MB Of Space Research Articles

VGNet: A Lightweight Intelligent Learning Method for Corn Diseases Recognition

The automatic recognition of crop diseases based on visual perception algorithms is one of the important research directions in the current prevention and control of crop diseases. However, there are two issues to be addressed in corn disease identification: (1) A lack of multicategory corn disease image datasets that can be used for disease recognition model training. (2) The existing methods for identifying corn diseases have difficulty satisfying the dual requirements of disease recognition speed and accuracy in actual corn planting scenarios. Therefore, a corn diseases recognition system based on pretrained VGG16 is investigated and devised, termed as VGNet, which consists of batch normalization (BN), global average pooling (GAP) and L2 normalization. The performance of the proposed method is improved by using transfer learning for the task of corn disease classification. Experiment results show that the Adam optimizer is more suitable for crop disease recognition than the stochastic gradient descent (SGD) algorithm. When the learning rate is 0.001, the model performance reaches a highest accuracy of 98.3% and a lowest loss of 0.035. After data augmentation, the precision of nine corn diseases is between 98.1% and 100%, and the recall value ranges from 98.6% to 100%. What is more, the designed lightweight VGNet only occupies 79.5 MB of space, and the testing time for 230 images is 75.21 s, which demonstrates better transferability and accuracy in crop disease image recognition.

Wave File Compression Analysis and Implementation using The Huffman Method

One of the sound file formats that are widely used in the Windows operating system is the Wave format (*. WAV). This format is widely used for gaming and multimedia purposes. Wave is actually a raw format where the voice signal is directly recorded and quantized into digital data. The basic format of this file by default does not support compression and is known as PCM (Pulse Code Modulation). If you record a CD Audio quality song using a sampling rate of 44.1 kHz, 16 bits per sample, 2 channels (stereo), then the total media required to store this audio data per second is 176,400 bytes, so for a duration of 1 minute it takes 10,584 MB. If the average duration of one song is 5 minutes, it takes more than 50 MB of space to store the audio data of the song. This is of course very wasteful of storage media such as hard disks even though there are currently large hard disk capacities available.

Technical Note: Standalone application to generate custom reflectance Look-Up Table for advanced optical Monte Carlo simulation in GATE/Geant4.

The need for high-fidelity modeling of radiation detectors to perform reliable detector performance optimization using Monte Carlo simulations requires to accurately simulate the light transport in the scintillator and the light collection by the photodetector. In this work, we implement our well-validated crystal reflectance model computed from three-dimensional (3D) crystal surface measurement in a standalone open-source application to allow researchers to generate fully customized crystal reflectance look-up-tables (LUTs) to be used in optical Monte Carlo simulation. The LUTDavisModel application can be installed in a few minutes on Windows, macOS, and Linux, using 26MB of space. MATLAB Runtime is required and is automatically installed with the application. The core algorithm has been previously validated experimentally and implemented in GATE v8.0. The standalone is divided into five panels, each of which performing a specific task: generate LUTs from a combination of surface type, scintillator, and coupling medium available in the database (such as LSO or BGO) or custom; compute LUTs with the reflectors available and custom coupling thickness; create a mixture of coupling media to account for possible defects in the optical coupling; plot precomputed LUTs for visual comparison. Tooltips and errors/warnings facilitate the navigation. The reported computational times were obtained with an Intel Core i7 MacBook Pro. LUTs can be generated with computational time ranging from a few minutes to several hours depending on the selected surface, sampling, and computational power. A longer time is needed when using rough surfaces and thick coupling media (hundreds of ) due to increased photon tracking. We developed a user-friendly standalone application to generate LUTs that can be used inside GATE Monte Carlo simulations. It can be easily downloaded, installed, and used. Future optimizations will expand the database, decrease the computational time through greater parallelization, and include the generation of LUTs to study Cerenkov photons transport.

Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture.

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

The Geometric Efficient Matching Algorithm for Firewalls

Since firewalls need to filter all the traffic crossing the network perimeter, they should be able to sustain a very high throughput, or risk becoming a bottleneck. Firewall packet matching can be viewed as a point location problem: Each packet (point) has five fields (dimensions), which need to be checked against every firewall rule in order to find the first matching rule. Thus, algorithms from computational geometry can be applied. In this paper, we consider a classical algorithm that we adapted to the firewall domain. We call the resulting algorithm “Geometric Efficient Matching” (GEM). The GEM algorithm enjoys a logarithmic matching time performance. However, the algorithm's theoretical worst-case space complexity is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) for a rule-base with n rules. Because of this perceived high space complexity, GEM-like algorithms were rejected as impractical by earlier works. Contrary to this conclusion, this paper shows that GEM is actually an excellent choice. Based on statistics from real firewall rule-bases, we created a Perimeter rules model that generates random, but nonuniform, rule-bases. We evaluated GEM via extensive simulation using the Perimeter rules model. Our simulations show that on such rule-bases, GEM uses near-linear space, and only needs approximately 13 MB of space for rule-bases of 5,000 rules. Moreover, with use of additional space improving heuristics, we have been able to reduce the space requirement to 2-3 MB for 5,000 rules. But most importantly, we integrated GEM into the code of the Linux iptables open-source firewall, and tested it on real traffic loads. Our GEM-iptables implementation managed to filter over 30,000 packets-per-second on a standard PC, even with 10,000 rules. Therefore, we believe that GEM is an efficient and practical algorithm for firewall packet matching.

Efficient computation of absent words in genomic sequences.

BackgroundAnalysis of sequence composition is a routine task in genome research. Organisms are characterized by their base composition, dinucleotide relative abundance, codon usage, and so on. Unique subsequences are markers of special interest in genome comparison, expression profiling, and genetic engineering. Relative to a random sequence of the same length, unique subsequences are overrepresented in real genomes. Shortest words absent from a genome have been addressed in two recent studies.ResultsWe describe a new algorithm and software for the computation of absent words. It is more efficient than previous algorithms and easier to use. It directly computes unwords without the need to specify a length estimate. Moreover, it avoids the space requirements of index structures such as suffix trees and suffix arrays. Our implementation is available as an open source package. We compute unwords of human and mouse as well as some other organisms, covering a genome size range from 109 down to 105 bp.ConclusionThe new algorithm computes absent words for the human genome in 10 minutes on standard hardware, using only 2.5 Mb of space. This enables us to perform this type of analysis not only for the largest genomes available so far, but also for the emerging pan- and meta-genome data.

Software Reviews : MAPINFO Publisher: Mapping Information Systems Corporation, 200 Broadway, Troy, NY 12180 (telephone: 800-327-8627) Year of Publication: 1989 Materials: 115.25-inch disks, manual Price: $750 Machine Specificity: IBM and compatibles System Requirements: Hard disk with at least 3 MB of space, 512K RAM (640K strongly recommended), graphics card, DOS 2.0 or higher; a mouse is supported but optional; also

MAPINFO is a map database, display, and plotting program. Its dBASE III style relational database component is menu-driven. The database management module contains features tailored for map functions, but it lacks loop and subroutine programming capabilities. Its mapping module can display boundaries, lines, points, and user-defined symbols such as circles and text. MAPINFO contains many features that go beyond traditional boundary shading. For example, addresses or other locations can be highlighted or magnified interactively, and distances between points may be determined. Presentation quality maps can also be printed.