Searchable encryption (SE) is an interesting tool that enables clients to outsource their encrypted data into external cloud servers with unlimited storage and computing power and gives them the ability to search their data without decryption. The current solutions of SE support single-
The autonomous navigation of robots is an important area of research. It can intelligently navigate itself from source to target within an environment without human interaction. Recently, algorithms and techniques have been made and developed to improve the performance of robots. It’s more effective and has high precision tasks than before. This work proposed to solve a maze using a Flood fill algorithm based on real time camera monitoring the movement on its environment. Live video streaming sends an obtained data to be processed by the server. The server sends back the information to the robot via wireless radio. The robot works as a client device moves from point to point depends on server information. Using camera in this work allows voiding great time that needs it to indicate the route by the robot.
Multidimensional Online analytical processing (MOLAP) technology is considered a good tool to produce meaningful and quality results by using a multidimensional cube. The term “multidimensional cube” is used to refer to the multiple layers of data that are used to show the result. This result is identified by high-level management to increase the Iraqi court work and to improve its quality. The Iraqi court needs an analytical report to make a strategic decision on case date, case type, case state, judge, criminal age, and criminal gender. Currently, MOLAP is known as the best and strongest technique because it provides rapid, dynamic, and multiple analyses of data; presents knowledge from different perspectives; comes up with data in time series intervals; and drills down into multiple levels of data layers to present different types of details. The SQL Server Reporting Service (SSRS) presents analysis reports based on the MOLAP cube. This paper focuses on designing and developing the analysis reports of the court data system of the Republic of Iraq by using SSRS with SQL Server Analysis Service to create the MOLAP cubes.
The problem of outlier detection is one of the most important issues in the field of analysis due to its applicability in several famous problem domains, including intrusion detection, security, banks, fraud detection, and discovery of criminal activities in electronic commerce. Anomaly detection comprises two main approaches: supervised and unsupervised approach. The supervised approach requires pre-defined information, which is defined as the type of outliers, and is difficult to be defined in some applications. Meanwhile, the second approach determines the outliers without human interaction. A review of the unsupervised approach, which shows the main advantages and the limitations considering the studies performed in the supervised approach, is introduced in this paper. This study indicated that the unsupervised approach suffers from determining local and global outlier objects simultaneously as the main problem related to algorithm parameterization. Moreover, most algorithms do not rank or identify the degree of being an outlier or normal objects and required different parameter settings by the research. Examples of such parameters are the radius of neighborhood, number of neighbors within the radius, and number of clusters. A comprehensive and structured overview of a large set of interesting outlier algorithms, which emphasized the outlier detection limitation in the unsupervised approach, can be used as a guideline for researchers who are interested in this field.
In this paper, a robust wavelet based watermarking scheme has been proposed for digital audio. A single bit is embedded in the approximation part of each frame. The watermark bits are embedded in two subsets of indexes randomly generated by using two keys for security purpose. The embedding process is done in adaptively fashion according to the mean of each approximation part. The detection of watermark does not depend on the original audio. To measure the robustness of the algorithm, different signal processing operations have been applied on the watermarked audio. Several experimental results have been conducted to illustrate the robustness and efficiency of the proposed watermarked audio scheme.
Searchable symmetric encryption (SSE) enables clients to outsource their encrypted documents into a remote server and allows them to search the outsourced data efficiently without violating the privacy of the documents and search queries. Dynamic SSE schemes (DSSE) include performing update queries, where documents can be added or removed at the expense of leaking more information to the server. Two important privacy notions are addressed in DSSE schemes: forward and backward privacy. The first one prevents associating the newly added documents with previously issued search queries. While the second one ensures that the deleted documents cannot be linked with subsequent search queries. Backward has three formal types of leakage ordered from strong to weak security: Type-I, Type-II, and Type-III. In this paper, we propose a new DSSE scheme that achieves Type-II backward and forward privacy by generating fresh keys for each search query and preventing the server from learning the underlying operation (del or add) included in update query. Our scheme improves I/O performance and search cost. We implement our scheme and compare its efficiency against the most efficient backward privacy DSSE schemes in the literature of the same leakage: MITRA and MITRA*. Results show that our scheme outperforms the previous schemes in terms of efficiency in dynamic environments. In our experiments, the server takes 699ms to search and return (100,000) results.
A model reference adaptive control of condenser and deaerator of steam power plant is presented. A fuzzy-neural identification is constructed as an integral part of the fuzzy-neural controller. Both forward and inverse identification is presented. In the controller implementation, the indirect controller with propagating the error through the fuzzy-neural identifier based on Back Propagating Through Time (BPTT) learning algorithm as well as inverse control structure are proposed. Simulation results are achieved using Multi Input-Multi output (MIMO) type of fuzzy-neural network. Robustness of the plant is detected by including several tests and observations.
Using a lower limb exoskeleton for rehabilitation (LLE) Lower limb exoskeleton rehabilitation robots (LER) are designed to assist patients with daily duties and help them regain their ability to walk. Even though a substantial portion of them is capable of doing both, they have not yet succeeded in conducting agile and intelligent joint movement between humans and machines, which is their ultimate goal. The typical LLE products, rapid prototyping, and cutting-edge techniques are covered in this review. Restoring a patient’s athletic prowess to its pr-accident level is the aim of rehabilitation treatment. The core of research on lower limb exoskeleton rehabilitation robots is the understanding of human gait. The performance of common prototypes might be used to match wearable robot shapes to human limbs. To imitate a normal stride, robot-assisted treatment needs to be able to control the movement of the robot at each joint and move the patient’s limb.
Every day, a tremendous amount of image data is generated as a result of recent advances in imaging and computing technology. Several content-based image retrieval (CBIR) approaches have been introduced for searching image collections. These methods, however, involve greater computing and storage resources. Cloud servers can address this issue by offering a large amount of computational power at a low cost. However, cloud servers are not completely trustworthy, and data owners are concerned about the privacy of their personal information. In this research, we propose and implement a secure CBIR (SCBIR) strategy for searching and retrieving cipher text image databases. In the proposed scheme, the extract aggregated feature vectors to represent the related image collection and use a safe Asymmetric Scalar-Product-Preserving Encryption (ASPE) approach to encrypt these vectors while still allowing for similarity computation. To improve search time, all encrypted features are recursively clustered using the k-means method to create a tree index. The results reveal that SCBIR is faster at indexing and retrieving than earlier systems, with superior retrieval precision and scalability. In addition, our paper introduces the watermark to discover any illegal distributions of the images that are received by unlawful data users. Particularly, the cloud server integrates a unique watermark directly into the encrypted images before sending them to the data users. As a result, if an unapproved image copy is revealed, the watermark can be extracted and the unauthorized data users who spread the image can be identified. The performance of the proposed scheme is proved, while its performance is demonstrated through experimental results.
Due to the recent improvements in imaging and computing technologies, a massive quantity of image data is generated every day. For searching image collection, several content-based image retrieval (CBIR) methods have been introduced. However, these methods need more computing and storage resources. Cloud servers can fill this gap by providing huge computational power at a cheap price. However, cloud servers are not fully trusted, thus image owners have legal concerns about the privacy of their private data. In this paper, we proposed and implemented a privacy-preserving CBIR (PP-CBIR) scheme that allows searching and retrieving image databases in a cipher text format. Specifically, we extract aggregated feature vectors to represent the corresponding image collection and employ the asymmetric scalar-product-preserving encryption scheme (ASPE) method to protect these vectors while allowing for similarity computation between these encrypted vectors. To enhance search time, all encrypted features are clustered by the k-means algorithm recursively to construct a tree index. Results show that PP-CBIR has faster indexing and retrieving with good retrieval precision and scalability than previous schemes.