Search results for: real time kernel preemption

Authors: Ting (Grace) Lin, Jianhong (Cecilia) Xia, Todd Robinson

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Accessibility analysis, examining people’s ability to access facilities and destinations, is a fundamental assessment for transport planning, policy making, and social exclusion research. Dynamic accessibility which measures accessibility in real-time traffic environment has been an advanced accessibility indicator in transport research. It is also a useful indicator to help travelers to understand travel time daily variability, assists traffic engineers to monitor traffic congestions, and finally develop effective strategies in order to mitigate traffic congestions. This research involved real-time traffic information by collecting travel time data with 15-minute interval via the TomTom^® API. A framework for measuring dynamic accessibility was then developed based on the gravity theory and accessibility dichotomy theory through space and time interpolation. Finally, the dynamic accessibility can be derived at any given time and location under dynamic accessibility spatial analysis framework.

Keywords: dynamic accessibility, hot spot, transport research, TomTom® API

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: Mingyang Sun, Guozheng Yan, Dasheng Liu, Lei Yang

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Driven by the demand of intelligent monitoring in rehabilitation centers or hospitals, a high accuracy real-time location system based on UWB (ultra-wideband) technology was proposed. The system measures precise location of a specific person, traces his movement and visualizes his trajectory on the screen for doctors or administrators. Therefore, doctors could view the position of the patient at any time and find them immediately and exactly when something emergent happens. In our design process, different algorithms were discussed, and their errors were analyzed. In addition, we discussed about a , simple but effective way of correcting the antenna delay error, which turned out to be effective. By choosing the best algorithm and correcting errors with corresponding methods, the system attained a good accuracy. Experiments indicated that the ranging error of the system is lower than 7 cm, the locating error is lower than 20 cm, and the refresh rate exceeds 5 times per second. In future works, by embedding the system in wearable IoT (Internet of Things) devices, it could provide not only physical parameters, but also the activity status of the patient, which would help doctors a lot in performing healthcare.

Keywords: intelligent monitoring, ultra-wideband technology, real-time location, IoT devices, smart healthcare

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Authors: Xiaoyan Dai, Hsieh Yisan

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Barcode processing of captured images is a huge challenge, as different shooting conditions can result in different barcode appearances. This paper proposes a deep learning-based barcode detection using synthetic-to-real data augmentation. We first augment barcodes themselves; we then augment images containing the barcodes to generate a large variety of data that is close to the actual shooting environments. Comparisons with previous works and evaluations with our original data show that this approach achieves state-of-the-art performance in various real images. In addition, the system uses hybrid resolution for barcode “scan” and is applicable to real-time applications.

Keywords: barcode detection, data augmentation, deep learning, image-based processing

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Authors: Susan Ka Yee Chow

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The Coronavirus disease (COVID-19) reached Hong Kong in 2019 resulting in epidemic in late January 2020. Considering the epidemic development, tertiary institutions made announcements that all on-campus classes were suspended since 01/29/2020. In Tung Wah College, e-learning was adopted in all courses for all programmes. For the undergraduate nursing students, the contact hours and curriculum are bounded by the Nursing Council of Hong Kong to ensure core competence after graduation. Unlike the usual e-learning where students are allowed having flexibility of time and place in their learning, real time learning mode using Blackboard was used to mimic the actual classroom learning environment. Students were required to attend classes according to the timetable using online platform. For lectures, voice over PowerPoint file was the initial step for mass lecturing. Real time lecture was then adopted to improve interactions between teacher and students. Post-lecture quizzes were developed to monitor the effectiveness of lecture delivery. The seminars and tutorials were conducted using real time mode where students were separated into small groups with interactive discussions with teacher within the group. Live time demonstrations were conducted during laboratory sessions. All teaching sessions were audio/video recorded for students’ referral. The assessments including seminar presentation and debate were retained. The learning mode creates an atmosphere for students to display the visual, audio and written works in a non-threatening atmosphere. Other students could comment using text or direct voice as they desired. Real time online learning is the pedagogy to replace classroom contacts in the emergent and unforeseeable circumstances. The learning pace and interaction between students and students with teacher are maintained. The learning mode has the advantage of creating an effective and beneficial learning experience.

Keywords: e-learning, nursing curriculum, real time mode, teaching and learning

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Authors: Alica Miller

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A semiflow is a triple consisting of a Hausdorff topological space $X$, a commutative topological monoid $T$ and a continuous monoid action of $T$ on $X$. The acting monoid $T$ is usually either the discrete monoid $\N_0$ of nonnegative integers (in which case the semiflow can be defined as a pair $(X,f)$ consisting of a phase space $X$ and a continuous function $f:X\to X$), or the monoid $\R_+$ of nonnegative real numbers (the so-called one-parameter monoid). However, it turns out that there are real-life situations where it is useful to consider the acting monoids that are a combination of discrete and continuous monoids. That, for example, happens, when we are observing certain dynamical system at discrete moments, but after some time realize that it would be beneficial to continue our observations in real time. The acting monoid in that case would be $T=\{0, t_0, 2t_0, \dots, (n-1)t_0\} \cup [nt_0,\infty)$ with the operation and topology induced from real numbers. This partly explains the motivation for the level of generality which is pursued in our research. We introduce the PSP monoids, which include all but ``pathological'' monoids, and most of our statements hold for them. The topic of our presentation are some recent results about chaos-related properties in semiflows, indecomposability and sensitivity of semiflows in the described general context.

Keywords: chaos, indecomposability, PSP monoids, semiflow, sensitivity

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Authors: Matthieu-P. Schapranow

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Recruitment of participants for clinical trials requires the screening of a big number of potential candidates, i.e. the testing for trial-specific inclusion and exclusion criteria, which is a time-consuming and complex task. Today, a significant amount of time is spent on identification of adequate trial participants as their selection may affect the overall study results. We introduce a unique patient eligibility metric, which allows systematic ranking and classification of candidates based on trial-specific filter criteria. Our web application enables real-time analysis of patient data and assessment of candidates using freely definable inclusion and exclusion criteria. As a result, the overall time required for identifying eligible candidates is tremendously reduced whilst additional degrees of freedom for evaluating the relevance of individual candidates are introduced by our contribution.

Keywords: in-memory technology, clinical trials, screening, eligibility metric, data analysis, clustering

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Authors: Thifhuriwi Emmanuel Madzunye, Patricia Harpur, Ephias Ruhode

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A review of the pertinent literature identified a gap concerning the hindrances and opportunities accompanying the implementation of real-time online learning systems (RTOLs) in rural areas. Whilst RTOLs present a possible solution to teaching and learning issues in rural areas, little is known about the implementation of digital strategies among schools in isolated communities. This study explores associated guidelines that have the potential to inform decision-making where Internet-based education could improve educational opportunities. A systematic literature review has the potential to consolidate and focus on disparate literature served to collect interlinked data from specific sources in a structured manner. During qualitative data analysis (QDA) of selected publications via the application of a QDA tool - ATLAS.ti, the following overarching themes emerged: digital divide, educational strategy, human factors, and support. Furthermore, findings from data collection and literature review suggest that signiant factors include a lack of digital knowledge, infrastructure shortcomings such as a lack of computers, poor internet connectivity, and handicapped real-time online may limit students’ progress. The study recommends that timeous consideration should be given to the influence of the digital divide. Additionally, the evolution of educational strategy that adopts digital approaches, a focus on training of role-players and stakeholders concerning human factors, and the seeking of governmental funding and support are essential to the implementation and success of RTOLs.

Keywords: communication, digital divide, digital skills, distance, educational strategy, government, ICT, infrastructures, learners, limpopo, lukalo, network, online learning systems, political-unrest, real-time, real-time online learning, real-time online learning system, pass-rate, resources, rural area, school, support, teachers, teaching and learning and training

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Authors: Ergun Sakalar, Kubra Bilgic

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In the last decades, real-time PCR has become a reliable tool preferred to use in many laboratories for pathogen detection. This technique allows for monitoring target amplification via fluorescent molecules besides admit of quantitative analysis by enabling of convert outcomes of thermal cycling to digital data. Sensitivity and traceability of real-time PCR are based on measuring of fluorescence that appears only when fluorescent reporter dye bound to specific target DNA.The fluorescent reporter systems developed for this purpose are divided into two groups. The first group consists of intercalator fluorescence dyes such as SYBR Green, EvaGreen which binds to double-stranded DNA. On the other hand, the second group includes fluorophore-labeled oligonucleotide probes that are separated into three subgroups due to differences in mechanism of action; initial primer-probes such as Cyclicons, Angler®, Amplifluor®, LUX™, Scorpions, and the second one hydrolysis probes like TaqMan, Snake assay, finally hybridization probes, for instance, Molecular Beacons, Hybprobe/FRET, HyBeacon™, MGB-Eclipse, ResonSense®, Yin-Yang, MGB-Pleiades. In addition nucleic acid analogues, an increase of probe affinity to target site is also employed with fluorescence-labeled probes. Consequently, abundant real-time PCR detection chemistries are chosen by researcher according to the field of application, mechanism of action, advantages, and proper structures of primer/probes.

Keywords: fluorescent dye, food safety, molecular probes, nucleic acid analogues

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Authors: Yamamah Abdulrazaq, Lahieb A. Abrahim, Samuel E. Davies, Iain Shewring

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An electric water heater (EWH) is a powerful appliance that uses electricity in residential, commercial, and industrial settings, and the ability to control them properly will result in cost savings and the prevention of blackouts on the national grid. This article discusses the usage of timers in EWH control strategies for demand-side management (DSM). Up to the authors' knowledge, there is no systematic mapping review focusing on the utilisation of EWH control strategies in DSM has yet been conducted. Consequently, the purpose of this research is to identify and examine main papers exploring EWH procedures in DSM by quantifying and categorising information with regard to publication year and source, kind of methods, and source of data for monitoring control techniques. In order to answer the research questions, a total of 31 publications published between 1999 and 2023 were selected depending on specific inclusion and exclusion criteria. The data indicate that direct load control (DLC) has been somewhat more prevalent than indirect load control (ILC). Additionally, the mixing method is much lower than the other techniques, and the proportion of Real-time data (RTD) to non-real-time data (NRTD) is about equal.

Keywords: demand side management, direct load control, electric water heater, indirect load control, non real-time data, real-time data

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Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

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This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: sound detection, impulsive signal, background noise, neural network

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Authors: Faisal Zaman, Ya Ping Wong, Boon Yian Ng

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Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this, we present a novel approach using modified kernel density estimation (KDE) technique with bilateral filtering to remove noisy points and outliers. First we present a method for estimating optimal bandwidth of multivariate KDE using particle swarm optimization technique which ensures the robust performance of density estimation. Then we use mean-shift algorithm to find the local maxima of the density estimation which gives the centroid of the clusters. Then we compute the distance of a certain point from the centroid. Points belong to outliers then removed by automatic thresholding scheme which yields an accurate and economical point surface. The experimental results show that our approach comparably robust and efficient.

Keywords: point preprocessing, outlier removal, surface reconstruction, kernel density estimation

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Authors: Adekunle O. Afolabi, Pekka Toivanen

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The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.

Keywords: recommendation systems, Internet of Things, healthcare, homecare, real-time

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Authors: Qiuyu Dai, Haochong Zhang, Xiangrong Liu

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Diagonal sparse matrix-vector multiplication is a well-studied topic in the fields of scientific computing and big data processing. However, when diagonal sparse matrices are stored in DIA format, there can be a significant number of padded zero elements and scattered points, which can lead to a degradation in the performance of the current DIA kernel. This can also lead to excessive consumption of computational and memory resources. In order to address these issues, the authors propose the DIA-Adaptive scheme and its kernel, which leverages the parallel instruction sets on MLU. The researchers analyze the effect of allocating a varying number of threads, clusters, and hardware architectures on the performance of SpMV using different formats. The experimental results indicate that the proposed DIA-Adaptive scheme performs well and offers excellent parallelism.

Keywords: adaptive method, DIA, diagonal sparse matrices, MLU, sparse matrix-vector multiplication

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: J. E. Oti, J. M. Kinuthia, R. Robinson, P. Davies

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This work reports the potential of using Palm Kernel (PK) ash and shell as a partial substitute for Portland Cement (PC) and coarse aggregate in the development of mortar and concrete. PK ash and shell are agro-waste materials from palm oil mills, the disposal of PK ash and shell is an environmental problem of concern. The PK ash has pozzolanic properties that enables it as a partial replacement for cement and also plays an important role in the strength and durability of concrete, its use in concrete will alleviate the increasing challenges of scarcity and high cost of cement. In order to investigate the PC replacement potential of PK ash, three types of PK ash were produced at varying temperature (350-750 degrees) and they were used to replace up to 50% PC. The PK shell was used to replace up to 100% coarse aggregate in order to study its aggregate replacement potential. The testing programme included material characterisation, the determination of compressive strength, tensile splitting strength and chemical durability in aggressive sulfate-bearing exposure conditions. The 90 day compressive results showed a significant strength gain (up to 26.2 N/mm2). The Portland cement and conventional coarse aggregate has significantly higher influence in the strength gain compared to the equivalent PK ash and PK shell. The chemical durability results demonstrated that after a prolonged period of exposure, significant strength losses in all the concretes were observed. This phenomenon is explained, due to lower change in concrete morphology and inhibition of reaction species and the final disruption of the aggregate cement paste matrix.

Keywords: sustainability, concrete, mortar, palm kernel shell, compressive strength, consistency

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Authors: Yawen Wang, Hongchang Chen, Shaomei Li, Chao Gao, Jiangpeng Zhang

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As the main challenge for target tracking is accounting for target scale change and real-time, we combine Mean-Shift and PCA-SIFT algorithm together to solve the problem. We introduce similarity comparison method to determine how the target scale changes, and taking different strategies according to different situation. For target scale getting larger will cause location error, we employ backward tracking to reduce the error. Mean-Shift algorithm has poor performance when tracking scale-changing target due to the fixed bandwidth of its kernel function. In order to overcome this problem, we introduce PCA-SIFT matching. Through key point matching between target and template, that adjusting the scale of tracking window adaptively can be achieved. Because this algorithm is sensitive to wrong match, we introduce RANSAC to reduce mismatch as far as possible. Furthermore target relocating will trigger when number of match is too small. In addition we take comprehensive consideration about target deformation and error accumulation to put forward a new template update method. Experiments on five image sequences and comparison with 6 kinds of other algorithm demonstrate favorable performance of the proposed tracking algorithm.

Keywords: target tracking, PCA-SIFT, mean-shift, scale-adaptive

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Authors: Ya-Mei Chang

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This research is intended to apply spatio-temporal point process methods to the dengue fever data in Tainan. The spatio-temporal intensity function of the dataset is assumed to be separable. The kernel estimation is a widely used approach to estimate intensity functions. The intensity function is very helpful to study the relation of the spatio-temporal point process and some covariates. The covariate effects might be nonlinear. An nonparametric smoothing estimator is used to detect the nonlinearity of the covariate effects. A fitted parametric model could describe the influence of the covariates to the dengue fever. The correlation between the data points is detected by the K-function. The result of this research could provide useful information to help the government or the stakeholders making decisions.

Keywords: dengue fever, spatial point process, kernel estimation, covariate effect

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Authors: Jing Hu, Dong Yang

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With the increasingly difficult development and operating environment of exploration, there are many new challenges and difficulties in developing and exploiting oil and gas resources. These include the ability to dynamically monitor wells and provide data and assurance for the completion and production of high-cost and complex wells. A key technology in providing these assurances and maximizing oilfield profitability is real-time permanent reservoir monitoring. The emergence of optical fiber sensing systems has gradually begun to replace traditional electronic systems. Traditional temperature sensors can only achieve single-point temperature monitoring, but fiber optic sensing systems based on the Bragg grating principle have a high level of reliability, accuracy, stability, and resolution, enabling cost-effective monitoring, which can be done in real-time, anytime, and without well intervention. Continuous data acquisition is performed along the entire wellbore. The integrated package with the downhole pressure gauge, packer, and surface system can also realize real-time dynamic monitoring of the pressure in some sections of the downhole, avoiding oil well intervention and eliminating the production delay and operational risks of conventional surveys. Real-time information obtained through permanent optical fibers can also provide critical reservoir monitoring data for production and recovery optimization.

Keywords: PDHM, optical fiber, coiled tubing, photoelectric composite cable, digital-oilfield

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Authors: Hina Munir Abbasi

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The current paper examines the relationship and diversification ability of Islamic stock indices /conventional stocks indices and Real Estate Investment Trust (REITs).To represent conditional dependency between stocks and REITs in a more realistic way, new modeling technique, time-varying copula with switching dependence is used. It represents reliance structure more accurately and realistically than a single copula regime as dependence may alter between positive and negative correlation regimes with time. The fluctuating behavior of markets has significant impact on economic variables; especially the downward trend during crisis. Overall addition of Real Estate Investment Trust in stocks portfolio reduces risks and provide better diversification benefit. Results varied depending upon the circumstances of the country. REITs provides better diversification benefits for Islamic Stocks, when both markets are bearish and can provide hedging benefit for conventional stocks portfolio.

Keywords: conventional stocks, real estate investment trust, copula, diversification, risk spillover, safe heaven

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Authors: R. Dariani, S. Schmidt, R. Kasper

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Based on a non-linear single track model which describes the dynamics of vehicle, an optimal path planning strategy is developed. Real time optimization is used to generate reference control values to allow leading the vehicle alongside a calculated lane which is optimal for different objectives such as energy consumption, run time, safety or comfort characteristics. Strict mathematic formulation of the autonomous driving allows taking decision on undefined situation such as lane change or obstacle avoidance. Based on position of the vehicle, lane situation and obstacle position, the optimization problem is reformulated in real-time to avoid the obstacle and any car crash.

Keywords: autonomous driving, obstacle avoidance, optimal control, path planning

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Authors: Jens Ehm

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Manufacturing in supply chains requires an efficient organisation of production and transport processes in order to guarantee the supply of all partners within the chain with the material that is needed for the reliable fulfilment of tasks. If one partner is not able to supply products for a certain period, these products might be missing as the working material for the customer to perform the next manufacturing step, potentially as supply for further manufacturing steps. This way, local disruptions can influence the whole supply chain. In order to avoid material shortages, an efficient scheduling of tasks is necessary. However, the occurrence of unexpected disruptions cannot be eliminated, so that a modification of the schedule should be arranged as fast as possible. This paper discusses the challenges for the implementation of real-time scheduling and control methods and presents a graph-based approach that enables the integrated scheduling of production and transport processes for multiple supply chain partners and offers the potential for quick adaptations to parts of the initial schedule.

Keywords: production, logistics, integrated scheduling, real-time scheduling

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Authors: Simon Keller, Rainer Mueller

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Continuous range queries are a common means to handle mobile clients in high-density areas. Most existing approaches focus on settings in which the range queries for location-based services are more or less static, whereas the mobile clients in the ranges move. We focus on a category called dynamic real-time range queries (DRRQ), assuming that both, clients requested by the query and the inquirers, are mobile. In consequence, the query parameters and the query results continuously change. This leads to two requirements: the ability to deal with an arbitrarily high number of mobile nodes (scalability) and the real-time delivery of range query results. In this paper, we present the highly decentralized solution adaptive quad streaming (AQS) for the requirements of DRRQs. AQS approximates the query results in favor of a controlled real-time delivery and guaranteed scalability. While prior works commonly optimize data structures on the involved servers, we use AQS to focus on a highly distributed cell structure without data structures automatically adapting to changing client distributions. Instead of the commonly used request-response approach, we apply a lightweight streaming method in which no bidirectional communication and no storage or maintenance of queries are required at all.

Keywords: approximation of client distributions, continuous spatial range queries, mobile objects, streaming-based decentralization in spatial mobile environments

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Authors: Saheed O. Akinwale, Olufemi A. Koya

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Based on the need to modify palm kernel shell (PKS) and coconut shell (CNS) for some engineering applications, the study evaluated some physical characteristics and fracture resistance, relevant in estimating energy requirements in comminution of the nutshells. The shells, obtained from local processing mills, were washed, sun-dried and sorted to remove kernels, nuts and other extraneous materials. Experiments were then conducted to determine the thickness, density, moisture content, and hardness of the shells. Fracture resistances were characterised by the average compressive load, stiffness and toughness at bio-yield point of specially prepared section of the shells, under quasi-static compression loading. The densities of the dried PKS at 7.12% and the CNS at 6.47% (wb) moisture contents were 1291.20 and 1247.40 kg/m3, respectively. The corresponding Brinnel Hardness Numbers were 58.40 ± 1.91 and 56.33 ± 4.33. Close shells thickness of both PKS and CNS exhibited identical physical properties although; CNS is relatively larger in physical dimensions than PKS. The findings further showed that both shell types exhibited higher resistance with compression along the longitudinal axes than the transverse axes. With compressions along the longitudinal axes, the fracture force were 1.41 ± 0.11 and 3.62 ± 0.09 kN; bio-stiffness; 934.70 ± 67.03 kN/m and 1980.74 ± 8.92 kN/m; and toughness, 2.17 ± 0.16 and 6.51 ± 0.15 KN mm for the PKS and CNS, respectively. With the estimated toughness of CNS higher than that of PKS, the study showed the requirement of higher comminution energy for CNS.

Keywords: bio-stiffness, coconut shell, comminution, crushing strength, energy requirement, palm kernel shell, toughness

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Authors: Adrizal, Irsan Ryanto, Sri Juwita, Adika Sugara, Tino Bapirco

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The objective of the research was to study the influence of the inclusion of the cabbage waste into a buffalo rations made of sugarcane waste on the feed formula and characteristic of complete feed silage. Research carried out a two-stage i.e. the feed formulation and experiment of making complete feed silage. Feed formulation is done by linear programming. Data input is the price of feed stuffs and their nutrient contents as well as requirements for rations, while the output is the use of each feed stuff and the price of complete feed. The experiment of complete feed silage was done by a completely random design 4 x 4. The treatments were 4 inclusion levels of the cabbage waste i.e. 0%,(T1) 5%(T2), 10%(T3) and 15% (T4), with 4 replications. The result of feed formulation for T1 was cabbage (0%), sugarcane top (17.9%), bagasse (33.3%), Molasses (5.0%), cabagge (0%), Thitonia sp (10.0%), rice brand (2.7%), palm kernel cake (20.0%), corn meal (9.1%), bond meal (1.5%) and salt (0.5%). The formula of T2 was cabagge (5%), sugarcane top (1.7%), bagasse (45.2%), Molasses (5.0%), , Thitonia sp (10.0%), rice brand (3.6%), palm kernel cake (20.0%), corn meal (7.5%), bond meal (1.5%) and salt (0.5%). The formula of T3 was cabbage (10%), sugarcane top (0%), bagasse (45.3%), Molasses (5.0%), Thitonia sp (10.0%), rice brand (3.8%), palm kernel cake (20.0%), corn meal (3.9%), bond meal (1.5%) and salt(0.5%). The formula of T4 was cabagge (15.0%), sugarcane top (0%), bagasse (44.1%), Molasses (5.0%), Thitonia sp (10.0%), rice brand (3.9%), palm kernel cake (20.0%), corn meal (0%), bond meal (1.5%) and salt (0.5%). An increase in the level of inclusion of the cabbage waste can decrease the cost of rations. The cost of rations (IDR/kg on DM basis) were 1442, 1367, 1333, and 1300 respectively. The rations formula were not significantly (P > 0.05) influent the on fungal colonies, smell, texture and color of the complete ration silage, but the pH increased significantly (P < 0.05). It concluded that inclusion of cabbage waste can minimize the cost of buffalo ration, without decreasing the silage quality of complete feed.

Keywords: buffalo, cabbage, complete feed, sillage characteristic, sugarcane waste

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Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Malahat Rezaee, Mahiran Basri, Raja Noor Zaliha Raja Abdul Rahman, Abu Bakar Salleh

Abstract:

Sodium diclofenac is one of the most commonly used drugs of nonsteroidal anti-inflammatory drugs (NSAIDs). It is especially effective in the controlling the severe conditions of inflammation and pain, musculoskeletal disorders, arthritis, and dysmenorrhea. Formulation as nanoemulsions is one of the nanoscience approaches that have been progressively considered in pharmaceutical science for transdermal delivery of drug. Nanoemulsions are a type of emulsion with particle sizes ranging from 20 nm to 200 nm. An emulsion is formed by the dispersion of one liquid, usually the oil phase in another immiscible liquid, water phase that is stabilized using surfactant. Palm kernel oil esters (PKOEs), in comparison to other oils; contain higher amounts of shorter chain esters, which suitable to be applied in micro and nanoemulsion systems as a carrier for actives, with excellent wetting behavior without the oily feeling. This research was aimed to study the effect of O/S ratio on stability and rheological behavior of sodium diclofenac loaded and unloaded palm kernel oil esters nanoemulsion systems. The effect of different O/S ratio of 0.25, 0.50, 0.75, 1.00 and 1.25 on stability of the drug-loaded and unloaded nanoemulsion formulations was evaluated by centrifugation, freeze-thaw cycle and storage stability tests. Lecithin and cremophor EL were used as surfactant. The stability of the prepared nanoemulsion formulations was assessed based on the change in zeta potential and droplet size as a function of time. Instability mechanisms including coalescence and Ostwald ripening for the nanoemulsion system were discussed. In comparison between drug-loaded and unloaded nanoemulsion formulations, drug-loaded formulations represented smaller particle size and higher stability. In addition, the O/S ratio of 0.5 was found to be the best ratio of oil and surfactant for production of a nanoemulsion with the highest stability. The effect of O/S ratio on rheological properties of drug-loaded and unloaded nanoemulsion systems was studied by plotting the flow curves of shear stress (τ) and viscosity (η) as a function of shear rate (γ). The data were fitted to the Power Law model. The results showed that all nanoemulsion formulations exhibited non-Newtonian flow behaviour by displaying shear thinning behaviour. Viscosity and yield stress were also evaluated. The nanoemulsion formulation with the O/S ratio of 0.5 represented higher viscosity and K values. In addition, the sodium diclofenac loaded formulations had more viscosity and higher yield stress than drug-unloaded formulations.

Keywords: nanoemulsions, palm kernel oil esters, sodium diclofenac, rheoligy, stability

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Authors: Simona Pileviciene

Abstract:

On 24th January 2014, Lithuania notified two primary cases of African swine fever (ASF) in wild boars. The animals were tested positive for ASF virus (ASFV) genome by real-time PCR at the National Reference Laboratory for ASF in Lithuania (NRL), results were confirmed by the European Union Reference Laboratory for African swine fever (CISA-INIA). Intensive wild and domestic animal monitoring program was started. During the period of 2014-2017 ASF was confirmed in two large commercial pig holding with the highest biosecurity. Pigs were killed and destroyed. Since 2014 ASF outbreak territory from east and south has expanded to the middle of Lithuania. Diagnosis by PCR is one of the highly recommended diagnostic methods by World Organization for Animal Health (OIE) for diagnosis of ASF. The aim of the present study was to compare singleplex real-time PCR assays to a duplex assay allowing the identification of ASF and internal control in a single PCR tube and to compare primers, that target the p72 gene (ASF 250 bp and ASF 75 bp) effectivity. Multiplex real-time PCR assays prove to be less time consuming and cost-efficient and therefore have a high potential to be applied in the routine analysis. It is important to have effective and fast method that allows virus detection at the beginning of disease for wild boar population and in outbreaks for domestic pigs. For experiments, we used reference samples (INIA, Spain), and positive samples from infected animals in Lithuania. Results show 100% sensitivity and specificity.

Keywords: African swine fewer, real-time PCR, wild boar, domestic pig

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Authors: Masood Roohi, Amir Taghavipour

Abstract:

This paper describes a strategy to develop an energy management system (EMS) for a charge-sustaining power-split hybrid electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit from the advantages of both parallel and series architecture. However, it gets relatively more complicated to manage power flow between the battery and the engine optimally. The applied strategy in this paper is based on nonlinear model predictive control approach. First of all, an appropriate control-oriented model which was accurate enough and simple was derived. Towards utilization of this controller in real-time, the problem was solved off-line for a vast area of reference signals and initial conditions and stored the computed manipulated variables inside look-up tables. Look-up tables take a little amount of memory. Also, the computational load dramatically decreased, because to find required manipulated variables the controller just needed a simple interpolation between tables.

Keywords: hybrid electric vehicles, energy management system, nonlinear model predictive control, real-time

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