Orthogonal frequency division multiplexing (OFDM) was endorsed in recent digital communication technologies such as 4G-LTE to cope with multipath fading channel, and respond to increasingly high data ...rate demand. Despite its attractive features, OFDM based systems suffers from High Peak to Average Power Ratio (PAPR) which limits its application to a certain level. In this paper, the peak windowing technique is investigated in details and the effect of window size on PAPR reduction and BER improvement performance is the main analysis concern by taking into consideration system circuitry non-linearity characteristics. The performance of five different peak windowing functions on PAPR reduction performance was analyzed, Individual window function performance for PAPR reduction were evaluated and the window function with the optimum performance over others was selected and used to assess the effect of window size of peak windowing for PAPR reduction in LTE system with non-linear High power Amplifier (HPA). The simulation results and analysis of proposed approach shows that Hann window function of window size ws=8 provide a 2.094dB PAPR reduction from 10.207dB to 8.113dB at 10-2 probability, with BER degradation of 0.0065dB and 0.214dB at 10-1 and 10-2probabilty respectively. A Comparative performance analysis of the proposed algorithm with other allied recent proposed approaches on PAPR reduction such as Gaussian windowing was carried out; a good performance of the proposed method is observed.
Bird sound classification is an important task in the implementation of automatic farm monitoring systems, as many birds are harmful threats to crop farms, especially maize, sorghum, and rice farms. ...The ability to automatically classify and detect bird pests can improve the monitoring system for farmers, by detecting and notifying them in case of the presence of bird pests on the farm, in near real-time for their quick decision-making and damage minimization by taking action to scare birds away. There are many studies on the classification of bird sounds. However, there are few studies on bird sound classification in the context of farm monitoring. In this paper, we present a method for bird sound classification based on grey-level co-occurrence matrix (GLCM) features. GLCM is a statistical approach that measures the texture properties of the image. In the context of sound classification, sound waves can be represented as spectrogram images, which can then be processed using GLCM features. To measure the texture of spectrograms, four GLCM features namely contrast, correlation, energy, and dissimilarity were calculated and used. The feature vectors obtained from the sound signals of two bird species, namely nightingale (Luscinia megarhynchos) and mousebird (Colius striatus), were utilized as inputs for classification using two different algorithms: Light Gradient Boosting Machine (LightGBM) and support vector machines (SVM). These two bird species are known to cause damage to crops in Bugesera, the eastern province of Rwanda. The LightGBM classifier exhibited promising results, achieving an accuracy of 90.6%, surpassing the 85% accuracy obtained with SVM in classifying nightingale and mousebird sounds. The superior performance of the LightGBM model suggests its potential usefulness in farm monitoring tasks.
Exposure to air pollution spikes cause health problems to regularly exposed organisms, raising the need to monitor them in real-time. Existing air pollution monitors mainly use a cloud-centric design ...considering relatively constant pollution, therefore duty-cycling sensors with long sleep periods to save their batteries. Such design is however inefficient for monitoring pollution spikes. Furthermore, since spikes vanish rapidly, integrity of monitoring data is very important. This paper presents a framework integrating edge-centric design and blockchain in monitoring air pollution spikes, while using short-term prediction artificial intelligence to timely alert pollution emitters about exceeding long-term average pollution limits defined by standards.
With the advent of artificial intelligence (AI) and Internet of Things (IoT), there has been a rapid increase in the use of sensors to intelligently monitor the environment and movement of objects. ...Smart solutions have been widely used for monitoring infectious diseases by limiting the transmission of contagious diseases using proximity sensing systems. This is an alternative to conventional social distancing technologies like Bluetooth and cameras which uses machine learning (ML), image processing to identify trespassers, and multiple object detection in real-time. This paper leverages the emerging Tiny ML technology to design and develop a wearable device that can prevent infectious diseases from spreading. The device senses the cough sound of the nearest person within a limited distance and then identify the nearest objects such as humans, animals (dog, goats), and wind-blown vegetation, based on patterns of PIR signals bounced back from different objects. By using machine learning algorithms, the device can be able to notify the user when they are in a safe environment or not. This solution is a wearable device that has the potential to be used in monitoring the transmission of contagious diseases by detecting and identifying moving objects and alerting people to keep their distance when they are in an unsafe environment with a high risk of being exposed to the disease. This work-focused research project will particularly focus on monitoring the risk environment to prevent infectious diseases between humans and between humans and animals, reminding users to keep their distance for their safety and the use of the Convolutional Neural Network (CNN) algorithm on the device for identifying moving objects and for detecting cough. The system has been evaluated, and the experiments have shown a performance accuracy of 92.1% for object detection and 68% for cough detection, promising for detecting a safe environment. This accuracy could be increased over time via reinforcement learning.
Structural Feature Engineering Approach for Detecting Polymorphic Malware Masabo, Emmanuel; Kaawaase, Kyanda Swaib; Sansa-Otim, Julianne ...
2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech),
2017-Nov.
Conference Proceeding
Currently, malware are distributed or transmitted in a polymorphic form, smartly obfuscated with packing and encryption routines. This serves the purpose of hardening analysis or simply making it ...impossible. Researchers have mainly resorted to static analysis, dynamic analysis or a combination of both in attempting to find more adequate solutions to polymorphic malware problems. This paper presents a novel simple feature engineering approach in terms of extracting, analyzing and processing structural based features for efficient detection of polymorphic malware. Our experiments achieve a detection accuracy of 98.7% on a small dataset.
The digital information revolution and increasing of outsourced data in many organizations caused significant changes in the global society where digital data are available everywhere with free of ...cost. Proving ownership rights of outsourced relational databases is a crucial issue in today internet-based application environments and in many content distribution applications. In the past few years, a large number of techniques have been proposed for right protection of numeric data. In this paper, a new relational database watermarking method for non-numeric multi words data is proposed. A mark is embedded by horizontally shifting the location of a word within selected attribute of selected tuples; a word is displaced right or left unmoved depending on watermark bit. The location where the mark to be inserted is determined by the Levenshtein Distance between two successive words within an attribute. Our method is effective as it is robust against different forms of malicious attacks and it is blind as it does not require the original database in order to extract the embedded watermark.
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