Artificial Intelligence-Based Classification of Multipath Types for Vehicular Localization in Dense Environments

Multipath-geometry is the most promising approach for vehicular localization in line of sight (LOS) and non-line of sight (NLOS) scenarios. In such approach, identifying the type of the propagated multipath (MP) is an important pre-required process. However, identifying the type of the MP in dense multipath environments is challenging. The previous works proposed iterative methods for this task. The iterative methods have their limitations such as required more in-depth analysis and high complexity of computation. However, leveraging artificial intelligence advantages, a lower complexity identification method is proposed in this work. We utilized supervised learning algorithms to distinguish the direct link, first-order, and higher-order MPs of millimeter-Wave Vehicle-to-Infrastructure communication. In particular, four models namely KNN, and SVM, MLP, and LSTM have been applied. The characteristics of the received signal paths including received signal strength and elevation and azimuth angle of arrival are considered as features of the training dataset. The results showed that the accuracy rates of the classification are ranged between 96.70% and 84.0%. The best accuracy rate was 96.70% obtained by LSTM, followed by 94.47 % obtained by MLP. Whereas, 93.67% and 84.0% accuracy rats were achieved by KNN and SVM respectively

Design And Investigation On Wideband Antenna Based On Polydimethylsiloxane (PDMS) For Medical Imaging Application

This paper presents an antenna for the medical imaging application which can detect unusual tissues on any part of the body. A compact design of wideband antenna with wearable properties is proposed for the medical imaging application. The wideband antenna is designed with introducing notches to the patch and a t-shaped slot at the partial ground. Polydimethylsiloxane (PDMS) is introduced to the antenna for the implementation of the wearable antenna. The proposed antenna operated in a frequency range of 3GHz to 6GHz. The antenna that embedded with PDMS shows a good agreement to the antenna without PDMS. An experimental proposed structure shows a good agreement with the simulated results. The overall dimension of the antenna is 24mm(W) x 38mm( L) which consider is a miniature antenna. This proposed design give an alternative solution for the antenna which cannot be wear on the body and protect the antenna. The introduction of PDMS will reduce the signal reflection cause by the high coupling of the human body

An analysis of lowest energy consumption (CPU time) through running several cryptography algorithms with different video formats

One persistent obstacle has been verified to be one of the main problems with the main developments in the electronics and technology fields, called: Data Security. The data should be encrypted in order to be quickly and securely linked via the electronic information transfer over the network. The procedure of transforming plain text to ciphered-text is called encryption, where cannot be changed or understood simply by undesirable individuals. It may similarly be described as the science that utilizes mathematics in decryption and encryption data processes. In this article, we consider different significant algorithms utilized for data decryption and encryption in whole areas, for making a comparative study for most vital algorithms. This paper focuses on various current cryptography algorithms types. This paper also analyses the algorithm's security and parameters that define the cryptosystem efficiency

Integrated System Technology of POME Treatment for Biohydrogen and Biomethane Production in Malaysia

In recent years, production of biohydrogen and biomethane (or a mixture of these; biohythane) from organic wastes using two-stage bioreactor have been implemented by developing countries such as Germany, USA and the United Kingdom using the anaerobic digestion (AD) process. In Thailand, biohythane production in a two-stage process has been widely studied. However, in Malaysia, treating organic and agricultural wastes using an integrated system of dark fermentation (DF) coupled with anaerobic digestion (AD) is scarce. For instance, in most oil palm mills, palm oil mill effluent (POME) is treated using a conventional open-ponding system or closed-digester tank for biogas capture. This paper reviewed relevant literature studies on treating POME and other organic wastes using integrated bioreactor implementing DF and/or AD process for biohydrogen and/or biomethane production. Although the number of papers that have been published in this area is increasing, a further review is needed to reveal current technology used and its benefits, especially in Malaysia, since Malaysia is the second-largest oil palm producer in the world.</jats:p

Artificial Intelligence-Based Classification of Multipath Types for Vehicular Localization in Dense Environments

Multipath-geometry is the most promising approach for vehicular localization in line of sight (LOS) and non-line of sight (NLOS) scenarios. In such approach, identifying the type of the propagated multipath (MP) is an important pre-required process. However, identifying the type of the MP in dense multipath environments is challenging. The previous works proposed iterative methods for this task. The iterative methods have their limitations such as required more in-depth analysis and high complexity of computation. However, leveraging artificial intelligence advantages, a lower complexity identification method is proposed in this work. We utilized supervised learning algorithms to distinguish the direct link, first-order, and higher-order MPs of millimeter-Wave Vehicle-to-Infrastructure communication. In particular, four models namely KNN, and SVM, MLP, and LSTM have been applied. The characteristics of the received signal paths including received signal strength and elevation and azimuth angle of arrival are considered as features of the training dataset. The results showed that the accuracy rates of the classification are ranged between 96.70% and 84.0%. The best accuracy rate was 96.70% obtained by LSTM, followed by 94.47 % obtained by MLP. Whereas, 93.67% and 84.0% accuracy rats were achieved by KNN and SVM respectively

Integrated system technology of POME treatment for biohydrogen and biomethane production in Malaysia

In recent years, production of biohydrogen and biomethane (or a mixture of these; biohythane) from organic wastes using two-stage bioreactor have been implemented by developing countries such as Germany, USA and the United Kingdom using the anaerobic digestion (AD) process. In Thailand, biohythane production in a two-stage process has been widely studied. However, in Malaysia, treating organic and agricultural wastes using an integrated system of dark fermentation (DF) coupled with anaerobic digestion (AD) is scarce. For instance, in most oil palm mills, palm oil mill effluent (POME) is treated using a conventional open-ponding system or closed-digester tank for biogas capture. This paper reviewed relevant literature studies on treating POME and other organic wastes using integrated bioreactor implementing DF and/or AD process for biohydrogen and/or biomethane production. Although the number of papers that have been published in this area is increasing, a further review is needed to reveal current technology used and its benefits, especially in Malaysia, since Malaysia is the second-largest oil palm producer in the world

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)