Negative impedance converter (NIC) circuits are very interesting and beneficial building blocks with the capability of generating negative resistance, capacitance and/or inductance elements which do not exist as a singular electrical component in practice. They are commonly used for the impedance matching and parasitic element cancellation in electrically small antennas and amplifier circuits. In this study, a special kind of NIC circuit in HF band up to 30 MHz is analysed, designed and physically realised with a current feedback (CFB) operational amplifier (OPAMP) which is the core active element of the NIC circuit. The non-inverting terminal of CFB OPAMP is used for RF input signal with the elimination of DC offset voltage in the proposed NIC circuit. The negative impedance conversion capability of the circuit is theoretically proved and simulated first. This capability of CFB OPAMP to generate negative impedance is very important in high-frequency applications as they have low distortion and faster switching than that of voltage feedback (VFB) OPAMP. For the physical realizations, printed circuit board (PCB) is designed and manufactured on FR-4 dielectric material. Measurement results obtained from the realized circuit with resistive (100Ω) and capacitive (10pF) loads to be converted negatively showed that the negative impedance conversion performance of the circuit is very close to its theoretical behaviour in the lower HF frequencies generally in 3- 20 MHz band.

The ever-increasing demands of Internet services like video on demand, big data applications, IoE and multi-tenant data centers have compelled the network industry to change its conventional non-evolving network architecture. Software Defined Network (SDN) has emerged as a promising network architecture which provides necessary abstractions and novel APIs to facilitate network innovations and simplifies network resource management by breaking the conventional network into multiple planes. All these SDN planes interact through open interfaces or APIs which are commonly categorized into southbound, northbound and west/eastbound interfaces. In this manuscript, we have identified and emphasized various communication protocols used at south and northbound interfaces. We have provided a taxonomy of south and northbound communication protocols based on their dependence, capabilities and properties. The pros and cons associated with each communication mechanism are highlighted and the numerous research challenges and open issues involved at these two interfaces are elucidated. In addition to it, we have proposed the necessary abstractions and extensions required in communication protocols at these two interfaces to simplify real-time monitoring and virtualization in next generation networks.

In the recent development of the Internet of Things (IoT), Artificial Intelligence (AI) plays a significant role in enabling cognitive IoT applications. Among popular IoT applications, location-based services are considered one of the primary applications where the real-time location of a moving object is estimated. In recent works, AI-based techniques have been investigated to the indoor localization problem, showing significant advantages over deterministic and probabilistic algorithms used for indoor localization. This paper presents a feasibility study of using Bluetooth Low Energy (BLE) and Feed Forward Neural Networks (FFNN) for indoor localization applications. The signal strength values received from thirteen different BLE ibeacon nodes placed in an indoor environment were trained using a Feed-Forward Neural Network (FFNN). The FFNN was tested under other hyper-parameter conditions. The prediction model provides reasonably good accuracy in classifying the correct zone of 86% when batch size is 100 under the learning rate of 0.01.Hence the FFNN could be used to implement on location-based IoT applications.

In this fast-paced technological world, individuals want to access all their electronic equipment remotely, which requires devices to connect over a network via the Internet. However, it raises quite a lot of critical security concerns. This paper presented a home automation security system that employs the Internet of Things (IoT) for remote access to one's home through an Android application, as well as Artificial Intelligence (AI) to ensure the home's security. Face recognition is utilized to control door entry in a highly efficient security system. In the event of a technical failure, an additional security PIN is set up that is only accessible by the owner. Although a home automation system may be used for various tasks, the cost is prohibitive for many customers. Hence, the objective of this paper is to provide a budget and user-friendly system, ensuring access to the application and home attributes by using multi-modal security. Using Haar Cascade and LBPH the system achieved 92.86% accuracy while recognizing face.

This Research focuses on cross layer approach to enhance the User Experience and Quality Of Service of video streaming in Long Term Evolution mobile network. During run time channel quality index is observed. Application layer requirement is fulfilled by changing modulation techniques as well as dynamic allocation of resources. This paper proposes a algorithm which improves the End-to-End Delay,Peak Signal to Noise Ratio of transmitted video over Mobile Network. Experimental results show the improvement in end to end delay by 89% and Peak Signal to Noise Ratio by 8%. Simulation in Network Simulator-3 provide credible evidence that this proposed cross-layer algorithm outperforms between earlier algorithm in providing better Quality Of Experience for real time, adaptive video streaming over Long Term Evolution.