HDTV Research Articles

Implementation of real-time high-speed ultrasound communications through tissue

In this work, we propose a novel implementation of both a transmitter and receiver with field programmable gate arrays (FPGAs) to achieve real-time continuous high-definition (HD) video transmission through tissue, which can enable HD and higher frame rate wireless capsule endoscopy. We used a Texas Instruments AFE58JD48EVM 16 channel analog front end (AFE) evaluation board as the receiver connected to a Xilinx ZCU106 Zynq Ultrascale MPSoC development board in which we implemented a digital down converter (DDC), OFDM demodulator, maximum ratio combiner and low-density parity-check (LDPC) decoder. For the transmitter, we used an Analog Devices EVAL-AD9166 vector signal generator evaluation board, which has a built-in 4.3 dBm output power amplifier as a transmitter connected to another ZCU106 development board in which we implemented a LDPC encoder, OFDM modulator and digital up converter (DUC). The modulated signal was transmitted through a tissue-mimicking abdominal phantom using a 2-mm microcrystal transducer and received with a Sonic Concepts IP103 64 channel phased array at a center frequency of 3.2 MHz. We achieved the continuous transmission of up to over[OML1] 6 Mbps error free payload data rate after LDPC decoder which is used to carry HD video streams through ultrasound.

Energy and Delay Guaranteed Joint Beam and User Scheduling Policy in 5G CoMP Networks

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

The smartphone biosensors for point-of-care detection of human infectious diseases: Overview and perspectives—A systematic review

The accurate and fast diagnosis of infectious diseases plays a critical role in the timely and tailored treatment of the patients and preventing the infections from the further spread. This is especially necessary in regions with low- and middle-income of the world. According to the smartphone-based sensory applications such as the inimitable ability to obtain portable, user-friendly, accurate, and effective functions, that in turn, converts lab-on-hardware an interesting area of new investigations. In the present review, original research articles published in English were collected from various databases during January 2011–July 2021. Also, the reference lists of the articles have been searched. In between 121 electronically searched citations, 35 articles had the inclusion criteria. The maximum potential and extended utilization of smartphone-based biosensors in the diagnosis of human infectious agents belonged to colorimetric smartphone biosensors. Also, because of their unique futures in easy visualization, comfortable analyzing and processing by smartphone, they are easier to use for point-of-care products. Based on the results, smartphone-based biosensors are rapid, precise, and low-cost diagnostic methods and increasing their quality in limited-resource settings is of major importance. In addition, their abilities to connect to the “cloud” data storage systems and the Internet of Things (IoT) networks for collecting and sharing medical analytical data provide a trending novel research field in medical information technology. • Smartphone biosensory applications for medical monitoring, data storage and transferring them. • Medical data sharing through internet-of-medical things (IoMT) in Smartphone biosensors. • Integration of optomechanical units with smartphone biosensors converts them into field-portable and cost-effective colorimetric or fluorometric readers. • Recording high-definition videos for human infectious disease diagnostics using the modern “Smartphone videoscopy”. Schematic representation of smartphone biosensors for infectious diseases.

Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services

In mobile networks, crowdsourcing in Quality of Experience (QoE) assessment phase involves collecting data from the user terminals or dedicated collection devices. A mobile operator or a research group may provide applications that can be run in different mobility test modes such as walk or drive tests. Crowdsourcing using users’ terminals (e.g., a smartphone) is a cheap approach for operators or researchers for addressing large scale area and may help to improve the allocated resources of a given service and/or the network provisioning in some segments. In this work, we first collect a dataset for three popular Internet services: on-demand video streaming, web browsing and file downloading at the user terminal level. We consider two user terminals from two different vendors and many mobility test modes. The dataset contains more than 220,000 measures from one of the major French mobile operators in the Île-de-France region. The measurements are effectuated for six months in 2021. Then, we implement different models from the literature for estimating the QoE in terms of user’s Mean Opinion Score (MOS) for every service using features at radio or application levels. After that, we provide an in-depth analysis of the collected dataset for detecting the root cause of poor performance. We show that the radio provisioning issues is not the only cause of detected anomalies. Finally, we discuss the prediction quality of HD video streaming service (i.e., launch time, the bitrate and the MOS) based only on the physical indicators. Our analysis is applied on both plain-text and encrypted traffic within different mobility modes.

Reliability of 2-Dimensional Video Analysis in Adolescent Runners.

The literature on the reliability of qualitative and quantitative measures for running video analysis in the adolescent population is limited. Reliability of 2-dimensional (2D) video analysis has been reported in adult runners, but these findings may not apply to youth runners. We sought to determine the intra-rater and inter-rater reliability of sagittal and frontal plane kinematics using 2D video analysis in healthy adolescent runners. High-definition (1080p) videos were recorded of 10 healthy runners between 14 and 18 years old running on a treadmill at self-selected speed with markers attached to the cervical spine, pelvis, and lower extremities. Kinematic variables in the sagittal and frontal planes were measured using Dartfish Motion Analysis Software by 3 raters (2 sports medicine physical therapists and a research assistant). Intra- and inter-rater reliability were calculated using intraclass correlation coefficients (ICCs). Of the 10 runners, 4 (40%) were male and the mean age was 16 ± 1.5 years. The intra-rater ICC for all kinematic variables ranged from 0.574 to 0.999 for the experienced physical therapist, and 0.367 to 0.973 for the inexperienced research assistant. The inter-rater ICC for all raters ranged from -0.01 to 0.941. Eleven kinematic variables showed substantial agreement and 4 showed almost perfect agreement. Step width and foot progression showed fair and poor agreement, respectively. Running analysis using 2D video can be performed reliably in adolescents on all kinematic variables except for step width and foot progression. Inexperienced raters can be properly trained in the video analysis of running kinematics to consistently assess the same runner.

Error Performance Analysis of Wireless Video Communication Systems Employing Multi-level MPSK Modulation and MIMO Technologies

Real-time communications of high definition video with the available limited channel bandwidth is a fundamental requirement for modern mobile and wireless networks such as Wi-Fi, WiMax, 3G, LTE and the emerging 5G. To fulfill this requirement, H.264/AVC and High Efficiency Video Coding (HEVC) or H.265 standards have been implemented to achieve high error performance over wireless channels and large compression efficiency thereby optimizing channel bandwidth. The problem of establishing and maintaining reliable communication paths among mobile users employing multiclass bitstreams to achieve high spatial diversity and coding gains is a big challenge for researchers. In practice, multimedia standards employ data partitioning where different levels of importance are assigned to the multimedia content. The problem of transmission of coded HEVC data over wireless channels employing MIMO technologies to maximize high spatial diversity and coding gains has not been explicitly explored in existing research works. Moreover, to meet the demand of 5G networks in providing ultra-reliable low latency communications it is required to study fully the performance of multimedia data over wireless channels. To this end we propose, in this paper, to study and analyze the performance of coded MIMO systems employing a triple-class bitstreams source namely a high priority, medium priority and low priority class. The objective of our study is to promote Unequal Error Protection (UEP) as well as spatial diversity of the coded bitstreams without sacrificing the channel bandwidth and increasing the computational complexity. Space-Time Block Codes with Orthogonal properties and Hierarchical or Multi-level 8PSK modulation are considered in our analysis and the adopted approach will be shown, through numerical evaluation and simulations, that excellent UEP capabilities as well as high spatial diversity gains can be achieved. New Bit Error Rate (BER) expressions in closed form are derived for independent and identically distributed Rayleigh channel in the presence of Additive White Gaussian Noise (AWGN). Single and multiple fading channel reception systems with Maximum Ratio Combining (MRC) are considered. The simulations of our proposed UEP transmission model are designed and implemented in Matlab Simulink® R2017. It is shown that the most important priority data gives a coding gain of around 11 dB over the less important one at a BER of 10--6 for an 8-diversity order Orthogonal Space-Time Block Code (OSTBC) employing Hierarchical 8PSK modulation. This work can be extended to evaluate the error probability of multiuser coded systems for 4G and 5G networks.

Enabling 5G indoor services for residential environment using VLC technology

Visible light communication (VLC) has emerged as a viable complement to traditional radio frequency (RF) based systems and as an enabler for high data rate communications for beyond-5G (B5G) indoor communication systems. In particular, the emergence of new B5G-based applications with quality of service (QoS) requirements and massive connectivity has recently led to research on the required service-levels and the development of improved physical (PHY) layer methods. As part of recent VLC standards development activities, the IEEE has formed the 802.11bb “Light Communications (LC) for Wireless Local Area Networking” standardization group. This paper investigates the network requirements of 5G indoor services such as virtual reality (VR) and high-definition (HD) video for residential environments using VLC. In this paper, we consider such typical VLC scenarios with additional impairments such as light-emitting diode (LED) nonlinearity and imperfect channel feedback, and propose hyperparameter-free mitigation techniques using Reproducing Kernel Hilbert Space (RKHS) methods. In this context, we also propose using a direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM)-based adaptive VLC transmission method that uses precomputed bit error rate (BER) expressions for these RKHS-based detection methods and performs adaptive BER-based modulation-order switching. Simulations of channel impulse responses (CIRs) show that the adaptive transmission method provides significantly improved error rate performance, which makes it promising for high data rate VLC-based 5G indoor services.

Joint Source-Channel Decoding of Polar Codes for HEVC-Based Video Streaming

Ultra High-Definition (UHD) and Virtual Reality (VR) video streaming over 5G networks are emerging, in which High-Efficiency Video Coding (HEVC) is used as source coding to compress videos more efficiently and polar code is used as channel coding to transmit bitstream reliably over an error-prone channel. In this article, a novel Joint Source-Channel Decoding (JSCD) of polar codes for HEVC-based video streaming is presented to improve the streaming reliability and visual quality. Firstly, a Kernel Density Estimation (KDE) fitting approach is proposed to estimate the positions of error channel decoded bits. Secondly, a modified polar decoder called R-SCFlip is designed to improve the channel decoding accuracy. Finally, to combine the KDE estimator and the R-SCFlip decoder together, the JSCD scheme is implemented in an iterative process. Extensive experimental results reveal that, compared to the conventional methods without JSCD, the error data-frame correction ratios are increased. Averagely, 1.07% and 1.11% Frame Error Ratio (FER) improvements have been achieved for Additive White Gaussian Noise (AWGN) and Rayleigh fading channels, respectively. Meanwhile, the qualities of the recovered videos are significantly improved. For the 2D videos, the average Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) gains reach 14% and 34%, respectively. For the 360֯ videos, the average improvements in terms of Weighted-to-Spherically-uniform PSNR (WS-PSNR) and Voronoi-based Video Multimethod Assessment Fusion (VI-VMAF) reach 21% and 7%, respectively.

A New Methodology for Bridge Inspections in Linear Infrastructures from Optical Images and HD Videos Obtained by UAV

Many bridges and other structures worldwide present a lack of maintenance or a need for rehabilitation. The first step in the rehabilitation process is to perform a bridge inspection to know the bridge′s current state. Routine bridge inspections are usually based only on visual recognition. In this paper, a methodology for bridge inspections in communication routes using images acquired by unmanned aerial vehicle (UAV) flights is proposed. This provides access to the upper parts of the structure safely and without traffic disruptions. Then, a standardized and systematized novel image acquisition protocol is applied for data acquisition. Afterwards, the images are studied by civil engineers for damage identification and description. Then, specific structural inspection forms are completed using the acquired information. Recommendations about the need of new and more detailed inspections should be included at this stage when needed. The suggested methodology was tested on two railway bridges in France. Image acquisition of these structures was performed using an UAV for its ability to provide an expert assessment of the damage level. The main advantage of this method is that it makes it possible to safely accurately identify diverse damages in structures without the need for a specialised engineer to go to the site. Moreover, the videos can be watched by as many engineers as needed with no personal movement. The main objective of this work is to describe the systematized methodology for the development of bridge inspection tasks using a UAV system. According to this proposal, the in situ inspection by a specialised engineer is replaced by images and videos obtained from an UAV flight by a trained flight operator. To this aim, a systematized image/videos acquisition method is defined for the study of the morphology and typology of the structural elements of the inspected bridges. Additionally, specific inspection forms are proposed for every type of structural element. The recorded information will allow structural engineers to perform a postanalysis of the damage affecting the bridges and to evaluate the subsequent recommendations.

Cochlear implant explantation: An in vitro model to evaluate electrode explant force and trauma

Objectives Removal of a cochlear implant and its intracochlear electrode array is sometimes necessary, potentially causing cochlear explant trauma. Explantation typically occurs years post-implantation by which time reactive tissue has formed around the electrode. We aimed to create an in-vitro electrode explant model to examine explant forces and intracochlear trauma across multiple electrode types and insertion depths. Study design An in-vitro model using gel to represent tissue surrounding the electrode was developed. Pre-curved electrodes and straight electrodes at different insertion depths (20mm, 25mm, 28mm) were explanted from the model. During explantation, explant force was measured, and high-definition videos were recorded to capture electrode exit path and gel disruption. Results Explant force patterns varied based on electrode position in the scala tympani. Explant forces did not correlate with gel disruption, which represented explant trauma. The least gel disruption occurred with pre-curved electrodes and the under-inserted straight electrode. The greatest disruption occurred with the overly inserted straight electrode. Conclusion An in-vitro model using gel to mimic tissue surrounding the electrode may provide insights into potential electrode explant trauma. Explant force did not correlate with explant trauma in our model. Pre-curved electrodes and shallower insertion depth of a straight electrode resulted in the least amount of explant trauma.

Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network

The development of technologies that can generate Deepfake videos is expanding rapidly. These videos are easily synthesized without leaving obvious traces of manipulation. Though forensically detection in high-definition video datasets has achieved remarkable results, the forensics of compressed videos is worth further exploring. In fact, compressed videos are common in social networks, such as videos from Instagram, Wechat, and Tiktok. Therefore, how to identify compressed Deepfake videos becomes a fundamental issue. In this paper, we propose a two-stream method by analyzing the frame-level and temporality-level of compressed Deepfake videos. Since the video compression brings lots of redundant information to frames, the proposed frame-level stream gradually prunes the network to prevent the model from fitting the compression noise. Aiming at the problem that the temporal consistency in Deepfake videos might be ignored, we apply a temporality-level stream to extract temporal correlation features. When combined with scores from the two streams, our proposed method performs better than the state-of-the-art methods in compressed Deepfake videos detection.

Balanced Energy-Aware and Fault-Tolerant Data Center Scheduling.

Fault tolerance, performance, and throughput have been major areas of research and development since the evolution of large-scale networks. Internet-based applications are rapidly growing, including large-scale computations, search engines, high-definition video streaming, e-commerce, and video on demand. In recent years, energy efficiency and fault tolerance have gained significant importance in data center networks and various studies directed the attention towards green computing. Data centers consume a huge amount of energy and various architectures and techniques have been proposed to improve the energy efficiency of data centers. However, there is a tradeoff between energy efficiency and fault tolerance. The objective of this study is to highlight a better tradeoff between the two extremes: (a) high energy efficiency and (b) ensuring high availability through fault tolerance and redundancy. The main objective of the proposed Energy-Aware Fault-Tolerant (EAFT) approach is to keep one level of redundancy for fault tolerance while scheduling resources for energy efficiency. The resultant energy-efficient data center network provides availability as well as fault tolerance at reduced operating cost. The main contributions of this article are: (a) we propose an Energy-Aware Fault-Tolerant (EAFT) data center network scheduler; (b) we compare EAFT with energy efficient resource scheduling techniques to provide analysis of parameters such as, workload distribution, average task per servers, and energy consumption; and (c) we highlight effects of energy efficiency techniques on the network performance of the data center.

Face Recognition Based Automated Attendance Management System

At the beginning and end of each session, attendance is an important aspect of the daily classroom evaluation. When using traditional methods such as calling out roll calls or taking a student's signature, managing attendance can be a time-consuming task. The teacher normally checks it, although it's possible that a teacher will miss someone or some students' answers many times. Face recognition-based attendance system is a solution to the problem of recognizing faces for the purpose of collecting attendance by utilizing face recognition technology based on high-definition monitor video and other information technology. Instead of depending on time-consuming approaches, we present a real-time Face Recognition System for tracking student attendance in class in this work. The suggested method included identifying human faces from a webcam using the Viola-Jones technique, resizing the identified face to the desired size, and then processing the resized face using a basic Local Binary Patterns Histogram algorithm. After the recognition is completed, the attendance will be immediately updated in a SQLite database with the relevant information. Many institutions will profit greatly from this endeavor. As a result, the amount of time it takes and the number of human errors it makes are minimized, making it more efficient.

Stable and Efficient Red-Emitting Perovskite Cross-Shaped Nanoplates.

The stable cross-shaped CsPbI3 nanoplates (NPs) with red emission were achieved by chemical synthesis with the assistance of YCl3. Y3+ replacing Pb2+ results in the anisotropic growth of the CsPbI3 nanocrystal to form NPs. Four corners of the NPs dissolved, thus forming the cross-shaped NPs. The emission of NPs was shifted from near-infrared (690 nm) to red emission (640 nm) as the dopant amount of Y3+ increased. Y3+ widens the width of the bandgap, which is also proved by first-principles calculations. In addition, the Cl- passivated the surface defects of the NPs, suppressing the nonradiative recombination. The NPs showed remarkable high photoluminescence quantum yields (PLQY) of 96%. PLQY is even more than 60% when NPs have been stored in a glovebox for more than 90 days. The NPs with adjustable wavelength and enhanced stability have a huge application potential in the field of a high-definition display.

Seeing All Evil: The Global Cruelty of Digital Visibility

Abstract Cruelty is a historical constant across world politics. Nonetheless, something has changed. Today, it is possible to observe death, massacre, torture, police brutality, terrorist attacks, drone strikes, and more, in high-definition video. Sometimes, we can watch live. In this article, I ask what it means when the historical sanitization of cruelty, injustice, and violence is stripped away. I do so in three ways. First, I explore how digital media has transformed how knowledge of violence is produced, circulates, and affects those who witness it. I focus in particular on how this visibility of cruelty affectively fractures our ontological security, undermines societal solidarity, and amplifies polarization. Second, I describe how this process is marked by substantive global inequalities vis-à-vis who is “protected” (or not) from exposure to graphic imagery. Third, I ground my discussion empirically through participant observation conducted with members of the militia group Hezbollah that focused on their emotional, affective, discursive, and political reactions to watching videos circulating on social media depicting members of their own group committing war crimes in Syria. The article concludes by dwelling on the worrying possible political futures these dynamics appear to be opening up.

Role of Network Slicing in Software Defined Networking for 5G: Use Cases and Future Directions

Network slicing is expected to be critical in the deployment of 5G mobile networks and systems. On top of a single physical infrastructure, the technology enables operators to operate several virtual networks. As the 5G commercialization was recently deployed, network function virtualization (NFV) and software-defined networking (SDN) will drive network slicing. In this article, we present an overview of SDN in 5G, and the motivation, role, and market growth of network slicing. We then discuss usage scenarios of SDN in network slicing for 5G. The proposed architecture comprises the three usage scenarios: enhanced mobile broadband (eMBB) provides the support to varying types of services used; ultra-reliable low-latency communications (URLLC) provides a certain class of applications such as higher bandwidth, high definition video streaming, mobile TV, and so on; massive machine type communications (mMTC) throws light on the types of services used to connect huge numbers of devices. Finally, challenges and solutions based on network slicing in 5G are presented.

Architecture of a Low Latency H.264/AVC Video Codec for Robust ML based Image Classification

The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy.Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays.

A Technical Guide to Recording Antegrade Mesenteric Bypass from the Surgeon’s Point of View

The COVID-19 pandemic has forced education to transform into a virtual platform. The decreased number of open cases performed by general and vascular surgical trainees necessitates the supplementation of intraoperative education with digital technologies. We present the benefits and drawbacks of recording, editing, and uploading surgical procedures for educational use. In this paper, we describe an educational video of an antegrade mesenteric bypass to treat chronic mesenteric ischaemia. Fomenting a common practice of filming surgeries is a step towards creating a high-definition video library for surgical trainees to familiarize themselves with procedures and/or enhance their learning, especially during mandated social distancing.

Multi-Band Heterogeneous Wireless Network Architecture for Industrial Automation: A Techno-Economic Analysis

To attain automation across different applications, industries are beginning to leverage advancements in wireless communication technologies. A ”one-size-fits-all” solution cannot be applied since wireless technologies are selected according to application needs, quality of service requirements, and economic restrictions. To balance the trade-off between technical and economic requirements, a multi-band heterogeneous wireless network architecture is presented and discussed in this paper. Wireless local area network (WLAN) and distributed antenna system (DAS) with Long Term Evolution (LTE) are considered as the backbone for the multi-band heterogeneous network into which other wireless technologies can be integrated. The technical and economic feasibility of the network are evaluated through a techno-economic analysis (TEA). The economic feasibility of the proposed network is measured in terms of net present value while the technical feasibility is measured in terms of network throughput and latency. Finally, network performance for DAS with LTE and WLAN are verified using an NS3 simulator for machine-to-machine, real-time video, and high-definition video data transmissions. The TEA analysis showed that the number of DAS units required to achieve technical feasibility is less than WLAN units, but the overall cost of DAS units are higher compared to WLAN units, even without taking into consideration industrial, scientific, and medical band technologies.

Automatic imaging system mounted on boom sprayer for crop scouting using an off-the-shelf RGB camera

This article describes an imaging system mounted on the boom of a self-propelled sprayer, which was developed to automatically capture and store high-definition video images, while handling agrochemical spray and machine vibration. This was accomplished using an off-the-shelf camera (DSC-RX0M2, Sony) and two single board computers. To enable automatic operation, the system was designed to start and terminate along with the engine. All components were powered successfully except for the camera which runs only on its own battery. This was overcome by delaying powering certain hardware and software components of the system for different periods to allow consistent initialization of the entire system. Accordingly, the current version of the system starts imaging 15 min after engine start-up and guarantees performance for 3 h of consecutive video capturing. After several field trials, the imaging system became able to save images with the required quality. The developed software is able to handle images at a rate of 30 frames per second without delay. In order to handle dynamic light conditions, the camera shooting mode was set to manual at a very high shutter speed. This provided detailed images at both sunny and cloudy conditions. While the current captured images proved to be adequate to start machine vision system development, there is a setback when trying to get images at early morning or evening as they tend to be too dark. This will be handled while upgrading the system towards a machine vision for the purpose of automatic scouting.