The project aimed to develop and implement an efficient web server in the C++ programming language. A highly concurrent network server was achieved using system calls such as polls and a limited ...number of threads. The server has built-in support for a new scripting language called Ckript. It is an original project that exposes most of the server’s functionality and is the primary way of developing back-end web applications. Ckript is an interpreted language with a strong typing system, garbage collection, semi-manual memory management, first-class functions, explicit variable references, support for certain object-oriented patterns and many others. In the article the syntax of the language but also the environment architecture has been explained. Finally, the testing procedure has been described with the results’ presentation and discussion at each step.
A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, ...and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system.
Abstract
Due to the high cost of human labor for maintenance, garbages in rivers often do not receive immediate cleaning, resulting in severe water pollution. Those floating garbages simultaneously ...strip the aesthetic of rivers and threaten the local marine ecosystem. Thence, we developed an unmanned vessel based on Arduino’s platform, capable of auto-collecting garbage. The vessel includes two modes: the human operational mode allows individuals to manipulate the vessel and to collect garbages through Bluetooth connection; the auto-collecting mode is based on OpenMV’s video processing function, enabling vessels to detect garbages nearby and transport the garbages into the vessel’s interior through a rotating caterpillar track. At the end of this project, the vessel remains large rooms for improvements, such as increasing the efficiency and effectiveness of garbage collection and garbage detection. The vessel is suitable in small size rivers or ponds were artificial cleaning faces obstacles.
Waste collection and management is a subject undergoing extensive study, and solutions are being proposed meticulously. Thanks to an exponential rise in population, there is an increased production ...of waste, and also a significant amount of litter consisting of plastic, paper, and other such products carelessly thrown about and scattered in public. Thus, the need for a more robust waste management strategy is essential. Presently, waste management techniques either lack efficiency, or incur high costs. Several Governmental as well as Non-Governmental Organizations have made efforts to clean public spaces. Collection of the unorganized and scattered garbage is the preliminary and most vital step of waste management, following proper segregation and disposal. This paper proposes, explains, and implements an original concept of making a modular, scalable and cost effective system for garbage collection. Making an efficient use of Internet of Things to maintain a constant connection between a central server and a network of garbage processing and collecting, independent, autonomous robots, we rely upon such a system to produce accurate results, as well as considerably reduce the cost, hence providing a feasible solution to minimize human effort and costs during waste collection. It provides a gateway towards implementing garbage collecting robots in smart cities. Rather than describing the design of a single robot, we propose an entire system of robots interconnected in a network, to optimize time, energy and overall speed. There is always a trade-off between accuracy, efficiency and cost of garbage collection, especially when robots get into the picture. Our purpose is to find the perfect balance between these factors.
This article points out an important threat that application-level Garbage Collection (GC) creates to the use of non-volatile memory (NVM). Data movements incurred by GC may invalidate the pointers ...to objects on NVM and, hence, harm the reusability of persistent data across executions. The article proposes the concept of movement-oblivious addressing (MOA), and develops and compares three novel solutions to materialize the concept for solving the addressability problem. It evaluates the designs on five benchmarks and a real-world application. The results demonstrate the promise of the proposed solutions, especially hardware-supported Multi-Level GPointer, in addressing the problem in a space- and time-efficient manner.
Population growth and industrialization lead to a proportionate increase in cities' daily waste generation rates. Communities in developing cities often turn to waste disposal methods that have ...proven destructive to human health and the environment. Further, the disposal of waste is not treated and utilized for waste-to-energy (WtE)-based energy generation. To overcome this situation, many researchers proposed various solutions. However, the optimal utilization of this waste for power generation still needs to be solved. The proposed work discusses a self-powered garbage management system using a Convolution Neural Network and IOT for households in smart cities. The proposed system collects household wastes and segregates them into organic and inorganic wastes using a Convolutional Neural Network (CNN). The inorganic waste is sent to the recycling bin, and the organic waste is used for power generation. The residue of the organic waste after power generation is utilized as fertilizer for plants. The proposed system comprises five modules: a garbage collector, a segregation unit, a power generator unit, an inorganic waste collection bin with IoT-enabled sensors, and an electronic control unit. The garbage collector unit collects household waste. The CNN-based waste classifier in the segregator unit separates the waste into organic and inorganic, and the organic waste is sent to the power generation unit. The waste is grinded using a combustion unit in the power generator, producing biogas for electric power generation.
The system is fully automatic, and a Raspberry Pi controller controls the complete process with the help of sensors and various motors. The system monitors the inorganic waste collection bin level using sensors. It sends a notification to the municipality's Garbage Collection Van operator using the IoT module once the bin is full and can be sent for recycling. The accuracy of the proposed CNN for waste segregation is 98%. While comparing with other pre-trained CNN models, such as InceptionV3 and Inception ResNet, the proposed method produces satisfactory results with 14% and 12% accuracy gains, respectively. The segregated and decomposed 50 kg of organic waste can produce 6 m3 of biogas which in turn can produce 114 MJ of electric energy, which can be utilized for street lights and also for the proposed smart self-power generating garbage management system to function. The proposed system is highly adaptable in smart cities for household municipal waste management with minimum routine monitoring and operational time requirements. Further, the system generates only a fraction of the required energy.
As the memory capacity of computational systems increases, the in-memory data management of Big Data processing frameworks becomes more crucial for performance. This paper analyzes and improves the ...memory efficiency of Flame-MR, a framework that accelerates Hadoop applications, providing valuable insight into the impact of memory management on performance. By optimizing memory allocation, the garbage collection overheads and execution times have been reduced by up to 85% and 44%, respectively, on a multi-core cluster. Moreover, different data buffer implementations are evaluated, showing that off-heap buffers achieve better results overall. Memory resources are also leveraged by caching intermediate results, improving iterative applications by up to 26%. The memory-enhanced version of Flame-MR has been compared with Hadoop and Spark on the Amazon EC2 cloud platform. The experimental results have shown significant performance benefits reducing Hadoop execution times by up to 65%, while providing very competitive results compared to Spark.
•Analysis and improvement of the memory efficiency of the MapReduce framework Flame-MR.•Optimization of memory allocations reduces garbage collection overheads by up to 85%.•Evaluation shows that off-heap buffers perform better for I/O-bound workloads.•Iterative applications are improved by up to 26% by caching intermediate results.•Comparison with Hadoop shows an average improvement of 48% with large memory spaces.
Water resources are an integral part of human life and it’s essential for the survival of life on this planet. However, in recent years the plastic and waste pollution in water bodies is increasing ...at an alarming rate due to which the aquatic ecosystem is disturbed, the quality of water is affected and the sunlight is blocked. Moreover, in the coronavirus pandemic usage of disposable masks, gloves, and other biomedical waste has added to the already existing pollution of the water bodies. Ineffective management of biomedical waste leads to public health risks and poses an even greater risk to the environment. Waste management is the most emerging challenge faced by mankind that can even threaten our very existence to the root. In this paper, we have proposed a design a product that can efficiently cope up with the present problem of improper waste disposal. The autonomous water cleaning robot collects waste and other floating garbage on the water body. The design implements a robust mechanical frame with sensor fusion and computer vision to achieve autonomous cleaning. The robot is provided with two energy sources, one battery and the other is solar power, a conveyor belt mechanism is further used to clean the water body. Providing a cost-effective solution, reducing man power and minimizing the time required for cleaning of the water bodies are the main objectives of this project.
Nowadays, extremely large amounts of structured and unstructured types of data are stored in public, private, and hybrid cloud storage using object storage systems. Among these, storing multimedia ...data such as image, video, and audio pose unique challenges and long-term effects on object storage Sustainability. Three such challenges are smoother and more efficient video streaming, middleware placement for media processing, and lastly, management of orphan garbage data. In order to tackle these challenges, this paper presents a generalized architecture for smooth and efficient management as well as retrieval of multimedia data in cloud systems. To do so, first, we propose a new middleware package in the object server for supporting smooth video streaming and on-demand playable video segments. Here, we demonstrate that video segment download time improves by up to 30% when segmentation is done in the object server rather than in the proxy server. After, we focus on how to find orphan garbage data on media cloud storage and to what extent they can hamper data retrieval. Specifically, we present a generalized architecture named 'RemOrphan' for detecting the orphan garbage data using OpenStack Swift hash Ring and scripts. We deploy a private media cloud SPMS and find that around 35% data can be orphan garbage data. Due to the huge amount of orphan data, rsync replication needs higher time and more network overhead which hampers the system's sustainability. We lower around 25% sync delay and 30% network overhead after deploying a deletion daemon to remove the orphan garbage data.
Java applications are diverse, depending by use case, exist application that use small amount of memory till application that use huge amount, tens or hundreds of gigabits. Java Virtual Machine is ...designed to automatically manage memory for applications. Even in this case due diversity of hardware, software that coexist on the same system and applications itself, these automatic decision need to be accompanied by developer or system administrator to triage optimal memory use. After developer big role to write optimum code from memory allocation perspective , optimizing memory use at Java Virtual Machine and application level become in last year's one of the most important task. This is explained in special due increased demand in applications scalability.
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