Pre-trained based CNN model to identify finger vein

In current biometric security systems using images for security authentication, finger vein-based systems are getting special attention in particular attributable to the facts such as insurance of data confidentiality and higher accuracy. Previous studies were mostly based on finger-print, palm vein etc. however, due to being more secure than fingerprint system and due to the fact that each person's finger vein is different from others finger vein are impossible to use to do forgery as veins reside under the skin. The system that we worked on functions by recognizing vein patterns from images of fingers which are captured using near Infrared (NIR) technology. Due to the lack of an available database, we created and used our own dataset which was pre-trained using transfer learning of AlexNet model and verification is done by applying correct as well as incorrect test images. The result of deep convolutional neural network (CNN) based several experimental results are shown with training accuracy, training loss, Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC)

Finger vein identification based on transfer learning of AlexNet

Nowadays finger vein-based validation systems are getting extra attraction among other authentication systems due to high security in terms of ensuring data confidentiality. This system works by recognizing patterns from finger vein images and these images are captured using a camera based on near-infrared technology. In this research, we focused finger vein identification system by using our own finger vein dataset, we trained it with transfer learning of AlexNet model and verified by test images. We have done three different experiments with the same dataset but different sizes of data. Therefore, we obtained varied predictability with 95% accuracy from the second experiment

A systematic approach for developing course learning outcomes to fulfill accreditation requirements in Malaysia

Writing appropriate learning outcomes is crucial to course design. Properly written outcomes aids the design of other course elements such as delivery methods and assessment. This paper proposes a systematic approach for writing learning outcomes. The proposed procedure takes advantage of the hierarchical structure of the programme building components. When applied, the procedure helps meet some of the accreditation requirements imposed by the Malaysian Engineering Accreditation Council such as explicit assessment of programme outcomes. Key to success is the careful mapping between course components. Besides, the procedure simplifies course design consequently leading to continual improvement. The procedure can be applied to wide range engineering courses. It also gives more control on planning a balanced assessment

The impact of time and power allocation on the performance of the three-node decode and-forward relay channel

Relying has in use for decades to tackle some of the challenges of wireless communication such as extending transmitting distance, transmitting over rough terrains. Relaying also achieve diversity which was proposed recently as an effective means to combat channel fading. In this work, effect of time and power allocation on relay performance is studied. The channel considered is the three-node channel with half-duplex constraint on the relay. The relaying technique assumed is decode-and-forward. Mutual information is used as the criteria to measure channel performance whereas noise is assumed to be the primary channel impairment. After deriving suitable formula for the mutual information as a function of time and power allocation, numerical results are obtained. Results have shown that the importance of relaying is more apparent when more resources are allocated to the relay. It was also shown that quality of the source to destination link has direct impact on the decision to relay or not to relay. Relatively good source to destination channel makes relaying less useful. The opposite is true for the other two links, namely the source to relay channel and the relay to destination channel. When these two channels are good, relaying becomes advantageous

Convolutional neural network-based finger vein recognition using near infrared images

Convolutional Neural Network (CNN) is opening new horizons in biometrics-based authentication field and finger vein recognition is the prominent one which can provide the best possible security system depending on this aforementioned technology. In this paper, we used 5 convolutional layers and 4 fully-connected layers where our developed network has shown the capability to produce the result with almost 100% accuracy rate which became possible due to the fact that deep learning, an end-to-end system is used which performs better in a lot of aspects in comparison to conventional techniques.Convolutional Neural Network (CNN) is opening new horizons in biometrics-based authentication field and finger vein recognition is the prominent one which can provide the best possible security system depending on this aforementioned technology. In this paper, we used 5 convolutional layers and 4 fully-connected layers where our developed network has shown the capability to produce the result with almost 100% accuracy rate which became possible due to the fact that deep learning, an end-to-end system is used which performs better in a lot of aspects in comparison to conventional techniques

Positioning of a wireless relay node for useful cooperative communication

Given the exorbitant amount of data transmitted and the increasing demand for data connectivity in the 21st century, it has become imperative to search for pro-active and sustainable solutions to the effectively alleviate the overwhelming burden imposed on wireless networks. In this study a Decode and Forward cooperative relay channel is analyzed, with the employment of Maximal Ratio Combining at the destination node as the method of offering diversity combining. The system framework used is based on a three-node relay channel with a source node, relay node and a destination node. A model for the wireless communications channel is formulated in order for simulation to be carried out to investigate the impact on performance of relaying on a node placed at the edge of cell. Firstly, an AWGN channel is used before the effect of Rayleigh fading is taken into consideration. Result shows that performance of cooperative relaying performance is always superior or similar to conventional relaying. Additionally, relaying is beneficial when the relay is placed closer to the receiver

Performance analysis of BUNSD-LMA

The IETF is developed Network Mobility Basic Support (NEMO BSP) to support session continuity and reachability to the Mobile Network Nodes (MNNs) as one unit while they move. While NEMO move and attached to different networks, it needs to register the MNNs. This function of registration decreases the performance of NEMO. NEMO BSP suffers from some challenges. The most important of these challenges are route optimization, seamless mobility, handover latency and registration time. Binding Update No Sense Drop (BUNSD) Binding Cache Entry (BCE) in Local Mobility Anchor (LMA) is proposed to find a possible solution to MNNs. MNNs that are roaming in a Proxy Mobile IPv6 (PMIPv6) domain to perform seamless mobility while they are maintaining their session continuity through mobile router (MR). In this paper, BUNSD-LMA is analyzed mathematically with NEMO BS based on handover latency, total packet delivery delay cost, and throughput time during handoff. The analytical result shows that the BUNSD-LMA had better performance in term of handover, and registrations of MNNs. As a result the total packet loss is decreased and seamless mobility of MNNs enhanced compared to NEMO BS benchmarks. Keywords: NEMO, PMIPv6, BUNSD, MR, MAG, LM

A review of lossless audio compression standards and algorithms

Over the years, lossless audio compression has gained popularity as researchers and businesses has become more aware of the need for better quality and higher storage demand. This paper will analyse various lossless audio coding algorithm and standards that are used and available in the market focusing on Linear Predictive Coding (LPC) specifically due to its popularity and robustness in audio compression, nevertheless other prediction methods are compared to verify this. Advanced representation of LPC such as LSP decomposition techniques are also discussed within this paper

A Packet Scheduling Scheme for Improving Real-time Applications Performance in Downlink LTE–advanced

Quality of Service based packet scheduling is a key-feature of LTE-A mandating selection and transmission of individual user packets based on their priority. HARQ Aware Scheduling, Retransmission Aware Proportional Fair, Chase Combining Based Max C/I Scheduling and Maximum- Largest Weighted First (M-LWDF) are popular Packet Scheduling Algorithms (PSAs) developed to meet QoS requirements. In highly erroneous LTE-A cannel, M-LWDF is considered to be one of best PSA. To validate the performance of M-LWDF for the LTE-A channel, Mean User Throughout, and Fairness performance measures were evaluated for 3 different PSAs designed based on M-LWDF algorithm in this paper. A C++ based simulation results indicate the superiority of the PSA3 algorithm within the threshold of the performance measures against benchmarks. It has shown more efficiency and the performance of RTA traffic was enhanced. Results show that PSA3 is superior to its benchmark PSA2 by 12% in Mean User Throughput and 11% in Fairness. PSA2 performed the worst because it prioritizes new users and it allocated all available RBs to the scheduled user leaving the rest to wait in the buffer. PSA3 maintians good Mean User Throughput and fairnessdue to scheduling each user on its RB which leads to multi-user diversity

Performance analysis of IEEE 1857.2 lossless audio compression linear predictor algorithm

In addition to commercial consumer market, high quality and multichannel audio has become more relevant in many other fields. More lossless audio compression standards and algorithm are proposed to tackle the problem of reducing the size of a raw audio bitstream without loss of data. This paper has two objectives. First, we aim to review and analyze the performance of the IEEE 1857.2 standard. Focus is on the predictor and the pre-processing block. The predictor utilizes Linear Predictive Coding (LPC) as its main mechanism. The pre-processing block normalizes the error residue of the Linear Predictive encoder. The second objective is to present results from experimenting different wave sound file type inputs. Results are discussed, and comparisons are made to identify the effect on compression ratio of the lossless encoder. As well as this, comparison is made to analyze the entropy flatness of the error residue from the predictor and pre-processing output the predictor order in the linear predictive coding mechanism varies. We concluded that pre-processing block works well to flatten the output at lower predictor order for all the sound types, but works best at improving the residual output for music sound type