Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high accuracies.MFCCs represents a sequence of voice signal-specific features.This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings.Since the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D array.Several learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the speaker.The highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation function.The most important output of this study is the inclusion of human voice as a new feature set.

An Efficient and Reliable Multicasting for Smart Cities

The Internet of thing(IoT)is a growing concept for smart cities,and it is compulsory to communicate data between different networks and devices.In the IoT,communication should be rapid with less delay and overhead.For this purpose,flooding is used for reliable data communication in a smart cities concept but at the cost of higher overhead,energy consumption and packet drop etc.This paper aims to increase the efficiency in term of overhead and reliability in term of delay by using multicasting and unicasting instead of flooding during packet forwarding in a smart city using the IoT concept.In this paper,multicasting and unicasting is used for IoT in smart cities within a receiver-initiated mesh-based topology to disseminate the data to the cluster head.Smart cities networks are divided into cluster head,and each cluster head or core node will be responsible for transferring data to the desired receiver.This protocol is a novel approach according to the best of our knowledge,and it proves to be very useful due to its efficiency and reliability in smart cities concept because IoT is a collection of devices and having a similar interest for transmission of data.The results are implemented in Network simulator 2(NS-2).The result shows that the proposed protocol shows performance in overhead,throughput,packet drop,delay and energy consumption as compared to benchmark schemes.

Man Overboard Detection System Using IoT for Navigation Model

Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry,roro,catamaran,frigate,yacht that are the vehicles services for the purpose of logistic and passenger transport.In this paper,all processes in the event of Man overboard(MOB)are initiated for smart transportation.In MOB the falling person is totally dependent on the person who first saw the falling person.The main objective of this paper is to develop a solution to this significant problem.If a staff member or a passenger does not see the fall into the sea,undesirable situations such as disappearance,injury and death can occur during the period until the absence of the fallen person is noticed.In this paper,a comprehensive and improved solution is provided in terms of personnel and passenger security especially in all the floating elements,in which human resources are intensively involved like passengers,freight,logistics,fishing,business,yacht,leisure and naval vessels.In this case,if the ship’s personnel or passengers fall into the sea in any way,it detected the fallen person into the sea by the sensors in the portable emergency device,which each person will carry.The warning system is activated via the in-ship automation system to which the information is transmitted by wireless communication.Thus,the case of MOB will be determined quickly.Internet of things(IoT)has a key role in identifying the location and information of the person falling into the sea through sensors,radio frequency,GPS and connected devices.Simultaneously,the alarm system on board will be activated and MOB flag(Oscar)will automatically be opened.This paper enables the Search and rescue(SAR)operations to be initiated and accelerated without losing time through decision-making process.

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Wireless Sensor Network(WSN)is considered to be one of the fundamental technologies employed in the Internet of things(IoT);hence,enabling diverse applications for carrying out real-time observations.Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks.A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks.Considering networks to be a type of graph,this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph.This idea was extended to the concept of edge metric dimension of a graph G,which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network.Regular plane networks can be easily constructed by repeating regular polygons.This design is of extreme importance as it yields high overall performance;hence,it can be used in various networking and IoT domains.The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks.In this paper,it is proved that the minimum landmarks required for the honeycomb network HC(n),and the hexagonal network HX(n)are 3 and 6 respectively.The bounds for the landmarks required for the hex-derived network HDN1(n)are also proposed.