A Fast Parts-based Approach to Speaker Verification using Boosted Slice Classifiers

Speaker verification on portable devices like smartphones is gradually becoming popular. In this context, two issues need to be considered: 1) such devices have relatively limited computation resources, and 2) they are liable to be used everywhere, possibly in very noisy, uncontrolled environments. This work aims to address both these issues by proposing a computationally efficient yet robust speaker verification system. This novel parts-based system draws inspiration from face and object detection systems in the computer vision domain. The system involves boosted ensembles of simple threshold-based classifiers. It uses a novel set of features extracted from speech spectra, called “slice features”. The performance of the proposed system was evaluated through extensive studies involving a wide range of experimental conditions using the TIMIT, HTIMIT and MOBIO corpus, against standard cepstral features and Gaussian Mixture Model-based speaker verification systems

Positioning and Sensing System Based on Impulse Radio Ultra-Wideband Technology

Impulse Radio Ultra-Wideband (IR-UWB) is a wireless carrier communication technology using nanosecond non-sinusoidal narrow pulses to transmit data. Therefore, the IR-UWB signal has a high resolution in the time domain and is suitable for high-precision positioning or sensing systems in IIoT scenarios. This thesis designs and implements a high-precision positioning system and a contactless sensing system based on the high temporal resolution characteristics of IR-UWB technology. The feasibility of the two applications in the IIoT is evaluated, which provides a reference for human-machine-thing positioning and human-machine interaction sensing technology in large smart factories. By analyzing the commonly used positioning algorithms in IR-UWB systems, this thesis designs an IRUWB relative positioning system based on the time of flight algorithm. The system uses the IR-UWB transceiver modules to obtain the distance data and calculates the relative position between the two individuals through the proposed relative positioning algorithm. An improved algorithm is proposed to simplify the system hardware, reducing the three serial port modules used in the positioning system to one. Based on the time of flight algorithm, this thesis also implements a contactless gesture sensing system with IR-UWB. The IR-UWB signal is sparsified by downsampling, and then the feature information of the signal is obtained by level-crossing sampling. Finally, a spiking neural network is used as the recognition algorithm to classify hand gestures

Mobile Biometry (MOBIO) Face and Speaker Verification Evaluation

This paper evaluates the performance of face and speaker verification techniques in the context of a mobile environment. The mobile environment was chosen as it provides a realistic and challenging test-bed for biometric person verification techniques to operate. For instance the audio environment is quite noisy and there is limited control over the illumination conditions and the pose of the subject for the video. To conduct this evaluation, a part of a database captured during the ``Mobile Biometry'' (MOBIO) European Project was used. In total there were nine participants to the evaluation who submitted a face verification system and five participants who submitted speaker verification systems. The nine face verification systems all varied significantly in terms of both verification algorithms and face detection algorithms. Several systems used the OpenCV face detector while the better systems used proprietary software for the task of face detection. This ended up making the evaluation of verification algorithms challenging. The five speaker verification systems were based on one of two paradigms: a Gaussian Mixture Model (GMM) or Support Vector Machine (SVM) paradigm. In general the systems based on the SVM paradigm performed better than those based on the GMM paradigm

System design and validation of multi-band OFDM wireless communications with multiple antennas

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Drone heading calculation indoors

Abstract. Aim of this master’s thesis was to study drone flying indoors and propose a drone-implemented system that enables the drone heading calculation. In the outdoors, the heading is calculated effectively with a drone’s sensors but using them indoors is limited. Indoor positioning currently has not both low-cost and reliable solution for drone heading calculating. The differences between indoor flying principles and outdoor flying principles of the drone are described in the beginning of the thesis. Then different ways to determine the drone’s heading indoors and how they compare with one another are discussed. Finally, two different heading calculation methods are implemented and tested. The methods are based on using multiple location measurements on the drone and using machine vision together with machine learning. Both methods are affordable and are evaluated to see if they could enable drone flying indoors. First method gives out potential results based on testing results, but it needs further development to be able to always provide reliable heading. Second method shows poor results based on verification.Dronen lentosuunnan laskenta sisätiloissa. Tiivistelmä. Työn tavoitteena oli tutkia dronen lentämistä sisätiloissa ja ehdottaa sitä varten droneen implementoitavaa systeemiä, joka mahdollistaa dronen suunnan laskennan. Ulkona suuntatieto saadaan dronen sensorien avulla, mutta sisätiloissa niiden tarkkuus ei riitä samalla tavalla. Sisätilapaikannuksessa ei ole olemassa sekä edullista että luotettavaa ratkaisua dronen suunnan laskentaan. Työssä perehdytään aluksi dronen lentämisen periaatteisiin sisätiloissa ja miten ne eroavat ulkona lentämisestä. Sitten kerrotaan erilaisista keinoista määrittää dronen suunta sisätiloissa ja niiden keskinäisestä vertailusta. Lopuksi testataan kahta erilaista suunnan-laskenta-menetelmää, jotka perustuvat paikkatiedon käyttöön ja konenäköön yhdessä koneoppimisen kanssa. Menetelmät ovat edullisia ja niiden sopivuutta dronen sisälennätykseen arvioidaan. Ensimmäinen menetelmä antaa hyviä testituloksia mutta tarvitsee lisää jatkokehitystä, jotta se voisi antaa aina luotettavaa suuntatietoa. Toinen menetelmä antaa heikkoja tuloksia verifioinnin perusteella

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks