Diffusion-Steered Super-Resolution Image Reconstruction

For decades, super-resolution has been a widely applied technique to improve the spatial resolution of an image without hardware modification. Despite the advantages, super-resolution suffers from ill-posedness, a problem that makes the technique susceptible to multiple solutions. Therefore, scholars have proposed regularization approaches as attempts to address the challenge. The present work introduces a parameterized diffusion-steered regularization framework that integrates total variation (TV) and Perona-Malik (PM) smoothing functionals into the classical super-resolution model. The goal is to establish an automatic interplay between TV and PM regularizers such that only their critical useful properties are extracted to well pose the super-resolution problem, and hence, to generate reliable and appreciable results. Extensive analysis of the proposed resolution-enhancement model shows that it can respond well on different image regions. Experimental results provide further evidence that the proposed model outperforms

Target-to-Target Interaction in Through-the-Wall Radars under Path Loss Compensated Multipath Exploitation-Based Signal Model for Sparse Image Reconstruction

Multipath caused by reflections from interior walls of buildings has been a long-standing challenge that affects Through-the-Wall Radar Imaging. Multipath creates ghost images that introduce confusion when detecting desired targets. Traditionally, multipath exploitation techniques under the compressive sensing framework have widely been applied to address the challenge. However, the multipath component emanating from target-to-target interactions has not been considered–a consequence that may, under multiple target scenarios, lead to incorrect image interpretation. Besides, far targets experience more attenuation due to free space path-loss, hence resulting into target undetectability. This study proposes a signal model, based on multipath exploitation techniques, by designing a sensing matrix that incorporates multipath returns due to target-to-target interaction and path loss compensation. The study, in addition, proposes the path-loss compensator that, if integrated into the proposed signal model, reduces path loss effects. Simulation results show that the Signal to Clutter Ratio and Relative Clutter Peak improved by 4.9 dB and 1.9 dB, respectively compared to the existing model.Keywords: Compressive sensing, multipath ghost, multipath exploitation, pathloss, path-loss compensator, through-the-wall-radar imaging

IoT-Based Smart Fishing Gear for Sustainability of the Tanzania Blue Economy

The fishing industry engages many Tanzanians and is among the leading sectors of Blue Economy in the country. However, fishing practices are small-scale with poor and insufficient number of fishing facilities, hence limiting productivity and efficacy. Studies argue that the low level of technology currently used in the country could possibly be an impeding factor. Specifically, fishers use non-interactive gears that cannot instantaneously update status and alert them whenever the gears are ready for collection. In such scenarios, fishers not only waste their time but also scarce resources such as fuel to facilitate trips to and from the fishing sites to check status of the gears. Thus, the application of Information and Communication Technology (ICT) could improve the production and lower operational costs is hypothesized. ICT solutions can help realize the processes with minimum human interventions while adding intelligence to the systems to make informed decisions and hence improve systems’ efficiency. In this work, an IoT based smart fishing gear that counts and records the number of fishes in the gear and then displays them on a mobile application is proposed. The system can send alerts to the user when the required number of fish is attained, and provides navigation support to localize the distant filled gears. Results show that the system can send the location of a given gear and timely update the number of catches via the mobile application

Performance Evaluation of Aspect Dependent-Based Ghost Suppression Methods for Through-the-Wall Radar Imaging

There are many approaches which address multipath ghost challenges in Through-the-Wall Radar Imaging (TWRI) under Compressive Sensing (CS) framework. One of the approaches, which exploits ghosts’ locations in the images, termed as Aspect Dependent (AD), does not require prior knowledge of the reflecting geometry making it superior over multipath exploitation based approaches. However, which method is superior within the AD based category is still unknown. Therefore, their performance comparison becomes inevitable, and hence this paper presents their performance evaluation in view of target reconstruction. At first, the methods were grouped based on how the subarrays were applied: multiple subarray, hybrid subarray and sparse array. The methods were fairly evaluated on varying noise level, data volume and the number of targets in the scene. Simulation results show that, when applied in a noisy environment, the hybrid subarray-based approaches were robust than the multiple subarray and sparse array. At 15 dB signal-to-noise ratio, the hybrid subarray exhibited signal to clutter ratio of 3.9 dB and 4.5 dB above the multiple subarray and sparse array, respectively. When high data volumes or in the case of multiple targets, multiple subarrays with duo subarrays became the best candidates. Keywords: Aspect dependent; compressive sensing; point target; through-wall-radar imaging

Multi-Vehicle Speed Estimation Algorithm Based on Real-Time Inter-Frame Tracking Technique

Inappropriate vehicle speeding remains a central factor that causes road accidents claiming millions of lives every year. This challenge has raised concerns for vehicle speed estimation as an attempt to promote speed enforcement methods. Traditionally, radar and lidar systems have widely been used for this purpose, despite their several shortfalls: cosine error effects, need for direct line-of-sight, and inability to simultaneously and accurately measure speed from multiple vehicles. The current work proposes an algorithm and a multi-vehicle speed estimation system in a multi-lane road environment to address multi-vehicle speed estimation shortfalls. The proposed solution exploits image processing and computer vision techniques to flag vehicles with inappropriate speeding patterns. A series of experiments showed that the developed system generates more accurate results than those given by the lidar system. In essence, the proposed system can estimate the speed of up to six vehicles concurrently. It can produce an average percentage error of 2.7% relative to the actual speed measured by a speedometer. This error is 5.4% lower than that demonstrated by the lidar system, emphasizing that the proposed system may be a more suitable approach to traffic laws enforcement. Keywords: Computer vision; image processing; inter-frame differencing; vehicle tracking; road acciden

Robust Edge Detection Method for the Segmentation of Diabetic Foot Ulcer Images

This research article published by Engineering, Technology & Applied Science Research, Vol. 10, No. 4, 2020Segmentation is an open-ended research problem in various computer vision and image processing tasks. This preprocessing operation requires a robust edge detector to generate appealing results. However, the available approaches for edge detection underperform when applied to images corrupted by noise or impacted by poor imaging conditions. The problem becomes significant for images containing diabetic foot ulcers, which originate from people with varied skin color. Comparative performance evaluation of the edge detectors facilitates the process of deciding an appropriate method for image segmentation of diabetic foot ulcers. Our research discovered that the classical edge detectors cannot clearly locate ulcers in images with black-skin feet. In addition, these methods collapse for degraded input images. Therefore, the current research proposes a robust edge detector that can address some limitations of the previous attempts. The proposed method incorporates a hybrid diffusion-steered functional derived from the total variation and the Perona-Malik diffusivities, which have been reported to can effectively capture semantic features in images. The empirical results show that our method generates clearer and stronger edge maps with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks

Early identification of Tuta absoluta in tomato plants using deep learning

This research article published by Elsevier B.V., 2020The agricultural sector is highly challenged by plant pests and diseases. A high–yielding crop, such as tomato with high economic returns, can greatly increase the income of small- holder farmers income when its health is maintained. This work introduces an approach to strengthen phytosanitary capacity and systems to help solve tomato plant pest Tuta ab- soluta devastation at early tomato growth stages. We present a deep learning approach to identify tomato leaf miner pest ( Tuta absoluta ) invasion. The Convolutional Neural Network architectures (VGG16, VGG19, and ResNet50) were used in training classifiers on tomato image dataset captured from the field containing healthy and infested tomato leaves. We evaluated performance of each classifier by considering accuracy of classifying the tomato canopy into correct category. Experimental results show that VGG16 attained the high- est accuracy of 91.9% in classifying tomato plant leaves into correct categories. Our model may be used to establish methods for early detection of Tuta absoluta pest invasion at early tomato growth stages, hence assisting farmers overcome yield losses

Exploring pilot assignment methods for pilot contamination mitigation in massive MIMO systems

In massive multi-input multi-output (MIMO) antenna system, the performance is restricted by inter-cell interference called pilot contamination. However, acquisition of channel state information for channel estimation introduces overheads of pilots required to estimate the channel frequently, a disadvantage that causes inefficient use of bandwidth. A tradeoff thus exists between the number of pilots required to estimate the channel versus the spectral efficiency. Studies on pilot decontamination using pilot assignment lack consideration of the comparative analysis to determine superior approaches. Limited literature exists on the analysis of the existing pilot assignment techniques. Different techniques exhibit varied performances and influence on resource utilization. The purpose of this study is to analyze various methods of pilot assignment schemes for mitigation of pilot contamination in massive MIMO systems. A systematic review approach is adopted to examine different related works and the adopted methodologies, outcomes, and restrictions to determine the most suitable approaches. This study further demonstrates the impact of coherence interval distribution between the pilots for channel estimation and data transmission. An analysis of a tradeoff between FDD and TDD modes is presented to determine the most suitable for massive MIMO antennas. In addition, the study provides recommendations for future research

Maintenance Automation Architecture and Electrical Equipment Fault Prediction Method in Tanzania Secondary Distribution Networks

Distribution networks remain the most maintenance-intensive parts of power systems. The implementation of maintenance automation and prediction of equipment fault can enhance system reliability while reducing the overall costs. In Tanzania, however, maintenance automation has not been deployed in secondary distribution networks (SDNs). Instead, traditional methods are used for condition prediction and fault identification of power assets (transformers and power lines). These (manual) methods are costly and time-consuming, and may introduce human-related errors. Motivated by these challenges, this work introduces maintenance automation into the network architecture by implementing effective maintenance and fault identification methods. The proposed method adopts machine learning techniques to develop a novel system architecture for maintenance automation in the SDN. Experimental results showed that different transformer prediction methods, namely support vector machine, kernel support vector machine, and multi-layer artificial neural network, give performance values of  96.72%, 97.50%, and 97.53%, respectively. Furthermore, oil based performance analysis was done to compare the existing methods with the proposed method. Simulation results showed that the proposed method can accurately identify up to ten transformer abnormalities. These results suggest that the proposed system may be integrated into a maintenance scheduling platform to reduce unplanned maintenance outages and human maintenance-related errors. Keywords: Predictive maintenance; fault identification; fault prediction; maintenance automation; secondary electrical distribution networ