Hundreds of image encryption algorithms have been developed for the security and integrity of images through the combination of DNA computing and chaotic maps. This combination of the two instruments is not sufficient enough to thwart the potential threats from the cryptanalysis community as the literature review suggests. To inject more robustness and security stuff, a novel image encryption scheme has been written in this research by fusing the chaotic system, DNA computing and castle -a chess piece. As the plain image is input, its pixels are shifted to the scrambled image at the randomly chosen pixel addresses. This scrambling has been realized through the routine called Image Scrambler using Castle (ISUC). Castle randomly moves on the hypothetical large chessboard. Pixels taken from the plain image are shifted to the addresses of the scrambled image, where castle lands in each iteration. After the plain image is scrambled, it is DNA encoded. Two mask images are also DNA encoded. Then to throw the diffusion effects in the cipher, DNA Addition and DNA XOR operations between the DNA encoded pixels data and the DNA encoded mask images have been conducted. Next, the pixels data are converted back into their decimal equivalents. Four dimensional chaotic system has been used to get the chaotic vectors. The hash codes given by the SHA-256 function have been used in the cipher to introduce the plaintext sensitivity in its design. We got an information entropy of 7.9974. Simulation carried out through the machine, and the thorough security analyses demonstrate the good security effects, defiance to the varied attacks from the cryptanalysis community, and the bright prospects for some real world application of the proposed image cipher.

Recent progress in Device-Free Wi-Fi Sensing (DFWS) has established the use of wireless signals like Wi-Fi not only to communicate but also as a tool to enable device-free sensing. As an emerging technique, DFWS has many capable applications such as sensing activity and gesture and fall detection, monitoring elderly, surveillance, and many more applications while waiving out the necessity to mount devices on the object. A wide range of applications can use the channel state information (CSI) from commercial Wi-Fi devices pervasively for ubiquitous sensing. Existing CSI tools, such as the Intel 5300 network interface controller tool or the Atheros 9390 tool, have limitations when deployed in large-scale systems due to their high deployment costs and limitations in the resolution of CSI measurements. Due to these shortcomings, DFWS applications need an alternative CSI tool in order to perform efficiently. In this paper, we present ESP32-based Wi-ESP as a CSI gathering tool that can report detailed CSI measurements based on 802.11n standards. The proposed Wi-ESP tool works as a complete device by collecting the CSI measurements as well as processing further for DFWS applications. Wi-ESP can work as standalone device, unlike other CSI tools, and can offer large-scale deployment to many DFWS applications. In this paper, we have explored the options of Wi-ESP as a tool for CSI measurements and processing and propose it as a tool for DFWS.

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With the rapid emergence of autonomous vehicles, there is a need to build such communication systems which help the passengers to communicate with autonomous vehicles (AVs) robustly. In this regard, this research work presents a multimodal passenger communication system. The communication system is known as "buddy" for AVs. Buddy is an all in one control system for AVs which incorporates touch, speech, text, and emotion recognition methods of interaction. Buddy makes it easy for passengers to interact with AVs. It enables the communication between the passengers and the AV which eventually provides a safe driving experience. Moreover, we have proposed and developed our own simulator two evaluate the performance of our proposed passenger communication system. We have also conducted extensive infield-tests to test the effectiveness of the proposed system. The extensive rigor analysis validates the results and hence the significance of the proposed passenger communication system.

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We present a computational imaging approach to estimate the depth from a single image using axial chromatic aberrations. It includes a co-design of optics and digital processing to select the optimal parameters of a lens such as focal length, f -number, and chromatic focal shift according to the performance of a depth estimation algorithm on the digital side. A simulation framework evaluates the complete systems performance in different imaging conditions including optimal axial chromatic lens aberration. A low-complexity algorithm estimates the depth map of real scenes. Experiments on real and synthetic scenes show the feasibility of the proposed system for depth estimation. In the case of relatively broadband object spectra and a lens with focal length of 4 mm, depth is estimated with an RMS error of 6-10%.Zusammenfassung In diesem Beitrag stellen wir einen neuartigen Ansatz vor, der es ermöglicht, die Tiefe aus einer einzigen Aufnahme anhand chromatischer Aberrationen zu bestimmen. Ferner wird ein optisches Designverfahren zur Auffindung der bestmöglichen optischen Parameter, wie die Brennweite, die Blendenzahl und die axiale chromatische Aberration in Abstimmung mit der Leistung des Algorithmus für die Tiefenschätzung auf digitaler Seite beschrieben. Für verschiedene Aufnahmebedingungen wird die Leistung des Gesamtsystems in einer Simulationsumgebung unter der Annahme einer optimal vorliegenden chromatischen Aberration untersucht. Ein Algorithmus geringer Komplexität schätzt die Tiefenkarte für reale Aufnahmen. Untersuchungen an realen Szenen zeigen die Machbarkeit des hier vorgestellten Systems zur Tiefenschätzung. Im Falle relativ breitbandiger Objektspektren und einer Linse mit einer Brennweite von 4 mm wird die Tiefe mit einem RMS-Fehler von 6-10% geschätzt.

Objective Verbal autopsy is a technique used to collect information about a decedent from his/her family members using questionnaires, conducting interviews, making observations, and sampling. In substantial parts of the world, particularly in Africa and Asia, many deaths are unrecorded. In 2017, globally pregnant women were dying daily around 810 and 295,000 in a year because of pregnancy-related problems, pointed out by World Health Organization. Identifying the cause of a death is a complex process which requires in-depth medical knowledge and practical experience. Generally, medical practitioners possess different knowledge levels, set of abilities, and problem-solving skills. Additionally, the medical negligence plays a significant part in further worsening the situation. Accurate identification of the cause of death can help a government to take strategic measures to focus on, particularly increasing the death rate in a specific region. Methods This research provides a solution by introducing a semantic-based verbal autopsy framework for maternal death (SVAF-MD) to identify the cause of death. The proposed framework consists of four main components as follows: (1) clinical practice guidelines, (2) knowledge collection, (3) knowledge modeling, and (4) knowledge codification. Maternal ontology for the framework is developed using Protégé knowledge editor. Resource description framework application programming interface (API) for PHP (RAP) is used as a Semantic Web toolkit along with Simple Protocol and RDF Query Language (SPARQL) is used for querying with ontology to retrieve data. Results The results show that 92% of maternal causes of deaths assigned using SVAF-MD correctly matched manual reports already prepared by gynecologists. Conclusion SVAF-MD, a semantic-based framework for the verbal autopsy of maternal deaths, assigns the cause of death with minimum involvement of medical practitioners. This research helps the government to ease down the verbal autopsy process, overcome the delays in reporting, and facilitate in terms of accurate results to devise the policies to reduce the maternal mortality.

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