Improvement of non-electrical engineering student knowledge content and motivation to learnt electronic circuit and system through implementation of hands – on learning

Electronic circuit and system (ECS) course introduces students to understand the circuit and system operation as well as using tool and technique to solve engineering problem related to circuitry. This course is compulsory offered for both electrical and non – electrical engineering students. The non – electrical engineering student will find this course challenging and difficult to understand especially when it involves circuit design and analysis. Therefore, transforming the learning environment from traditional to innovative approach could help in the enhancement of student learning. Therefore, this paper is presented to assess student reflection after the implementation of hands – on learning towards improvement of content of knowledge and motivation in learning about active filter in ECS course. Students are required to design an active filter using the specification given, verified using simulation tool and doing the actual testing in laboratory. A qualitative study is conducted via survey questionnaire and the student reflections were analyzed using thematic analysis. Results shows an improvement in student content of knowledge and motivation to learnt after the implementation of the hands – on learning. The outcome shows in this study suggest the necessity of including the hands – on learning to maximize student engagement for students as well as achieving the course learning outcome

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%

Route optimization via RSSI APPs in indoor proxy mobile IPv6 test-bed

Traditionally IP mobility support mechanisms, Mobile IPv4 or Mobile IPv6 are based on the host-based solution to keep on going sessions despite the movement. The current trend is towards networks-based solutions where mobility support is based on network operation. Proxy Mobile IPv6 (PMIPv6) has been developed by the Internet Engineering Task Force (IETF) as a network-based mobility management protocol. The development is aimed to guarantee and support mobility for IP devices or called as Mobile Node (MN) without the MN involving in any IP layer mobility related to signaling as stated in RFC5213. PMIPv6 in RFC5213 address the needed to enable Localized Routing (LR) but it not specify a complete procedure to establish Route Optimization (RO). Data packets belong to MN need to travel to Local Mobility Anchor (LMA) via bidirectional tunnel between Mobile Access Gateway (MAG) and LMA. This result to long end to end delay. This un-optimized route result in increasing signaling data delay, huge handover latency, large transport cost and many more. Therefore, it important to optimized the data path so that the data destined to MN will be traverse using the shorter path or directly change traffic tunnel between MAG. As for this, the aims of this study are to reduce the handover latency and optimized data route by the illustration of development the PMIPv6 test-bed with implementation of RO via RSSI APPs. Several experiment will be discuss to see the performance of RO algorithm via RSSI APPs with non-optimized RO

Route optimization in proxy mobile IPv6 test-bed via RSSI APPs

Proxy Mobile IPv6 is the new protocol, but there is a problem on Localized Routing (LR) algorithm which it is not ready build in PMIPv6 protocol. As for this in PMIPv6 in RFC5213 address the needed to enable Localized Routing (LR) but it not specify a complete procedure to establish Route Optimization (RO). RFC6279 state the problem statement on LR issue with several scenarios to tackle down. LR is important especially to minimize data delay and decrease handover latency due to the un-optimized data route. Data packets on PMIPv6 protocol without LR always need to travel to Local Mobility Anchor (LMA) with result in end-to-end delay. Therefore, this paper propose the new LR algorithm to optimized data route for selected scenarios to reduce handover and data packet delay. This paper also will illustrate the setting up of the PMIPv6 and test-bed performance

Prototype development for real-time epilepsy seizures detector using three parameters

This paper proposes a prototype for real-time epilepsy seizures detection using skin conductance, temperature and sense movement. This proposed work is expected to help epilepsy patients to receive immediate help from the people around when seizures happen. This prototype is wearable and developed using Arduino Nano, Galvanic Skin Response (GSR) sensor, accelerometer, temperature sensor and pulse sensor. Epilepsy patients can wear this prototype just like a watch. The prototype is connected to the mobile application via Bluetooth and can alert the people around by buzzing alarm as well as sending text message to the doctor or family member. Details development and results are discussed in this paper

Prototype development for real-time epilepsy seizures detector using three parameters

This paper proposes a prototype for real-time epilepsy seizures detection using skin conductance, temperature and sense movement. This proposed work is expected to help epilepsy patients to receive immediate help from the people around when seizures happen. This prototype is wearable and developed using Arduino Nano, Galvanic Skin Response (GSR) sensor, accelerometer, temperature sensor and pulse sensor. Epilepsy patients can wear this prototype just like a watch. The prototype is connected to the mobile application via Bluetooth and can alert the people around by buzzing alarm as well as sending text message to the doctor or family member. Details development and results are discussed in this paper