Countless Applications Research Articles

1 kHz Remote Control of a Balancing Robot with Wi-Fi-in-the-Loop

Countless industrial applications can potentially benefit from the implementation of wireless control systems, leading to a widespread research effort to investigate new solutions in the field. Nevertheless, currently available wireless communication standards for industrial automation are not able to achieve high control frequencies. In particular, time–critical applications (e.g. industrial robotics and manipulation) require high sampling frequencies to be properly implemented. The higher throughput provided by IEEE 802.11 (Wi-Fi) can theoretically tame critical applications, although reliability is a key issue. In this work Wi-Fi is adopted to increase the achievable control rates up to 1 kHz, while reliability is guaranteed by mitigating communication flaws through model–based estimation techniques. The core of the proposed approach relies on a modified Kalman filter that exploits a buffer of incoming measures to account for delayed data packets. The proposed solution is validated through a hardware– in–the–loop experiment that features actual Wi-Fi hardware and a commercial embedded PC board. The obtained results give a preliminary, yet valuable, validation of the proposed approach testing the solution on relevant hardware.

Energy Efficient Heartbeat-Based MAC Protocol for WBAN Employing Body Coupled Communication

Wireless Body Area Networks (WBANs) are a fast-growing field fueled by the number of wearable devices developed for countless applications appearing on the market. To enable communication between a variety of those devices, the IEEE 802.15.6 standard was established. However, this standard has some intrinsic limitations in addressing the heterogeneity of the network nodes in terms of activity, data rates (from less than bit/s to multiple Mbit/s), energy availability, form factor, and location on, around or inside the body. To address these concerns, an alternative model is proposed that could serve as an extension of the IEEE 802.15.6 Standard. At its core is an adaptive and low-overhead synchronization scheme based on heartbeat sensing. This forms the base for a TDMA-based (Time Division Multiple Access) Media Access Control (MAC) protocol dedicated to multi-tier networks. While this effort focuses specifically on Capacitive Body-Coupled Communication (C-BCC), other physical layers can be easily incorporated as well. Based on these premises, this paper compares various random-access slot allocation approaches to accommodate the multiple data rates matching the system requirements, while incorporating a duty-cycling strategy anchored by heartbeat detection. This work proposes a novel, flexible, and robust solution, making use of heartbeat synchronization and addressing the corresponding challenges. It efficiently interconnects multiple device types over a wide range of data rates and targets a mesh of stars topology. At the cost of an increased communication latency, the proposed protocol outperforms the IEEE 802.15.4 MAC standard in terms of energy efficiency by a factor of at least 12x in a realistic scenario.

Coming to Cultura

Coming to Cultura Peyton Del Toro (bio) They Could Have Named Her Anything Stephanie Jimenez Little A www.amazon.com/They-Could-Have-Named-Anything-ebook/dp/B07HF3YVYV 300pages, Print, $10.99 Latinx writer Stephanie Jimenez has gifted us with her debut new adult novel, They Could Have Named Her Anything, a narrative that interrogates Latina girlhood in a way many have shied away from. The book centers around high school student Maria Rosario, her financially distressed father, Miguel, her rich, white friend, Rocky, and Rocky’s power-hungry father, Charlie. Maria is a Puerto Rican-Ecuadorian girl from Queens, but attends an Upper East Side private school on full scholarship, making her one of the few Latinas at her school. Feeling like an outsider in school while being critical of her family at home, Maria is desperate to be truly seen. She is a girl with big dreams, one of them being to be the first of her family to go to college. However, the gender- and class-based walls begin closing in on her, and similar to how her mother “showed Maria how to bury [her used pad] at the bottom of the trash can, Maria understood then that it was the men in the family from whom she was supposed to be hiding … And it wasn’t just her body — she soon learned that everything about her was wrong — her questions, her values, her dreams.” Miguel recently lost his job, and despite filling out countless applications for a new one, was having no luck. Maria knew her family was opposed to her dreams, and she also knew that it wasn’t entirely their choice to oppose — they needed her to work. “What a struggle it would be to be who she was when it came at the risk of someone else’s disappointment.” Jimenez has crafted a story that intricately engages with the way our goals and desires are often entangled with our insecurities, touching on issues of language, faith, consent, assimilation, mental health, shame, and money. Most importantly, Jimenez articulates the racialized nature of these issues, and how people of color are often preyed upon due to personal insecurities of the privileged. Unlike many novels that focus on the lives of teenagers, this book doesn’t avoid issues that are mythically “inappropriate” for the age of its audience. Maria befriends Rocky, a rich white girl that also attends her private school. While visiting Rocky’s place after school one day, Maria meets Charlie, a “charming” businessman and, when convenient, Rocky’s father. Charlie is nothing like Maria’s Latino boyfriend, Andres, who pressures her into having sex and disrespects the vulnerability involved with it. Maria sees that Charlie holds a level of privilege she could only dream of obtaining, and if she could just get him to “see” her, she must be worthy of opportunity. However, the point of view shifts many times throughout the novel, and when Charlie is the narrator, readers understand that what he “sees” and admires in Maria is what he represents to her: She “made him feel powerful, capable.” In his transactional nature, Charlie takes advantage of Maria while promising to pay for her college tuition and anything else that she might dream. In this brilliant novel, Jimenez takes Gloria Anzaldúa’s concept of mestiza consciousness to a tangible level, where young, aspiring-scholar Maria moves from memorizing Emerson to prove her intelligence to a privileged white man to reading poems from Latina anthologies to herself. Ultimately, Jimenez has expanded Sor Juana Inés de la Cruz’s poem “Hombres necios que acusaís” (1689) into a story about a young Latina, even including the quote: “Who was more to blame, even if both were in the wrong, / la que peca por la paga / o el que paga por pecar?” Maria’s borderlands are ones of not being a child but not quite an adult; of “entiendo pero no hablo” ; of wishing to be desired but wanting to consent; of being the kind of girl men fuck and the kind of girl men marry; of wanting to go to college but having familial responsibilities...

Electrochemically driven multi-material 3D-printing

Major efforts for the advancement of additive manufacturing are lately focused on the development of multi-material 3D-printing (mMat-3DP) methods which can enable the fabrication of complete devices in a single printing process combining materials with different properties (structural, functional, conductive, etc.). Printing conductive (metal and non-metal) materials with low-energy-consuming and economical methods is of particular interest since it would facilitate the production of electrodes, catalytic surfaces and electronic circuitry in general for countless applications. In order to contribute to the future vision of mMat-3DP, we wish to show here an economical method to selectively deposit different conductive materials (metal and conductive polymer) by means of electrochemical driving forces. A custom-made electrochemical liquid dispenser with embedded electrodes is used to electrodeposit selectively a metal (Cu), a conductive polymer (polyaniline), or a combination of the two, with both precursors present simultaneously and conveniently in the common electrolytic bath. Combining the 3D-patterning ability of a desktop 3D-printer with a concurrent control of the electrochemical process, selective deposition is demonstrated over a conductive graphite foil used as the cathode. Printing and electrochemical parameters have been optimized using scanning electron microscopy and energy dispersive X-ray spectroscopy to characterize the printed structures. The electrochemical 3D-printing method, being inherently low-cost, scalable and compatible with electrode fabrication methods shall find a broad scope of applications.

Liquid metals and their hybrids as stimulus–responsive smart materials

The re-emergence of room temperature liquid metals presents an exciting paradigm for an ideal combination of metallic and fluidic properties. The unique fluid metal features of non-hazardous Ga-based liquid metals, including high surface energy, low viscosity, electrical and thermal conductivity, a wide temperature range of the liquid state, and desirable chemical activity for many applications, have led to remarkable possibilities for harnessing their properties and achieving unique functionalities. The realization of their stimulus-responsivity and multi-functionality make Ga-based liquid metals an attractive family of ‘smart materials’ that could act as the basis of countless applications in new frontiers, covering a wide range from materials science and engineering to medicine. Constructing hybrids of Ga-based liquid metals with other functional materials can further extend the field-responsive capacity of liquid metals to incredible levels. An increasing number of reports have revealed Ga-based liquid metals and their hybrids as remarkable soft smart-response materials. Nevertheless, the mechanisms underlying their stimulus–response activities, their interactions with other functional entities, and efficient tuning in their intimate integration, still require further exploration. Considering the applications of Ga-based liquid metals and their hybrids, this review focuses on their field-responsive physical and chemical properties. The recent field-responsive reports are comprehensively presented. The analysis of their responsive properties and the types of field applied in each case are discussed, so that a critical outlook on this field can be established.

Frugal Energy Harvesting: Microwave Energy Radiated Into the Environment From Wireless Networks

In today's interconnected world, small sensor nodes are becoming increasingly important. There are countless applications using devices of minimal size in biomedical equipment, either for remote diagnosis or as wearable sensors in clothing. Equipping all these devices with batteries is logistically and economically difficult. Instead, the most sensible approach is for the sensor nodes to collect their energy from the environment. There are various possibilities, such as solar energy, kinetic energy from movements, and thermal energy. However, in this article, we will look at another possibility: microwave energy that is radiated into the environment from wireless networks. To use this microwave energy, we need a wireless energy harvester (WEH). There are numerous approaches to design such a WEH depending on the use case; for example, a WEH design could be flexible [1] or broadband [2]. Information on passive RF identification systems can be found, for example, in [3] and [4]. For such systems, energy harvesting from incident radio waves is a key aspect. In this article, we explain our harvester design presented for the June 2019 IEEE Microwave Theory and Techniques Society (MTT-S) International Microwave Symposium (IMS) Student Design Competition (SDC), Wearable/Frugal Microwave Energy Harvesting Design; our design was driven by being both small and economical.

Studying Avogadro’s Law with Arduino

Asensor of the type BMP180, together with Arduino, is used to easily measure at­mospheric pressure and temperature at schools. The open source Arduino microcontroller board presents countless teaching applications in science and engineering projects. It allows students to implant their creativity into a friendly environment and take ownership of their scientific explorations. Schools take advantage by incorporating Arduino into the learning and teaching process because of the inexpensive cost of the experiments and the educational experiences being provided to students. The potential of the Arduino platform in physics education is well known, and most students are usually excited by physics labs based on it.

Dynamic Graph CNN for Learning on Point Clouds

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.

Synthetic Data: How AI Is Transitioning From Data Consumer to Data Producer... and Why That's Important

Artificial intelligence (AI) empowers countless applications by consuming data and spotting patterns; however, this paradigm is being flipped. AI is now being used to produce data from patterns, and the resulting "synthetic data" are rapidly enabling a vast range of groundbreaking solutions.

Experimental Tools and Techniques for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have earned an upsurging inquisitiveness of the research community over the past decade and for the upcoming era, due to which we can realize its countless applications in various fields including medical, military, environment and academia. Wireless sensor networks composed of thousands or sometimes millions of sensor nodes which are usually operated by batteries that have limited energy, data processing, storage and transmission capacity. Moreover, these sensor nodes are densely deployed in human inaccessible regions and connected through error prone wireless links to offer a long coverage, therefore It is very difficult rather impractical to analyze the behavior of WSNs with real experiments. Most researches in this particular field are carried out by using simulation tools in several educational associations and research hubs. For this reason study of existing experimental tools for analyzing the behavior of WSNs becomes essential. This paper is intended to present a review on experimental tools and techniques available in the literature for WSNs, including simulators, emulators and test beds. These tools are highly useful for analyzing the operational behavior of wireless sensor networks. This paper also provides a roadmap for selecting appropriate experimental tools for various kinds of WSNs and its applications by taking into account their capabilities, relative ease of use and accuracy.

4D- quantitative structure–activity relationship modeling: making a comeback

ABSTRACT Introduction: Predictive Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.

Effect of beading parameters on cross-linked chitosan adsorptive properties

Among countless applications, chitosan has the potential to be an effective and cheap adsorbent for pollutant removal in (waste) water. In form of beads, it also exhibits good mechanical and fluid dynamic properties that are suitable for utilization in large-scale adsorption processes (e.g. as column packing). The present study investigates the beading process to prepare chitosan and cross-linked chitosan beads by phase inversion method. Beading parameters (i.e. chitosan amount, acid concentration, non-solvent agent concentration, and temperature) are optimized to obtain an appropriate adsorbent structure to uptake large organic molecules (reactive red 2, a dye, is utilized as model pollutant). Chitosan beads present remarkably enhanced adsorption capacity, mainly thanks to increased specific surface area and porosity. The addition of a cross-linker (diepoxyoctane) further improves pollutant removal. The adsorption process is well-fitted by the Freundlich model, suggesting a multilayer adsorption. The kinetic study also substantiates the complexity of the adsorption mechanism on bead surface, which, however, is satisfactorily predicted by a simple fractal kinetic model. Finally, it is proved that chitosan and cross-linked chitosan beads can be recycled (by washing with NaOH solution) at least 5 times before sensible performance loss.

Experimental methods in chemical engineering: Artificial neural networks–ANNs

Artificial neural networks (ANNs) are one of the most powerful and versatile tools provided by artificial intelligence and they have now been exploited by chemical engineers for several decades in countless applications. ANNs are computational tools providing a minimalistic mathematical model of neural functions. Coupled with raw data and a learning algorithm, they can be applied to tasks such as modelling, classification, and prediction. Recently, their popularity has grown remarkably and they now constitute one of the most relevant research areas within the fields of artificial intelligence and machine learning. ANNs are large collections of simple classifiers called neurons. Chemical engineers apply them to model complex relationships, predict reactor performance, and to automate process controllers. ANNs can leverage their ability to learn and exploit large data sets, but they can also get stuck in local minima or overfit and are difficult to reverse engineer. In 2016 and 2017, ANNs were cited in 13 245 Web of Science (WoS) articles, 538 of which were in chemical engineering; the top WoS categories were electrical & electronic engineering (1615 occurrences) artificial intelligence (1253), and energy & fuels (980). The top 4 journals mentioning ANNs were Neural Computing & Applications (117), Neurocomputing (84), Energies (76), and Renewable & Sustainable Energy Reviews (76). In the near future, as larger data sets become available (and arduous to analyze), chemical engineers will be able to apply and leverage more sophisticated ANN architectures.

Recommendation and Classification Systems: A Systematic Mapping Study

Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of increasing their profits or offering a more specialized service to their customers. Moreover, there are countless applications in which classification algorithms are used, seeking to find patterns that are difficult for people to detect or whose detection cost is very high. Sometimes, it is necessary to use a mixture of both algorithms to give an optimal solution to a problem. This is the case of the ADAGIO, a R&D project that combines machine learning (ML) strategies from heterogeneous data sources to generate valuable knowledge based on the available open data. In order to support the ADAGIO project requirements, the main objective of this paper is to provide a clear vision of the existing classification and recommendation ML systems to help researchers and practitioners to choose the best option. To achieve this goal, this work presents a systematic review applied in two contexts: scientific and industrial. More than a thousand papers have been analyzed resulting in 80 primary studies. Conclusions show that the combination of these two algorithms (classification and recommendation) is not very used in practice. In fact, the validation presented for both cases is very scarce in the industrial environment. From the point of view of software development life cycle, this review also shows that the work being done in the ML (for classification and recommendation) research and industrial environment is far from earlier stages such as business requirements and analysis. This makes it very difficult to find efficient and effective solutions that support real business needs from an early stage. It is therefore that the article suggests the development of new ML research lines to facilitate its application in the different domains.

Curvature analysis of single layer graphene on the basis of extreme low-frequency Raman spectroscopy

Single layer graphene (SLG) sheets offer exciting optical and electronic properties, as well as excellent mechanical performance, which are desirable for countless potential applications in ultrathin optical, electronic, and mechanical devices. Typically, the mechanical properties of SLG are extrapolated from few layer graphene (FLG) systems in most existing experimental studies, despite the fact that the environmental mechanical response of SLG is quite different from FLG. Raman spectroscopy is one of the most versatile and nondestructive experimental techniques to probe graphene samples. Here, we provide direct experimental evidence for the vibrational behavior of SLG and its response to high pressure conditions (0–10 GPa) via Raman spectroscopy including the extreme low-frequency Raman region (5–250 cm–1). Artificial introduction of the curvature of the SLG sheets causes van Hove singularities within the range of Fermi energies (EF). The radius of curvature ρ can be predicted via a comparison of the shear mode and the breathing mode of SLG with the squash mode and the radial breathing mode of single wall carbon nanotubes (SWNTs). Furthermore, an additional polarization analysis further confirms similar low frequency modes of SLG and SWNTs under pressure. This direct investigation of SLG mechanical properties improves the quality of the available mechanical data, which is required for the design of new graphene-based nanocomposites and the development of electronic or mechatronic devices.

CRISPR/Cas9-Based Antiviral Strategy: Current Status and the Potential Challenge.

From its unexpected discovery as a bacterial adaptive immune system to its countless applications as one of the most versatile gene-editing tools, the CRISPR/Cas9 system has revolutionized every field of life science. Virology is no exception to this ever-growing list of CRISPR/Cas9-based applications. Direct manipulation of a virus genome by CRISPR/Cas9 has enabled a systematic study of cis-elements and trans-elements encoded in a virus genome. In addition, this virus genome-specific mutagenesis by CRISPR/Cas9 was further funneled into the development of a novel class of antiviral therapy targeting many incurable chronic viral infections. In this review, a general concept on the CRISPR/Cas9-based antiviral strategy will be described first. To understand the current status of the CRISPR/Cas9-based antiviral approach, a series of recently published antiviral studies involving CRISPR/Cas9-mediated control of several clinically-relevant viruses including human immunodeficiency virus, hepatitis B virus, herpesviruses, human papillomavirus, and other viruses will be presented. Lastly, the potential challenge and future prospect for successful clinical translation of this CRISPR/Cas9-based antiviral method will be discussed.

Yb3+/Er3+−codoped GeO2–PbO–PbF2 glass ceramics for ratiometric upconversion temperature sensing based on thermally and non-thermally coupled levels

The contactless real-time temperature sensing technique with high temporal and spatial resolution is in high demand for the countless applications. Here, the Yb3+/Er3+−codoped GeO2–PbO–PbF2 glass ceramics synthesized via the melt-quenching technique has been presented as an optical thermometer. The thermal sensing was designed based on the temperature dependent fluorescence intensity ratios of thermally (2H11/2 and 4S3/2) and non-thermally (2H11/2 and 4F9/2) coupled Er3+ levels. The ratiometric techniques provide the thermal sensing within the temperature range of 300–466 K. The absolute and relative thermal sensitivities as well as the temperature resolution were calculated and compared with other Yb3+/Er3+-doped materials. The temperature of the microelectronic component on the printed circuit board was defined using the optical thermometry as a proof of concept revealing Yb3+/Er3+−codoped GeO2–PbO–PbF2 phosphor to be a promising candidate for precise non-contact thermal sensor.

Universal evaporation dynamics of ordered arrays of sessile droplets

Manipulation of an array of surface droplets organised in an ordered structure turns out to be of immense consequence in a wide variety of applications ranging from photonics, near field imaging and inkjet printing on the one hand to bio-molecular analysis and DNA sequencing on the other. While evaporation of a single isolated sessile droplet has been well studied, the collective evaporative dynamics of an ordered array of droplets on a solid substrate remains elusive. Physically, the closed region between the centre and side droplets in the ordered array reduces the mobility of the diffusing vapour, resulting in its accumulation along with enhanced local concentration and a consequent increment in the lifetime of the centre droplet. Here, we present a theoretical model to account for evaporation lifetime scaling in closely placed ordered linear droplet arrays. In addition, the present theory predicts the limiting cases of droplet interaction; namely, critical droplet separation for which interfacial interaction ceases to exist and minimum possible droplet separation (droplets on the verge of coalescence) for which the droplet system achieves maximum lifetime scaling. Further experimental evidence demonstrates the applicability of the present scaling theory to extended dimensions of the droplet array, generalising our physical conjecture. It is also worth noting that the theoretical time scale is applicable across a wide variety of drop–substrate combinations and initial droplet volumes. We also highlight that the scaling law proposed here can be extended seamlessly to other forms of confinement such as an evaporating droplet inside a mini-channel, as encountered in countless applications ranging from biomedical engineering to surface patterning.

Automated Face Detection, Recognition and Gender Estimation Applied to Person Identification

Face detection, recognition and gender estimation are one of the most significant research areas in computer vision, not only because of the challenging nature of faces as an object but also due to the countless applications that require the application of face detection, tracking and recognition. Although many significant types of research on face detection, recognition and gender estimation problems have done in the last few years separately, there is no particular research on face detection, recognition and gender estimation together from a real-time video for person identification. So, we feel that these types of significant research are still needed to work. The main contributions of our paper are divided into three parts, namely face detection, recognition and gender estimation for person identification. In our research work, we use Local Binary pattern Histogram (LBPH) method and Convolution Neural Network (CNN) to extract the facial features of face images whose computational complexity is very low. By calculating the Local Binary Patterns Histogram (LBPH) neighborhood pixels and Convolution levels, we extract effective facial feature to realize face recognition and gender estimation. We show the experimental results using these methods to recognize face and gender for person identification. CNN increase the calculating speed of testing real-time video and also improve the recognition rate. By using LPBH, we get 63% accuracy on average where CNN gives 99.88% training accuracy for face recognition-1 and 96.88% accurate for gender estimation-1 and 100% training accuracy for face recognition-2 and 93.38% training accuracy for gender estimation-2. However, Convolution Neural Networks (CNN) learns fast and predict efficiently.

Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques

The recent years have witnessed rapid strides in the use of cloud computing and its countless applications. A cloud can contain massive volumes of multimedia data in the form of images, video and audio. Cloud computing platforms confront challenges in terms of data confidentiality, message integrity, user authentication and compression. Multimedia data needs plenty of storage capacity. Consequently, there is a need for multimedia data compression to reduce data size. Compression techniques are quite reliable, offering benefits to organizations dealing with metasized data in the cloud. Compressing large quanta of data leads to superior utilization of cloud storage. Compression techniques can compress data used for storage and transmission, yet compression alone is inadequate because multimedia data shared should, of necessity, be secure. Therefore, both multimedia compression and security are mandatory in the cloud. The chief goal of this paper is to propose a new framework, comprising multiscale transforms, public key cryptography and appropriate encoding techniques, that performs joint medical image compression and image encryption in the cloud. Multiscale transforms play a lead role in image compression, and the ones discussed in this paper include wavelet, bandelet, curvelet, ridgelet and contourlet transforms. Wavelet transforms offer robust localization both in terms of time and frequency domains. Bandelet transforms offer natural images geometric regularity to help improve the efficiency of representation. Curvelet transforms handle curve discontinuities well, with ridgelet transforms being the core idea behind curvelets. Contourlet transforms capture smooth contours and edges at any orientation. The Rivest-Shamir-Adleman (RSA) algorithm is used to encrypt images to provide maximum security when they are being transferred. Encoding techniques involved in this paper comprise the Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Performance parameters such as peak signal to noise ratio (PSNR), mean square error (MSE), image quality index and structural similarity index (SSIM) are used for evaluation. It is justified that the proposed framework compresses images securely in the cloud.