Cost-Effective and Energy-Efficient Techniques for Underwater Acoustic Communication Modems

Finally, the modem developed has been tested experimentally in laboratory (aquatic environment) showing that can communicates at different data rates (100..1200 bps) compared to state-of-the-art research modems. The software used include LabVIEW, MATLAB, Simulink, and Multisim (to test the electronic circuit built) has been employed.Underwater wireless sensor networks (UWSNs) are widely used in many applications related to ecosystem monitoring, and many more fields. Due to the absorption of electromagnetic waves in water and line-of-sight communication of optical waves, acoustic waves are the most suitable medium of communication in underwater environments. Underwater acoustic modem (UAM) is responsible for the transmission and reception of acoustic signals in an aquatic channel. Commercial modems may communicate at longer distances with reliability, but they are expensive and less power efficient. Research modems are designed by using a digital-signal-processor (DSP is expensive) and field-programmable-gate-array (FPGA is high power consuming device). In addition to, the use of a microcontroller is also a common practice (which is less expensive) but provides limited computational power. Hence, there is a need for a cost-effective and energy-efficient UAM to be used in budget limited applications. In this thesis different objectives are proposed. First, to identify the limitations of state-of-the-art commercial and research UAMs through a comprehensive survey. The second contribution has been the design of a low-cost acoustic modem for short-range underwater communications by using a single board computer (Raspberry-Pi), and a microcontroller (Atmega328P). The modulator, demodulator and amplifiers are designed with discrete components to reduce the overall cost. The third contribution is to design a web based underwater acoustic communication testbed along with a simulation platform (with underwater channel and sound propagation models), for testing modems. The fourth contribution is to integrate in a single module two important modules present in UAMs: the PSK modulator and the power amplifier

A Universal Machine-Learning-Based Automated Testing System for Consumer Electronic Products

Consumer electronic manufacturing (CEM) companies face a constant challenge to maintain quality standards during frequent product launches. A manufacturing test verifies product functionality and identifies manufacturing defects. Failure to complete testing can even result in product recalls. In this research, a universal automated testing system has been proposed for CEM companies to streamline their test process in reduced test cost and time. A universal hardware interface is designed for connecting commercial off-the-shelf (COTS) test equipment and unit under test (UUT). A software application, based on machine learning, is developed in LabVIEW. The test site data for around 100 test sites have been collected. The application automatically selects COTS test equipment drivers and interfaces on UUT and test measurements for test sites through a universal hardware interface. Further, it collects real-time test measurement data, performs analysis, generates reports and key performance indicators (KPIs), and provides recommendations using machine learning. It also maintains a database for historical data to improve manufacturing processes. The proposed system can be deployed standalone as well as a replacement for the test department module of enterprise resource planning (ERP) systems providing direct access to test site hardware. Finally, the system is validated through an experimental setup in a CEM company

A Hybrid Approach for Efficient and Secure Point Multiplication on Binary Edwards Curves

The focus of this article is to present a novel crypto-accelerator architecture for a resource-constrained embedded system that utilizes elliptic curve cryptography (ECC). The architecture is built around Binary Edwards curves (BEC) to provide resistance against simple power analysis (SPA) attacks. Furthermore, the proposed architecture incorporates several optimizations to achieve efficient hardware resource utilization for the point multiplication process over GF(2m). This includes the use of a Montgomery radix-2 multiplier and the projective coordinate hybrid algorithm (combination of Montgomery ladder and double and add algorithm) for scalar multiplication. A two-stage pipelined architecture is employed to enhance throughput. The design is modeled in Verilog HDL and verified using Vivado and ISE design suites from Xilinx. The obtained results demonstrate that the proposed BEC accelerator offers significant performance improvements compared to existing solutions. The obtained throughput over area ratio for GF(2233) on Virtex-4, Virtex-5, Virtex-6, and Virtex-7 Xilinx FPGAs are 9.43, 14.39, 26.14, and 28.79, respectively. The computation time required for a single point multiplication operation on the Virtex-7 device is 19.61 µs. These findings indicate that the proposed architecture has the potential to address the challenges posed by resource-constrained embedded systems that require high throughput and efficient use of available resources

Implementation of Omni-D Tele-Presence Robot Using Kalman Filter and Tricon Ultrasonic Sensors

The tele-presence robot is designed to set forth an economic solution to facilitate day-to-day normal activities in almost every field. There are several solutions to design tele-presence robots, e.g., Skype and team viewer, but it is pretty inappropriate to use Skype and extra hardware. Therefore, in this article, we have presented a robust implementation of the tele-presence robot. Our proposed omnidirectional tele-presence robot consists of (i) Tricon ultrasonic sensors, (ii) Kalman filter implementation and control, and (iii) integration of our developed WebRTC-based application with the omnidirectional tele-presence robot for video transmission. We present a new algorithm to encounter the sensor noise with the least number of sensors for the estimation of Kalman filter. We have simulated the complete model of robot in Simulink and Matlab for the tough paths and critical hurdles. The robot successfully prevents the collision and reaches the destination. The mean errors for the estimation of position and velocity are 5.77% and 2.04%. To achieve efficient and reliable video transmission, the quality factors such as resolution, encoding, average delay and throughput are resolved using the WebRTC along with the integration of the communication protocols. To protect the data transmission, we have implemented the SSL protocol and installed it on the server. We tested three different cases of video resolutions (i.e., 320×280, 820×460 and 900×590) for the performance evaluation of the video transmission. For the highest resolution, our TPR takes 3.5 ms for the encoding, and the average delay is 2.70 ms with 900 × 590 pixels

An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier

Energy consumption is always a key feature in devices powered by electric accumulators. The power amplifier is the most energy-demanding module in mobile devices, portable appliances, static transceivers, and even nodes used in underwater acoustic networks. These devices incorporate a modulator, typically a pulse-width modulation (PWM) and a class-D power amplifier, for higher efficiency. We propose a technique to integrate the modulator of a transmitter and PW-modulator of a class-D amplifier to improve the overall efficiency of the system. This integrated set operates as an up-converter, phase modulator (PM), and binary phase-shift keying (BPSK) modulator under certain conditions. The theoretical concept is verified using Matlab and a model is designed and simulated in Simulink. For validation purposes, an electronic circuit is built and tested using Multisim. The results obtained by simulations and circuit implementation show that the proposed integrated system is an energy-efficient and cost-effective solution compared to conventional techniques

Windows malware detection based on static analysis with multiple features

Malware or malicious software is an intrusive software that infects or performs harmful activities on a computer under attack. Malware has been a threat to individuals and organizations since the dawn of computers and the research community has been struggling to develop efficient methods to detect malware. In this work, we present a static malware detection system to detect Portable Executable (PE) malware in Windows environment and classify them as benign or malware with high accuracy. First, we collect a total of 27,920 Windows PE malware samples divided into six categories and create a new dataset by extracting four types of information including the list of imported DLLs and API functions called by these samples, values of 52 attributes from PE Header and 100 attributes of PE Section. We also amalgamate this information to create two integrated feature sets. Second, we apply seven machine learning models; gradient boosting, decision tree, random forest, support vector machine, K-nearest neighbor, naive Bayes, and nearest centroid, and three ensemble learning techniques including Majority Voting, Stack Generalization, and AdaBoost to classify the malware. Third, to further improve the performance of our malware detection system, we also deploy two dimensionality reduction techniques: Information Gain and Principal Component Analysis. We perform a number of experiments to test the performance and robustness of our system on both raw and selected features and show its supremacy over previous studies. By combining machine learning, ensemble learning and dimensionality reduction techniques, we construct a static malware detection system which achieves a detection rate of 99.5% and error rate of only 0.47%

Large Field-Size Elliptic Curve Processor for Area-Constrained Applications

This article has proposed an efficient area-optimized elliptic curve cryptographic processor architecture over GF(2409) and GF(2571). The proposed architecture employs Lopez-Dahab projective point arithmetic operations. To do this, a hybrid Karatsuba multiplier of 4-split polynomials is proposed. The proposed multiplier uses general Karatsuba and traditional schoolbook multiplication approaches. Moreover, the multiplier resources are reused to implement the modular squares and addition chains of the Itoh-Tsujii algorithm for inverse computations. The reuse of resources reduces the overall area requirements. The implementation is performed in Verilog (HDL). The achieved results are provided on Xilinx Virtex 7 device. In addition, the performance of the proposed design is evaluated on ASIC 65 nm process technology. Consequently, a figure-of-merit is constructed to compare the FPGA and ASIC implementations. An exhaustive comparison to existing designs in the literature shows that the proposed architecture utilizes less area. Therefore, the proposed design is the right choice for area-constrained cryptographic applications

Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is a human eye disease that affects people who are suffering from diabetes. It causes damage to their eyes, including vision loss. It is treatable; however, it takes a long time to diagnose and may require many eye exams. Early detection of DR may prevent or delay the vision loss. Therefore, a robust, automatic and computer-based diagnosis of DR is essential. Currently, deep neural networks are being utilized in numerous medical areas to diagnose various diseases. Consequently, deep transfer learning is utilized in this article. We employ five convolutional-neural-network-based designs (AlexNet, GoogleNet, Inception V4, Inception ResNet V2 and ResNeXt-50). A collection of DR pictures is created. Subsequently, the created collections are labeled with an appropriate treatment approach. This automates the diagnosis and assists patients through subsequent therapies. Furthermore, in order to identify the severity of DR retina pictures, we use our own dataset to train deep convolutional neural networks (CNNs). Experimental results reveal that the pre-trained model Se-ResNeXt-50 obtains the best classification accuracy of 97.53% for our dataset out of all pre-trained models. Moreover, we perform five different experiments on each CNN architecture. As a result, a minimum accuracy of 84.01% is achieved for a five-degree classification

Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield

first_pagesettingsOrder Article Reprints Open AccessArticle Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield by Syeda Iqra Hassan 1,2,3ORCID,Muhammad Mansoor Alam 4,5,6,7ORCID,Muhammad Yousuf Irfan Zia 3ORCID,Muhammad Rashid 8ORCID,Usman Illahi 9ORCID andMazliham Mohd Su’ud 5,10,* 1 Department of Electronics and Electrical Engineering, Universiti Kuala Lumpur British Malaysian Institute (UniKL BMI), Batu 8, Jalan Sungai Pusu, Gombak 53100, Malaysia 2 National Centre for Big Data and Cloud Computing, Ziauddin University, Karachi 74600, Pakistan 3 Department of Electrical Engineering, Ziauddin University, Karachi 74600, Pakistan 4 Faculty of Computing, Riphah International University, Islamabad 46000, Pakistan 5 Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia 6 Malaysian Institute of Information Technology, University of Kuala Lumpur, Kuala Lumpur 50250, Malaysia 7 Faculty of Engineering and Information Technology, School of Computer Science, University of Technology, Sydney 2006, Australia 8 Department of Computer Engineering, Umm Al Qura University, Makkah 21955, Saudi Arabia 9 Department of Electrical Engineering, FET, Gomal University, Dera Ismail Khan 29050, Pakistan 10 Water and Engineering Section, MFI, Universiti Kuala Lumpur Malaysian France Institute (UniKL MFI), Section 14, Jalan Damai, Seksyen 14, Bandar Baru Bangi 43650, Malaysia * Author to whom correspondence should be addressed. Sensors 2022, 22(21), 8567; https://doi.org/10.3390/s22218567 Received: 16 September 2022 / Revised: 23 October 2022 / Accepted: 1 November 2022 / Published: 7 November 2022 (This article belongs to the Section Smart Agriculture) Download Browse Figures Versions Notes Abstract Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode