Using radio frequency spectral measurements for determination of corn mechanical damage

Dielectric measurements between 10 Hz to 13 MHz were obtained using an HP 4192A Impedance Analyzer for artificially damaged and combine damaged corn samples. For medium and severe artificial damage, the measurements were obtained at two moisture contents (11 and 19 percent), five damage levels (0, 10, 25, 50, and 100 percent), and two bulk densities. The results showed that dielectric properties were able to measure moisture content, bulk density, and mechanical damage level. Using a four-variable model, medium damage level was predicted with R2 = 0.95 and RMSE = 8.8 percent, while severe damage level was predicted with R2 = 0.98 and RMSE = 5.97 percent using a four-variable model. However, when the actual moisture content and bulk density were used along with dielectric variables, the severe damage level prediction was slightly reduced with R2 = 0.97 and RMSE = 6.96 percent although the model used only two dielectric variables in addition to actual moisture content and bulk density of the corn samples. Medium damage level prediction was improved with R2 = 0.98 and RMSE = 5.77 percent, using four-dielectric variable and the actual corn moisture and bulk density.;Similar analysis was performed on combine damaged corn samples. Four different damage levels were produced. Damage levels were evaluated using Chowdhury\u27s method and visual inspection method. The results from visual inspection were: 0, 10.6, 17.4, and 31.2 percent mechanical damage. Three moisture content levels were prepared (12.5, 18, and 23 percent), and two bulk densities were produced. The damage calibration model was developed for each moisture level separately. For low moisture samples, damage level was predicted with an R 2 = 0.95 and RMSE = 3.36 percent using a six-variable model. For medium moisture samples, damage level was predicted with R2 = 0.95 and RMSE = 3.27 percent, using a five-variable model. Finally, for high moisture samples, damage level was predicted with R2 = 0.97 and RMSE = 2.29 percent using a three-variable model.;The technology shows a good potential for developing a mechanical damage sensor. Further testing on additional varieties, types of grains, and more moisture contents is needed

Dynamic bandwidth allocation in ATM networks

Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes

Covert channel: network steganography deployment

Steganography is one of the major techniques used to hide the existence of communication that lies between two overt parties. Steganography also denoted by art of hiding communication. This work relates the areas of steganography, network protocols and security for practical data hiding in communication networks employing TCP/IP. This project major focus is to deploy steganography on network protocol using covert_TCP tool. This tool uses 3 methods in order to employ steganography at TCP/IP header; IP identification encoding, TCP sequence number encoding and ACK “bouncing” server method. We also employ network steganography in two different network environment which is on transmission on single host (loopback interface) and inter local area network. After that phase, we analyze packet income and out the network. We used network sniffer tool, tcpdump to dump all the packet and analyze the accuracy of the data transmit using covert channel as employ by covert_TCP

A literature review on collaborative caching techniques in MANETs: issues and methods used in serving queries

Collaborative cache management in Mobile Ad Hoc Networks (MANETs) environment is considered as an efficient technique to increase data accessibility and availability, by sharing and coordination among mobile nodes. Due to nodes’ mobility, limited battery power and insufficient bandwidth, researchers addressed these challenges by developing many different collaborative caching schemes. The objective of this paper is to review various collaborative caching techniques in MANETs. Collaborative caching techniques are classified by methods used in serving queries, such as: hop-by-hop discovering, broadcasting messages, flooding, and query service differentiation. This review reveals that techniques utilizing hop-by-hop methods have better performance compared to others, especially techniques using additional strategies

Real-Time classification of various types of falls and activities of daily livings based on CNN LSTM network

In this research, two multiclass models have been developed and implemented, namely, a standard long-short-term memory (LSTM) model and a Convolutional neural network (CNN) combined with LSTM (CNN-LSTM) model. Both models operate on raw acceleration data stored in the Sisfall public dataset. These models have been trained using the TensorFlow framework to classify and recognize among ten different events: five separate falls and five activities of daily livings (ADLs). An accuracy of more than 96% has been reached in the first 200 epochs of the training process. Furthermore, a real-time prototype for recognizing falls and ADLs has been implemented and developed using the TensorFlow lite framework and Raspberry PI, which resulted in an acceptable performance

Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers

We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let’s performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environment

Document analysis at DFKI. - Part 1: Image analysis and text recognition

Document analysis is responsible for an essential progress in office automation. This paper is part of an overview about the combined research efforts in document analysis at the DFKI. Common to all document analysis projects is the global goal of providing a high level electronic representation of documents in terms of iconic, structural, textual, and semantic information. These symbolic document descriptions enable an "intelligent\u27; access to a document database. Currently there are three ongoing document analysis projects at DFKI: INCA, OMEGA, and PASCAL2000/PASCAL+. Though the projects pursue different goals in different application domains, they all share the same problems which have to be resolved with similar techniques. For that reason the activities in these projects are bundled to avoid redundant work. At DFKI we have divided the problem of document analysis into two main tasks, text recognition and text analysis, which themselves are divided into a set of subtasks. In a series of three research reports the work of the document analysis and office automation department at DFKI is presented. The first report discusses the problem of text recognition, the second that of text analysis. In a third report we describe our concept for a specialized document analysis knowledge representation language. The report in hand describes the activities dealing with the text recognition task. Text recognition covers the phase starting with capturing a document image up to identifying the written words. This comprises the following subtasks: preprocessing the pictorial information, segmenting into blocks, lines, words, and characters, classifying characters, and identifying the input words. For each subtask several competing solution algorithms, called specialists or knowledge sources, may exist. To efficiently control and organize these specialists an intelligent situation-based planning component is necessary, which is also described in this report. It should be mentioned that the planning component is also responsible to control the overall document analysis system instead of the text recognition phase onl

Corporate governance mechanisms and earnings management in Malaysian government linked companies. The impact of GLCs transformation policy

As the major shareholder, Malaysian Government in 2004 has embarked on the Government linked Companies (GLCs) transformation policy program that mainly emphasizes on enhancing the corporate governance mechanisms of the State owned Enterprises (SOEs) in order to enhance effectiveness of the board. The paper aims to examine the impact of corporate governance mechanisms as embedded in the transformation program on the practice of earnings management. In particular, the study uses data for two periods of time (pre and post transformation), and examine whether the period of post transformation policy has experienced any improvement of board monitoring role in curbing earnings management activities. The main findings show that there is an increase of earnings management activities in post transformation period. Further, the findings revealed that all corporate governance mechanisms have little impact to curb earnings management activities except for board meetings and leadership structure in the post transformation period.The board meetings and separate role of two top positions in the companies were shown to have negative impact on earnings management post transformation policy and that relationship do not hold for the period pre transformation policy. Although the study has shown positive preliminary impact of tightening corporate governance in GLCs, scope to expand the research was also discussed

Biocompatibility Evaluation of a New Hydrogel Dressing Based on Polyvinylpyrrolidone/Polyethylene Glycol

The composition of the dressings is based on polyvinylpyrrolidone (PVP), polyethylene glycol (PEG), and agar. The electron beam irradiation technique has been used to prepare hydrogel wound dressings. The in vitro biocompatibility of the hydrogel was investigated by check samples (hydrocolloid Comfeel), antibacterial test (Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, Escherichia Coli k12), anti fungal test (Candida Albicans) and cytotoxicity test (Fibroblast L929). Results have shown cell attachment characteristics and nontoxicity of all samples. Antibacterial testing also showed that the antibacterial effect of the hydrogel sample to the check sample increased to 30%. Also, investigation of antifungal analysis did not show any trace of fungi growth on the surface of the hydrogel, whereas antifungal effect did not observe on the surface of the check sample. Finally, this hydrogel sample showed a good in vitro biocompatibility