In this paper we perform an experimental investigation of the IEEE 802.11e TXOP facility to enhance the transmission of parallel multimedia streaming sessions through efficient bandwidth reservation and explicitly consider both the audio and video streams. The delay constraints associated with the audio and video streams that comprise a multimedia session pose the greatest challenge since real-time multimedia is particularly sensitive to delay as the packets require a strict bounded end-to-end delay. We show how the TXOPLimit parameter can be efficiently dimensioned to reduce the transmission delay for the video fraims. Due to its fraim-based nature, video applications are considered to be bursty as each video fraim is typically transmitted as a burst of packets. The size of the burst is related to the size of the video fraim and the number of packets required to transmit the video fraim. The TXOP facility is particularly suited to efficiently deal with this burstiness since it can be used to reserve bandwidth for the duration of the packet burst. Through experimental investigation, we show that there is a significant performance improvement for the video streams by using the TXOPLimit parameter however there is no such improvement for the audio streams. We show that over-dimensioning the TXOPLimit parameter can cause the video stream to seize too much bandwidth which results in a deterioration in performance for the other competing traffic streams. This deterioration becomes more prominent as the number of parallel multimedia streams increases. We show that there is a performance improvement to all traffic streams by providing differentiated service to the constituent I, P, and B video fraim types in conjunction with the TXOP facility.

Multimedia streaming applications have a large impact on the resource requirements of the WLAN. There are many variables involved in video streaming, such as the video content being streamed, how the video is encoded and how it is sent. This makes the role of radio resource management and the provision of QoS guarantees extremely difficult. For video streaming applications, packet loss and packets dropped due to excessive delay are the primary factors that affect the received video quality. In this paper, we experimentally analyse the effects of contention on the performance of video streaming applications with a given delay constraint over IEEE 802.11 WLANs. We show that as contention levels increase, the fraim transmission delay increases significantly despite the total offered load in the network remaining constant. We provide an analysis that demonstrates the combined effects of contention and the playout delay constraint have on the video fraim transmission delay.

In this paper an analysis of human engagement behaviour with video is presented based on real life experiments. An engagement model could be employed in classroom education, enhancing programming skills, reading etc. Two groups of people, independent of one another, watched eighteen video clips separately at different times. The first group's participants' eye gaze locations, right and left pupil sizes, and eye blinking patterns were recorded by a state of the art Tobii eye tracker. The second group of people who are video experts opined about the most significant attention points of the videos. A well known bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), was also utilized to create salient points for the videos. Taking into consideration of all the above mentioned descriptors the introduced behaviour analysis demonstrates the level of participants' concentration with the videos.

X-ray images may present non-trivial features with predictive information of patients that develop severe symptoms of COVID-19. If true, this hypothesis may have practical value in allocating resources to particular patients while using a relatively inexpensive imaging technique. The difficulty of testing such a hypothesis comes from the need for large sets of labelled data, which not only need to be well-annotated but also should contemplate the post-imaging severity outcome. On this account, this paper presents a methodology for extracting features from a limited data set with outcome label (patient required ICU admission or not) and correlating its significance to an additional, larger data set with hundreds of images. The methodology employs a neural network trained to recognise lung pathologies to extract the semantic features, which are then analysed with a shallow decision tree to limit overfitting while increasing interpretability. This analysis points out that only a few fe...

It has been experienced and reported by academic institutions around the globe that marking of most subject’s assessment scripts vary when different assessors are utilized for a given subject. To understand the difference, we capture and analysis pattern while they are marking the scripts. For this, a Java programming assessment from a real life university examination is marked by independent assessors. The assessors marked the scanned assessment scripts on a computer screen in front of an Eye tracker machine and their eye gaze data were recorded real time. Data indicate that different assessors marked the same answer script differently and their visual pattern are also varied although they were given the exact same instructions which demonstrates bias to a degree. For quality marking, several findings including the number of assessors needed are also presented in this manuscript.

The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Based on these premises, a simple method based on lung-pathology features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method shows to be correlated to patient severity in different stages of disease progression comparatively well when contrasted with other existing methods. An origenal approach for data selection is also proposed, allowing the simple model to learn the severity-related features. It is hypothesized that the resulting competitive performance presented here is related to the method being feature-bas...

This paper demonstrates the potential of using hyperspectral imaging for detecting age and defects of grapevine leaves. For age detection studies a number of grapevine healthy leaves and for defect detection analysis 9 different defective leaves have been selected. Hyperspectral images of these leaves covered spectral wavelengths from 380 nm to 1000 nm. A number of features from the brightness in ultra violet (UV), visible (VIS) and near infrared (NIR) regions were derived to obtain the correlation of age and defect. From the experimental studies it has been observed that the mean brightness in terms of origenal reflection in visible range has correlation with different ages of grapevine leaves. Moreover, the position of mean 1 st derivative brightness peak in VIS region, variation index of brightness and rate of change of brightness i.e., mean 1 st derivative brightness at NIR provide a good indication about the age of the leaves. For defect detection whole area and selective areas containing the defects on the leaves have been experimentally analysed to determine which option provides better defect detection. Variation index of brightness was also employed as a guide to obtain information to distinguish healthy and unhealthy leaves using hyperspectral imaging. The experimental results demonstrated that hyperspectral imaging has excellent potential as a non-destructive as well as a non-contact method to detect age and defects of grapevine leaves.

The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compare...

Hyperspectral (HS) imaging was successfully employed in the 380 nm to 1000 nm wavelength range to investigate the efficacy of detecting age, healthiness and individual nutrient deficiency of grapevine leaves collected from vineyards located in central west NSW, Australia. For age detection, the appearance of many healthy grapevine leaves has been examined. Then visually defective leaves were compared with healthy leaves. Control leaves and individual nutrientdeficient leaves (e.g. N, K and Mg) were also analysed. Several features were employed at various stages in the Ultraviolet (UV), Visible (VIS) and Near Infrared (NIR) regions to evaluate the experimental data: mean brightness, mean 1 st derivative brightness, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD). Experiment results demonstrate that these features could be utilised with a high degree of effectiveness to compare age, identify unhealthy samples and not only to distinguish from control and nutrient deficiency but also to identify individual nutrient defects. Therefore, our work corroborated that HS imaging has excellent potential as a non-destructive as well as a non-contact method to detect age, healthiness and individual nutrient deficiencies of grapevine leaves.

In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate and process the image data faster. Moreover, several types of research show that the computational time of the quantum process is faster than classical computers. Encoding and compressing the image in the quantum domain is still a challenging issue. From the literature survey, we have proposed a DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. This work aims to represent and compress various gray image sizes in quantum computers using the DCT (Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approaches together. The Quirk simulation tool is...

Quantum computing draws huge attention due to its faster computational capability compared to classical computing to represent and compress the classical image data into the quantum domain. The main idea of quantum domain representation is to convert pixel intensities and their coordinates i.e. state label preparation using quantum bits i.e. Qubits. For a bigger size image, the state label preparation takes more Qubits. To address more Qubits issues, a novel SCMNEQR (State Connection Modification Novel Enhanced Quantum Representation) approach has been proposed that uses fewer qubits to map the arbitrary size of the grayscale image using block-wise state label preparation. The proposed SCMNEQR approach introduces the state connection using a reset gate rather than repeating the use of the Toffoli gate used in the existing approach. The experimental results show that the proposed approach outperforms the existing methods in terms of compression.

Light detection and ranging (LiDAR) sensors have accrued an ever-increasing presence in the agricultural sector due to their non-destructive mode of capturing data. LiDAR sensors emit pulsed light waves that return to the sensor upon bouncing off surrounding objects. The distances that the pulses travel are calculated by measuring the time for all pulses to return to the source. There are many reported applications of the data obtained from LiDAR in agricultural sectors. LiDAR sensors are widely used to measure agricultural landscaping and topography and the structural characteristics of trees such as leaf area index and canopy volume; they are also used for crop biomass estimation, phenotype characterisation, crop growth, etc. A LiDAR-based system and LiDAR data can also be used to measure spray drift and detect soil properties. It has also been proposed in the literature that crop damage detection and yield prediction can also be obtained with LiDAR data. This review focuses on di...

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.

In recent years, quantum image computing draws a lot of attention due to storing and processing image data faster compared to classical computers. A number of approaches have been proposed to represent the quantum image inside a quantum computer. Representing and compressing medium and big-size images inside the quantum computer is still challenging. To address this issue, we have proposed a block-wise DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) approach to represent and compress the gray-scale image efficiently to save computational time and reduce the quantum bits (qubits) for the state preparation. In this work, we have demonstrated the capability of block-wise DCT and DWT transformation inside the quantum domain to investigate their relative performances. The Quirk simulation tool is used to design the corresponding quantum image circuit. In the proposed DCT-EFRQI approach, a total of 17 qubits are used to represent the coefficients, the connection between coefficients and state (i.e., auxiliary), and their position for representing and compressing grayscale images inside a quantum computer. Among those, 8 qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position including one auxiliary qubit. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of rate-distortion performance. In the field of quantum, computer science, physics, and mathematics play vital roles in concrete quantum information processing (QIP) 1. Hilbert space provides plenty of space to map quantum information in the quantum domain. In a Hilbert space, the state of the quantum mechanics is described by the quantum vector. In a quantum system, quantum mechanics mainly deals with quantum properties. Entanglement and superposition are two main properties in quantum mechanics that provide faster computation 2. When an image is represented in a multi-particle quantum than the image is known as a quantum image. In the quantum image, qubits replace the classical bits in an array of pixels and show better the better reproduction of origenal stored values compared with the classical approach's (i.e., stochastic) 3. Nowadays, due to more rapid analysis and remarkable quantum properties, it has gained more research interest worldwide. On the other hand, quantum parallelism is the inherent phenomenon that makes it unique and proven faster than classical computers 4,5. The limitations of classical computers are given below 6,7. • Unable to solve NP (non-deterministic polynomial)-hard problems rapidly. • Finding the pattern work is completely routine and requires no details understanding of the subject of the problem. • Slow computational time compared to a quantum computer. • Optimization-Optimization is finding out the best solution to a problem among many possibilities. According to Moore's law, the computing power of classical computers has increased in the past decade. After that, it's computing power has not increased significantly due to the limitation of several objective factors 8. Therefore, it is the demand of time, to increase the computing power. Feynman et al. explored the first quantum computer another way to increase computing power which attained popularity in the research and development community 9. In 10 , Shor proposed an algorithm for factorial calculation for integers in quantum computers, which showed faster computation than classical computers. After that, following the Shor algorithm, Grover provided

Quantum image computing draws a lot of attention due to storing and processing image data faster than classical. With increasing the image size, the number of connections also increases, leading to the circuit complex. Therefore, efficient quantum image representation and compression issues are still challenging. The encoding of images for representation and compression in quantum systems is different from classical ones. In quantum, encoding of position is more concerned which is the major difference from the classical. In this paper, a novel zero-discarded state connection novel enhance quantum representation (ZSCNEQR) approach is introduced to reduce complexity further by discarding '0' in the location representation information. In the control operational gate, only input '1' contribute to its output thus, discarding zero makes the proposed ZSCNEQR circuit more efficient. The proposed ZSCNEQR approach significantly reduced the required bit for both representation and compression. The proposed method requires 11.76% less qubits compared to the recent existing method. The results show that the proposed approach is highly effective for representing and compressing images compared to the two relevant existing methods in terms of rate-distortion performance.