Requirement Validation for Embedded Systems in Automotive Industry Through Modeling

Requirement validation contributes significantly toward the success of software projects. Validating requirements is also essential to ensure the correctness of embedded systems in the auto industry. The auto industry emphasizes a lot on the verification of car designs and shapes. Invalid or erroneous requirements lead to inappropriate designs and degraded product quality. Considering the required expertise and time for requirement validation, significant attention is not devoted to verification and validation of requirements in the industry. Currently, the failure ratio of software projects is significantly higher and the key reason for that appears to be the inappropriate and invalidated requirements at the early stages in the projects. To that end, we propose a model-based approach that uses the existing V&V model. Through virtual prototyping, the proposed approach eliminates the need to validate the requirements after each stage of the project. Consequently, the model is validated after the design phase and the errors in requirements are detected at the earliest stage. In this research, we performed two different case studies for requirement validation in the auto industry by using a modeling-based approach and formal technique using Petri nets. A benefit of the proposed modeling-based approach is that the projects in the auto industry domain can be completed in less time due to effective requirements validation. Moreover, the modeling-based approach minimizes the development time, cost and increases productivity because the majority of the code is automatically generated using the approach

Impact of Training and Supervisor Support on Organizational Commitment with mediating role of Job Satisfaction

Training and Supervisor support shows a very important role in growing organizational commitment. The purpose of this research is to give a hypothetical justification of: a) impact of supervisor support on organizational commitment, mediating part of job satisfaction for the positive connection between supervisor support and organizational commitment, b) direct impact of training on organizational commitment and job satisfaction for the positive association among training, organizational commitment too. Self-administered questionnaires were distributed. The population which is targeted for this study was employees of the textile firms in Lahore. Data were collected from 320 employees. The study uses the SPSS and Structural Equation Modelling to test the hypotheses among 320 respondents. The results indicate that there is a significant relationship between training and organizational commitment, supervisor support and organizational commitment, training and job satisfaction. Supervisor support is significantly related with job satisfaction and there is a significant relationship between job satisfaction and organizational commitment

HateClassify: A Service Framework for Hate Speech Identication on Social Media

It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multi-class problem. In this work, we present the hate identification on the social media as a multi-label problem. To this end, we propose a CNN-based service framework called "HateClassify" for labeling the social media contents as the hate speech, offensive, or non-offensive. Results demonstrate that the multi-class classification accuracy for the CNN based approaches particularly Sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multi-label classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multi-label classification instead of multi-class classification, hate speech detection is increased up to 20%

Toxicity and Repellency of Plant Extract and Termiticide against Fungus Growing Subterranean Termites (Blattodea: Termitidae)

Different methods such as physical, biological and chemical are used to manage soil fungus increasing termites. Synthetic insecticide plays a vital part in the management of termites. The pesticide used in big quantities causes phytotoxicity, mammalian toxicity and resistance to pesticides in target pests and insect outbreaks. Intensive pesticides exert chronic effects on living organisms with annoyance for beneficial insects. It also accompanied with environmental hazards and developed resistance. Plant leaves extracts provide a distinct variety of biochemical compounds with diverse prospective uses. Resistance development requires the discovery of fresh biological compounds with a wide spectrum of action. Plant leaves extract and Chlorfenapyr solution in methanol and water with various concentrations (15 %, 10 %, 5 % and 0 %) were applied to the soil against termites to determine mortality and repellency. Posttreatment data was obtained and evaluated through statistical analysis. The result revealed that the extract of Conocarpus lancifolius with the solution of methanol and solution of water exhibited higher mortality of subterranean termites, whereas the solution of methanol had higher repellency and mortality than water solution of botanical extract. Water and methanol solution of insecticide chlorfenapyr used against the subterranean termites, both are found to be efficacious against termites, while insecticide with the solution of methanol revealed 100% mortality. Nonetheless, plant extract of C. lancifolius with water and methanol solution and chlorfenapyr with methanol solution can be applied as new biological control tools against subterranean termites

An Optimal Ride Sharing Recommendation Framework for Carpooling Services

Carpooling services allow drivers to share rides with other passengers. This helps in reducing the passengers’ fares and time, as well as traffic congestion and increases the income for drivers. In recent years, several carpooling based recommendation systems have been proposed. However, most of the existing systems do no effectively balance the conflicting objectives of drivers and passengers. We propose a Highest Aggregated Score Vehicular Recommendation (HASVR) framework that recommends a vehicle with highest aggregated score to the requesting passenger. The aggregated score is based on parameters, namely: (a) average time delay, (b) vehicle’s capacity, (c) fare reduction, (d) driving distance, and (e) profit increment. We propose a heuristic that balances the incentives of both drivers and passengers keeping in consideration their constraints and the real-time traffic conditions. We evaluated HASVR with a real-world dataset that contains GPS trace data of 61,136 taxicabs. Evaluation results confirm the effectiveness of HASVR compared to existing scheme in reducing the total mileage used to deliver all passengers, reducing the passengers’ fare, increasing the profit of drivers, and increasing the percentage of satisfied ride requests

An efficient technique for retinal vessel segmentation and denoising using modified isodata and CLAHE

Retinal damage caused due to complications of diabetes is known as a Diabetic Retinopathy (DR). In this case, the vision is obscured due to damage of tiny retinal blood vessels. These tiny blood vessels may cause leakage that affect the vision and can lead to complete blindness. Identification of these new retinal vessels and their structure is an essential for analysis of DR. Automatic blood vessel segmentation plays a significant role to assist subsequent automatic methodologies that aid to such analysis. In literature, most authors have used computationally-hungry strong preprocessing steps followed by a simple thresholding and postprocessing steps. This paper proposed an arrangement of simple preprocessing steps that consist of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement and a difference image of green channel from its Gaussian blur filtered image to remove local noise or geometrical objects. The proposed Modified Iterative Self Organizing Data Analysis Technique (MISODATA) has been used for segmentation of vessel and non-vessel pixels based on global and local thresholding. Finally, postprocessing steps have been applied using region properties (area, eccentricity) to eliminate the unwanted regions/segments, nonvessel pixels, and noise. A novel postprocessing steps are used to reject misclassified foreground pixels. The strategy has been tested on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases. The average accuracy rates of 0.952 and 0.957 with average sensitivity rates 0.780 and 0.745 along with average specificity rates of 0.972 and 0.974 were obtained on DRIVE and STARE datasets, respectively. The performance of the proposed technique has been assessed comprehensively. The acquired accuracy, robustness, low complexity, and high efficiency make the method an efficient tool for an automatic retinal image analysis. The proposed technique perform well as compared to the existing strategies on the online available databases in term of accuracy, sensitivity, specificity, false positive rate, true positive rate, and area under receiver operating characteristic (ROC) curve

Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices

The prevalent use of the Internet of Things (IoT) devices over the Sea, such as, on oil and gas platforms, cargo, and cruise ships, requires high-speed connectivity of these devices. Although satellite based backhaul links provide vast coverage, but they are inherently constrained by low data rates and expensive bandwidth. If a signal propagated over the sea is trapped between the sea surface and the Evaporation Duct (ED) layer, it can propagate beyond the horizon, achieving long-range backhaul connectivity with minimal attenuation. This paper presents experimental measurements and simulations conducted in the Industrial, Scientific, and Medical (ISM) Band Wi-Fi frequencies, such as 5.8 GHz to provide hassle-free offshore wireless backhaul connectivity for IoT devices over the South China Sea in the Malaysian region. Real-time experimental measurements are recorded for 10 km to 80 km path lengths to determine average path loss values. The fade margin calculation for ED must accommodate additional slow fading on top of average path loss with respect to time and climate-induced ED height variations to ensure reliable communication links for IoT devices. Experimental results confirm that 99% link availability of is achievable with minimum 50 Mbps data rate and up to 60 km distance over the Sea to connect offshore IoT devices

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC

Utility of the CHA2DS2-VASc score for predicting ischaemic stroke in patients with or without atrial fibrillation: a systematic review and meta-analysis

AIMS: Anticoagulants are the mainstay treatment for stroke prevention in patients with non-valvular atrial fibrillation (NVAF), and the CHA2DS2-VASc score is widely used to guide anticoagulation therapy in this cohort. However, utility of CHA2DS2-VASc in NVAF patients is debated, primarily because it is a vascular scoring system, which does not incorporate atrial fibrillation related parameters. Therefore, we conducted a meta-analysis to estimate the discrimination ability of CHA2DS2-VASc in predicting ischaemic stroke overall, and in subgroups of patients with or without NVAF. METHODS AND RESULTS: PubMed and Embase databases were searched till June 2020 for published articles that assessed the discrimination ability of CHA2DS2-VASc, as measured by C-statistics, during mid-term (2-5 years) and long-term (\u3e5 years) follow-up. Summary estimates were reported as random effects C-statistics with 95% confidence intervals (CIs). Seventeen articles were included in the analysis. Nine studies (n = 453 747 patients) reported the discrimination ability of CHA2DS2-VASc in NVAF patients, and 10 studies (n = 138 262 patients) in patients without NVAF. During mid-term follow-up, CHA2DS2-VASc predicted stroke with modest discrimination in the overall cohort [0.67 (0.65-0.69)], with similar discrimination ability in patients with NVAF [0.65 (0.63-0.68)] and in those without NVAF [0.69 (0.68-0.71)] (P-interaction = 0.08). Similarly, at long-term follow-up, CHA2DS2-VASc had modest discrimination [0.66 (0.63-0.69)], which was consistent among patients with NVAF [0.63 (0.54-0.71)] and those without NVAF [0.67 (0.64-0.70)] (P-interaction = 0.39). CONCLUSION: This meta-analysis suggests that the discrimination power of the CHA2DS2-VASc score in predicting ischaemic stroke is modest, and is similar in the presence or absence of NVAF. More accurate stroke prediction models are thus needed for the NVAF population

Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter