Urban stormwater has become a persistent concern on a global scale due to its adverse environmental implications. It is the prime vector of aquatic contaminants worldwide that causes pollutants when water bodies drain. Bioretention systems are increasingly used to alleviate setbacks associated with stormwater run-off in urban locales. It has played a substantial role in the implementation of low impact development (LID), a concept that addresses urban stormwater problems caused by land changes and development. The use of LID technologies is an innovative approach. However, it is beset with challenges, such as the insufficiency of data on rainfall distribution and difficulty in interpreting data. To address these research gaps, the present study developed a fuzzy rough set data algorithm for bioretention systems. Event mean concentration calculations and fuzzification of rainfall were performed to produce a rough set-based decision rule. Using the Weibull probability distribution, fuzzification of rainfall and parameter data, rule induction, and Preece testing, bioretention design considerations were determined. The bioretention characterizations generated evident pollutants present in the catch basin before and after filtration. In addition, the bioretention characterization conducted in this study was able to reduce the number of tests needed for rainfall identification based on the different attributes.

The present study analyzes the microstrip antenna design for wireless power transfer in smart buildings, harnessing the ambient electromagnetic radiation due to common electronic gadgets that energize wireless sensor networks, computing devices, and connected appliances. With the increased number of these devices, so does the potential for health problems caused by electromagnetic radiation. However, these devices also provide a renewable energy source through their emissions. This study suggests the creation of a 5G Microstrip antenna that enhances the absorption of this radiation for the purpose of recharging batteries in smart buildings. The design capitalizes on the inherent low-profile and cost-effective features of microstrip antennas, making them well-suited for incorporation into building infrastructure and 5G wireless technologies. Although each individual device emits a little amount of energy, the combined effect achieved by advanced antenna design and power converters is anticipated to result in a substantial energy production. The antenna designer tool from MATLAB was used to carry out a conceptual simulation of the microstrip antenna. This has set up the framework of a feasible way of predicting the performance with high efficiency and sustainability for a wireless power transfer (WPT) system.

Signal processing is crucial that as one sends information, there is a corresponding process to encode, decode, and clean the signal of unwanted noise and disruptions via use of filters. Due to the environment and how unpredictable it can be and how noise can come from almost anywhere, the typical filter to be used are adaptive filters. Adaptive filters are non-linear filters and have been used regularly regarding adaptive signal processing, this means that the filter changes accordingly and adapts to the environmental noise surrounding it. The world today has numerous applications for adaptive filters such as channel equalization and acoustic noise cancellation. This incentivizes the further development of this specific technology and the constant research that is ongoing. The main component when it comes to adaptive filtering is the algorithms used for the filter. This research compares the least mean square (LMS) and recursive least square (RLS) algorithms concerning their effectiveness in filtering out unwanted acoustic noises. The paper will cover the design and implementation of an optimized rough set based adaptive finite impulse response (FIR) filter for acoustic noise cancellation. The microstrip and bowtie antenna were used to relay the data. The software MATLAB was used for the simulation.

Split air conditioning units are crucial for ensuring the thermal comfort of buildings. Conventional scheduling or pre-timed system activities result in high consumption and wasted energy. This study proposes an intelligent control system for automatic setpoint adjustment in an educational building based on real-time indoor and outdoor environmental and room occupancy data. Principal component analysis was used to identify energy consumption components in ramp-up and steady-state power usage scenarios. K-means clustering was then used to categorize environmental scenarios and occupancy patterns to identify operational states, predict power consumption and environmental variables, and generate fuzzy inference system rules. The application of rough set theory eliminated rule redundancy by at least 99.27% and enhanced computational speed by 96.40%. After testing using real historical data from an uncontrolled environment and occupant thermal comfort satisfaction surveys reflecting a range of ACU setpoints, the enhanced inference system achieved daily average power savings of 25.56% and a steady-state power period at 63.24% of the ACU operating time, as compared to conventional and variable setpoint operations. The proposed technique provides a basis for dynamic and data-driven decision-making, enabling sustainable energy management in smart building applications.

As the world embraces a technological revolution on how everyday devices are connected to the internet, users provide sensitive information using the internet which is broken down and distributed as packets throughout the network. Packet sniffers tap to these packets, capable of potentially compromising security and privacy of unsuspecting users. This study aims to put into the test some well-known Intrusion Detection Systems (IDS) and observe how they fare against popular packet-sniffing tools such as Wireshark and tcpdump. The varied sniffing methods and techniques from various sniffing tools will provide an evaluation of performance of the intrusion detection systems.[1]  Ansari, S., Rajeev, S. & Chandrashekar, H. Packet Sniffing: A brief introduction. Potentials IEEE, Vol. 21 (5), (2002), pp. 17-19.[2]  Chomsiri, T. Sniffing packets on LAN without ARP spoofing.  IEEE in Convergence and Hybrid Information Technology ICCIT'08. Third International Conference, Vol. 2 (1), (2008), pp. 472-477.[3]  Anh, N. & Shorey, R. Network sniffing tools for WLANs: merits and limitations. 2005 IEEE International Conference on Personal Wireless Communications, (2005).[4]  Hu, Q., Asghar, M. & Brownlee, N. Evaluating network intrusion detection systems for high-speed networks. Telecommunication Networks and Applications Conference (ITNAC) 2017 27th International, (2017), pp. 1-6.[5]  Guo, K., Lu, H. & Yu, R. Packet Capture and Protocol Analysis Based on Winpcap. 2016 International Conference on Robots & Intelligent System (ICRIS), (2016).[6]  Goyal, P. & Goyal, A. Comparative study of two most popular packet sniffing tools-Tcpdump and Wireshark. 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), (2017).[7]  Meghana, J., Subashri, T. & Vimal, K. A survey on ARP cache poisoning and techniques for detection and mitigation. Signal Processing Communication and Networking (ICSCN). 2017 Fourth International Conference, (2017), pp. 1-6.[8]  Arzhakov, A. & Silnov, D. Architecture of multithreaded network scanner. IEEE Micro/Nanotechnologies and Electron Devices (EDM) 2017 18th International Conference of Young specialists, (2017), pp. 43-45.[9]  Bhosale, D. & Mane, V. Comparative study and analysis of network intrusion detection tools. 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), (2015).[10] Tirumala, S., Sathu, H. & Sarrafzadeh, A. Free and open source intrusion detection systems. Machine Learning and Cybernetics (ICMLC) 2015 International Conference, (2015).[11] Albin, E. & Rowe, N. A realistic experimental comparison of the suricata and snort intrusion -detection systems. Advanced Information Networking and Applications Workshops (WAINA) 26th International Conference, (2012).[12] Africa, A., Mesina, A., Izon, J. & Quitevis, B. Development of a Novel Android Controlled USB File Transfer Hub. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 9 (2-8), (2017), pp. 1-5.[13] SmartSniff. (2018). https://www.nirsoft.net/utils/smsniff.html.  [14] Wireshark. (2018).  https://www.wireshark.org/.[15] Windump. (2013). https://www.winpcap.org/windump/. [16] Dsniff, Dug Song. (2018). https://www.monkey.org/~dugsong/dsniff/.[17] Cain and Abel. (2018). http://www.oxid.it/cain.html. [18] Ettercap. (2018).  http://www.ettercap-project.org/ettercap/index.html. [19] Network grep. (2018).  http://ngrep.sourceforge.net/usage.html. [20] Nmap. (2018). https://nmap.org/. [21] Africa, A., Aguilar, J., Lim Jr., C., Pacheco, P. & Rodrin, S. Automated Aquaculture System that Regulates Ph, Temperature and Ammonia. 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2017).[22]S.Dhar. (2018). http://www.just.edu.jo/~tawalbeh/nyit/incs745/presentations/Sniffers.pdf.  [23] A. Africa, A Rough Set-Based Expert System for diagnosing information system communication networks. International Journal of Information and Communication Technology, Vol. 11 (4), (2017), pp. 496-512.[24]Africa, A., Bautista, S., Lardizabal, F., Patron, J. & Santos, A. Minimizing Passenger Congestion in Train Stations through Radio Frequency Identification (RFID) coupled with Database Monitoring System. ARPN Journal of Engineering and Applied Sciences, Vol. 12 (9), (2017), pp. 2863-2869.[25] Africa, A. & Cabatuan, M. A Rough Set Based Data Model for Breast Cancer Mammographic Mass Diagnostics. International Journal of Biomedical Engineering and Technology, Vol. 18 (4), (2015), pp. 359-369.[26] Africa, A. A Rough Set Based Solar Powered Flood Water Purification System with a Fuzzy Logic Model. ARPN Journal of Engineering and Applied Sciences, Vol. 12 (3), (2017), pp. 638-647.[27] Africa, A. A Mathematical Fuzzy Logic Control  Systems Model Using Rough Set Theory for Robot Applications. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 9 (2-8), (2017), pp. 7-11.[28] Brucal, S., Africa, A. & Dadios, E. Female Voice Recognition using Artificial Neural Networks and MATLAB Voicebox Toolbox. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10 (1-4), (2018), pp. 133-138.[29] Africa, A. & Velasco, J. Development of a Urine Strip Analyzer using Artificial Neural Network using an Android Phone. ARPN Journal of Engineering and Applied Sciences, Vol. 12 (6), (2017), pp. 1706-1712.[30] Loresco, P. & Africa, A. ECG Print-out Features Extraction Using Spatial-Oriented Image Processing Techniques. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10 (1-5), (2018), pp. 15-20.[31] Snort. (2018). https://www.snort.org/. [32] Africa, A. A Logic Scoring of Preference Algorithm using ISO/IEC 25010:2011 for Open Source Web Applications Moodle and Wordpress. ARPN Journal of Engineering and Applied...