nevertheless, sufficiently high power of this polynomial, ~4.7, should make QLSA an appealing alternative. Unfortunately for the QLSA, the asymptotic separability of the Green's function leads to high compressibility of the BIEs matrices. Classical fast algorithms such as Multilevel Fast Multipole Method (MLFMM) take advantage of this fact and reduce the runtime to O(Nlog(N)), i.e., the QLSA is only quadratically faster than the MLFMM. To be truly impactful for computational electromagnetics and acoustics engineers, QLSA must provide more substantial advantage than that. We propose a computational scheme which combines elements of the classical fast algorithms with the QLSA to achieve the required performance.

Keywords: quantum linear system algorithm, boundary integral equations, dense matrices, electromagnetic scattering theory

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Authors: T. Punjansing, M. Nakkuntod, S. Homchan, P. Inthima, A. Kongbangkerd

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The influence of organic supplements on growth and multiplication efficiency of Phaius tankervilleae var. alba seedlings was investigated. 12 week-old seedlings were cultured on half-strength semi-solid Murashige and Skoog (MS) medium supplemented with 30 g/L sucrose, 8 g/L agar and various concentrations of coconut water (0, 50, 100, 150 and 200 mL/L) combined with potato extract (0, 25 and 50 g/L) and the pH was adjusted to 5.8 prior to autoclaving. The cultures were then kept under constant photoperiod (16 h light: 8 h dark) at 25 ± 2 °C for 12 weeks. The highest number of shoots (3.0 shoots/explant) was obtained when cultured on the medium added with 50 ml/L coconut water and 50 g/L potato extract whereas the highest number of leaves (5.9 leaves/explant) and roots (6.1 roots/explant) could receive on the medium supplemented with 150 ml/L coconut water and 50 g/L potato extract. with 150 ml/L coconut water and 50 g/L potato extract. Additionally, plantlets of P. tankervilleae var. alba were transferred to grow into seven different substrates i.e. soil, sand, coconut husk chip, soil-sand mix (1: 1), soil-coconut husk chip mix (1: 1), sand-coconut husk chip mix (1: 1) and soil-sand-coconut husk chip mix (1: 1: 1) for four weeks. The results found that acclimatized plants showed 100% of survivals when sand, coconut husk chip and sand-coconut husk chip mix are used as substrates. The number of leaves induced by sand-coconut husk chip mix was significantly higher than that planted in other substrates (P > 0.05). Meanwhile, no significant difference in new shoot formation among these substrates was observed (P < 0.05). This precursory developing protocol was likely to be applied for more large scale of plant production as well as conservation of germplasm of this orchid species.

Keywords: organic supplements, acclimatization, Phaius tankervilleae var. alba, orchid

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Authors: Rashmi Ranade, Neelu Joshi

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Barleria prionitis L. is a well explored Indian medicinal plant valued for its stem and leaf which forms an important ingredient of many Ayurvedic formulations. It is used for the treatment of various disorders like toothache, bleeding gums, strengthening gums, whooping cough, inflammation, arthritis, enlargement of scrotum and sciatica etc. The plant is propagated vegetatively through stem cuttings. Frequent harvesting of this plant has led to the shortage of planting material, and it has acquired the status of vulnerable plant species. Plant tissue culture technology offers a very good alternative for propagation and conservation of such plant species. The present investigation was undertaken to develop in vitro regeneration protocol for B. prionitis L. via callus organogenesis pathway. Stem and leaf explants were used for this purpose. Different media and plant growth regulators were optimized to develop the protocol. The problem of phenol secretion and browning and in vitro cultures at the establishment phase was successfully curbed with the usage of antibrowning agents such as ascorbic acid and activated charcoal. Optimum shoot multiplication was achieved by the use of liquid media and incorporation of silver nitrate and TIBA (triiodobenzoic acid) into the media. High percent rooting (76%) was observed on WPM media supplemented with IBA (2.0 mg/l), IAA (0.5 mg/l), GA3(0.5) and activated charcoal(500 mg/l). The rooted plantlets were subjected to in vitro hardening on sterile potting mix (soil:farmyard manure:compost; 1:2:1) and acclimatized under greenhouse conditions. Around 85% survival of plantlets was recorded upon acclimatization. This lab scale protocol would be tested for in vitro scaling up production of B. prionitis L.

Keywords: explant browning, liquid culture, micropropagation, shoot multiplication, phenolic secretion

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Authors: Keisuke Takahata, Hiroshi Suetsugu

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Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation.

Keywords: digital terrain model, satellite LiDAR, gaussian processes, uncertainty quantification

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Authors: Wen Shyong Hsieh, Chih Hsueh Lin

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In this paper, we combine the concepts of chameleon hash function (CHF) and identification based cryptography (IBC) to build a message authentication environment for VANET under sparse RSUs. Based on the CHF, TA keeps two common secrets that will be embedded to all identities to be as the evidence of mutual trusting. TA will issue one original identity to every RSU and vehicle. An identity contains one public ID and one private key. The public ID, includes three components: pseudonym, random key, and public key, is used to present one entity and can be verified to be a legal one. The private key is used to claim the ownership of the public ID. Based on the concept of IBC, without any negotiating process, a CHF pairing key multiplied by one private key and other’s public key will be used for mutually trusting and to be utilized as the session key of secure communicating between RSUs and vehicles. To help the vehicles to do message authenticating, the RSUs are assigned to response the vehicle’s temple identity request using two short time secretes that are broadcasted by TA. To light the loading of request information, one day is divided into M time slots. At every time slot, TA will broadcast two short time secretes to all valid RSUs for that time slot. Any RSU can response the temple identity request from legal vehicles. With the collected announcement of public IDs from the neighbor vehicles, a vehicle can set up its neighboring set, which includes the information about the neighbor vehicle’s temple public ID and temple CHF pairing key that can be derived by the private key and neighbor’s public key and will be used to do message authenticating or secure communicating without the help of RSU.

Keywords: Internet of Vehicles (IOV), Vehicular Ad-hoc Networks (VANETs), Chameleon Hash Function (CHF), message authentication

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Authors: Chen Zhang, Nan Chen

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High-dimensional process monitoring becomes increasingly important in many application domains, where usually the process distribution is unknown and much more complicated than the normal distribution, and the between-stream correlation can not be neglected. However, since the process dimension is generally much bigger than the reference sample size, most traditional nonparametric multivariate control charts fail in high-dimensional cases due to the curse of dimensionality. Furthermore, when the process goes out of control, the influenced variables are quite sparse compared with the whole dimension, which increases the detection difficulty. Targeting at these issues, this paper proposes a new nonparametric monitoring scheme for high-dimensional processes. This scheme first projects the high-dimensional process into several subprocesses using random projections for dimension reduction. Then, for every subprocess with the dimension much smaller than the reference sample size, a local nonparametric control chart is constructed based on the spatial rank test to detect changes in this subprocess. Finally, the results of all the local charts are fused together for decision. Furthermore, after an out-of-control (OC) alarm is triggered, a diagnostic framework is proposed. using the square-root LASSO. Numerical studies demonstrate that the chart has satisfactory detection power for sparse OC changes and robust performance for non-normally distributed data, The diagnostic framework is also effective to identify truly changed variables. Finally, a real-data example is presented to demonstrate the application of the proposed method.

Keywords: random projection, high-dimensional process control, spatial rank, sequential change detection

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Authors: Hajji Lobna, Chattaoui Mayssa, Regaieg Hajer, M'Hamdi-Boughalleb Naima, Rhouma Ali, Horrigue-Raouani Najet

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In this study, three local isolates of Trichoderma (Tr1: T. viride, Tr2: T. harzianum and Tr3: T. asperellum) were isolated and evaluated for their biocontrol effectiveness under in vitro conditions and in greenhouse. In vitro bioassay revealed a biopotential control against Fusarium oxysporum f. sp. radicis lycopersici and Meloidogyne javanica (RKN) separately. All species of Trichoderma exhibited biocontrol performance and (Tr1) Trichoderma viride was the most efficient. In fact, growth rate inhibition of Fusarium oxysporum f. sp. radicis lycopersici (FORL) was reached 75.5% with Tr1. Parasitism rate of root-knot nematode was 60% for juveniles and 75% for eggs with the same one. Pots experiment results showed that Tr1 and Tr2, compared to chemical treatment, enhanced the plant growth and exhibited better antagonism against root-knot nematode and root-rot fungi separated or combined. All Trichoderma isolates revealed a bioprotection potential against Fusarium oxysporum f. sp. radicis lycopersici. When pathogen fungi inoculated alone, Fusarium wilt index and browning vascular rate were reduced significantly with Tr1 (0.91, 2.38%) and Tr2 (1.5, 5.5%), respectively. In the case of combined infection with Fusarium and nematode, the same isolate of Trichoderma Tr1 and Tr2 decreased Fusarium wilt index at 1.1 and 0.83 and reduced the browning vascular rate at 6.5% and 6%, respectively. Similarly, the isolate Tr1 and Tr2 caused maximum inhibition of nematode multiplication. Multiplication rate was declined at 4% with both isolates either tomato infected by nematode separately or concomitantly with Fusarium. The chemical treatment was moderate in activity against Meloidogyne javanica and Fusarium oxysporum f. sp. radicis lycopersici alone and combined.

Keywords: trichoderma spp., meloidogyne javanica, Fusarium oxysporum f.sp.radicis lycopersici, biocontrol

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Authors: Nidhi Jindal, Ashok Chaudhury, Manisha Mangal

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Andrographis paniculata, (Burm f.) Wallich ex. Nees (Family Acanthaceae) popularly known as King of Bitters, is an important medicinal herb. It has an astonishingly wide range of medicinal properties such as anti-inflammatory,antidiarrhoeal, antiviral, antimalarial, hepatoprotective, cardiovascular, anticancer, and immunostimulatory activities. It is widely cultivated in southern Asia. Though propagation of this herb generally occurs through seeds, it has many germination problems which intrigued scientists to work out on the alternative techniques for its mass production. The potential of tissue culture techniques as an alternative tool for AP multiplication was found to be promising. However, the high mortality rate of explants caused by phenolic browning of explants is one of the difficulties reported. Low multiplication rates were reported in the proliferation phase, as well as cultures decline characterized by leaf fall and loss of overall vigor. In view of above problems, a study was undertaken to overcome seed dormancy to improve germination potential and to investigate further on the possible means for successful proliferation of cultures via preventive approaches to overcome failures caused by phenolic browning. Experiments were conducted to improve germination potential and among all the chemical and mechanical trials, scarification of seeds with sand paper proved to be the best method to enhance the germination potential (82.44%) within 7 days. Similarly, several pretreatments and media combinations were tried to overcome browning of explants leading to the conclusion that addition of 0.1% citric acid and 0.2% of ascorbic acid in the media followed by rapid sub culturing of explants controlled browning and decline of explants by 67.45%.

Keywords: plant tissue culture, empirical measure, germination, tissue culture

Procedia PDF Downloads 393

Authors: N. Anjana

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Worldwide, the prevalence of mental illnesses is increasing. The issues of persons with mental illness and their caregivers have been well documented in the literature. However, data regarding the factors affecting the caregiving experience of children with parental mental illnesses is sparse. This systematic review aimed to examine the existing literature of the factors affecting the caregiving experience of children of parents with mental illnesses. A comprehensive search of databases such as PubMed, EBSCO, JSTOR, ProQuest Central, Taylor and Francis Online, and Google Scholar were performed to identify peer-reviewed papers examining the factors associated with caregiving experiences of children with parental mental illnesses such as schizophrenia and major depression, for the 10-year period ending November 2019. Two researchers screened studies for eligibility. One researcher extracted data from eligible studies while a second performed verification of results for accuracy and completeness. Quality appraisal was conducted by both reviewers. Data describing major factors associated with caregiving experiences of children with parental mental illnesses were synthesized and reported in narrative form. Five studies were considered eligible and included in this review. Findings are organized under major themes such as the impact of parental mental illness on children’s daily life, how children provide care to their mentally ill parents as primary carers, social and relationship factors associated with their caregiving, positive and negative experiences in caregiving and how children cope with their experiences with parental mental illnesses. Literature relating to the caregiving experiences of children with parental mental illnesses is sparse. More research is required to better understand the children’s caregiving experiences related to parental mental illnesses so as to better inform management for enhancing their mental health, wellbeing, and caregiving practice.

Keywords: caregiving experience, children, parental mental illnesses, wellbeing

Procedia PDF Downloads 116

Authors: Feng Yue, Fei Dai

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With the rapid development of urbanization process in China, its environmental protection pressure is severely tested. So, analyzing and optimizing the landscape pattern is an important measure to ease the pressure on the ecological environment. This paper takes Wuhan Urban Development Zone as the research object, and studies its landscape pattern evolution and quantitative optimization strategy. First, remote sensing image data from 1990 to 2015 were interpreted by using Erdas software. Next, the landscape pattern index of landscape level, class level, and patch level was studied based on Fragstats. Then five indicators of ecological environment based on National Environmental Protection Standard of China were selected to evaluate the impact of landscape pattern evolution on the ecological environment. Besides, the cost distance analysis of ArcGIS was applied to simulate wildlife migration thus indirectly measuring the improvement of ecological environment quality. The result shows that the area of land for construction increased 491%. But the bare land, sparse grassland, forest, farmland, water decreased 82%, 47%, 36%, 25% and 11% respectively. They were mainly converted into construction land. On landscape level, the change of landscape index all showed a downward trend. Number of patches (NP), Landscape shape index (LSI), Connection index (CONNECT), Shannon's diversity index (SHDI), Aggregation index (AI) separately decreased by 2778, 25.7, 0.042, 0.6, 29.2%, all of which indicated that the NP, the degree of aggregation and the landscape connectivity declined. On class level, the construction land and forest, CPLAND, TCA, AI and LSI ascended, but the Distribution Statistics Core Area (CORE_AM) decreased. As for farmland, water, sparse grassland, bare land, CPLAND, TCA and DIVISION, the Patch Density (PD) and LSI descended, yet the patch fragmentation and CORE_AM increased. On patch level, patch area, Patch perimeter, Shape index of water, farmland and bare land continued to decline. The three indexes of forest patches increased overall, sparse grassland decreased as a whole, and construction land increased. It is obvious that the urbanization greatly influenced the landscape evolution. Ecological diversity and landscape heterogeneity of ecological patches clearly dropped. The Habitat Quality Index continuously declined by 14%. Therefore, optimization strategy based on greenway network planning is raised for discussion. This paper contributes to the study of landscape pattern evolution in planning and design and to the research on spatial layout of urbanization.

Keywords: landscape pattern, optimization strategy, ArcGIS, Erdas, landscape metrics, landscape architecture

Procedia PDF Downloads 133

Authors: Luis A. Lizama-Perez, Manuel J. Linares, Mauricio Lopez

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We introduce a method to perform remote user authentication on what we call non-invertible cryptography. It exploits the fact that the multiplication of an invertible integer and a non-invertible integer in a ring Zn produces a non-invertible integer making infeasible to compute factorization. The protocol requires the smallest key size when is compared with the main public key algorithms as Diffie-Hellman, Rivest-Shamir-Adleman or Elliptic Curve Cryptography. Since we found that the unique opportunity for the eavesdropper is to mount an exhaustive search on the keys, the protocol seems to be post-quantum.

Keywords: invertible, non-invertible, ring, key transfer

Procedia PDF Downloads 141

Authors: Gideon Baffoe

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The concept of neighbourhood remains highly relevant in urban studies. However, until now, no attempt has been made to statistically chart the field. This study aims to provide a macroscopic overview using bibliometric analysis of the main characteristics of neighbourhood research in order to understand the academic landscape. The study analyses the emergence and evolution of the concept of neighbourhood in published research, conceptual and intellectual structures as well as scholarship collaboration. It is found that topics related to the local economy of neighbourhoods are sparse, suggesting a major gap in the literature.

Keywords: neighbourhood, global south, bibliometric analysis, scholarship

Procedia PDF Downloads 107

Authors: A. Andreasyan, C. Connors

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The Elliptic Curve Digital Signature algorithm-based X509v3 certificates are becoming more popular due to their short public and private key sizes. Moreover, these certificates can be stored in Internet of Things (IoT) devices, with limited resources, using less memory and transmitted in network security protocols, such as Internet Key Exchange (IKE), Transport Layer Security (TLS) and Secure Shell (SSH) with less bandwidth. The proposed method gives another advantage, in that it increases the performance of the above-mentioned protocols in terms of key exchange by saving one scalar multiplication operation.

Keywords: cryptography, elliptic curve digital signature algorithm, key exchange, network security protocol

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Authors: Khaled Shehata, Hanady Hussien, Sara Yehia

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Securing the data stored on E-passport is a very important issue. RSA encryption algorithm is suitable for such application with low data size. In this paper the design and implementation of 1024 bit-key RSA encryption and decryption module on an FPGA is presented. The module is verified through comparing the result with that obtained from MATLAB tools. The design runs at a frequency of 36.3 MHz on Virtex-5 Xilinx FPGA. The key size is designed to be 1024-bit to achieve high security for the passport information. The whole design is achieved through VHDL design entry which makes it a portable design and can be directed to any hardware platform.

Keywords: RSA, VHDL, FPGA, modular multiplication, modular exponential

Procedia PDF Downloads 353

Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Shunsuke Miyoshi, Yasuyuki Nogami, Takuya Kusaka, Nariyoshi Yamai

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Elliptic curve discrete logarithm problem (ECDLP) is one of problems on which the security of pairing-based cryptography is based. This paper considers Pollard's rho method to evaluate the security of ECDLP on Barreto-Naehrig (BN) curve that is an efficient pairing-friendly curve. Some techniques are proposed to make the rho method efficient. Especially, the group structure on BN curve, distinguished point method, and Montgomery trick are well-known techniques. This paper applies these techniques and shows its optimization. According to the experimental results for which a large-scale parallel system with MySQL is applied, 94-bit ECDLP was solved about 28 hours by parallelizing 71 computers.

Keywords: Pollard's rho method, BN curve, Montgomery multiplication

Procedia PDF Downloads 241

Authors: Neil M. Mame

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We consider only finite graphs without loops nor multiple edges. Let G be a graph with E(G) = {e1, e2, …., em}. The edge space of G, denoted by ε(G), is a vector space over the field Z2. The elements of ε(G) are all the subsets of E(G). Vector addition is defined as X+Y = X Δ Y, the symmetric difference of sets X and Y, for X, Y ∈ ε(G). Scalar multiplication is defined as 1.X =X and 0.X = Ø for X ∈ ε(G). The set S ⊆ ε(G) is called a generating set if every element ε(G) is a linear combination of the elements of S. For a non-empty set X ∈ ε(G), the smallest subgraph with edge set X is called edge-induced subgraph of G, denoted by G[X]. The set EH(G) = { A ∈ ε(G) : G[A] ≅ H } denotes the uniform set of H with respect to G and εH(G) denotes the subspace of ε(G) generated by EH(G). If εH(G) is generating set, then we call H a generator subgraph of G. This paper gives the characterization for the generator subgraphs of the wheel that contain cycles and gives the necessary conditions for the acyclic generator subgraphs of the wheel.

Keywords: edge space, edge-induced subgraph, generator subgraph, wheel

Procedia PDF Downloads 435