Habitat Monitoring using wireless sensor networks

The deployment of wireless sensor networks in habitat monitoring is gaining importance as the manpower cost is increasing day by day. The positions of the cattle is detected and if detections at successive time intervals indicate that the position of the cattle is hardly changing, there is a chance that the cattle is sick or injured and a warning message is issued to the owner of the farm. The positions have been estimated using the Direction of Arrival estimation by maximum likelihood and MUSIC (MUltiple SIgnal Classification) algorithms. The performance of the system has been evaluated in terms of minimum root mean square error and probability of resolution. The results of direction of arrival have been improvised using the averaging process and the multimodal problem has been optimized using differential evolution. Since Direction of Arrival estimation gives only the direction and not the precise position, the phase detection of the signals is done to differentiate different positions having the same direction of arrival. Finally analysis is done regarding the movement of cattle. If it is found that they do not move and occupy the same position for a considerably large period of time, warning message is issued to the owner of the farmland

Wavelet pooling for convolutional neural networks

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Petia Radeva,[en] Wavelets are mathematical functions that are currently used in many computer vision problems, such as image denoising or image compression. In this work, first we will study all the basic theory about wavelets, in order to understand them and build a basic knowledge that allows us to develop another application. For such purpose, we propose two pooling methods based on wavelets: one based on simple wavelet basis and one that combines two basis working in parallel. We will test them and show that they can be used at the same level of performance as max and average pooling

Diseño de la transformada rápida de Fourier con algoritmo Split-Radix en FPGA

La Transformada Rápida de Fourier SplitRadix (SRFFT) es un algoritmo computacionalmente eficiente que se utiliza para calcular la Transformada Discreta de Fourier (DFT), la cual a partir de una secuencia finita de datos, obtiene otra que describe su comportamiento en el dominio de la frecuencia. Esta herramienta se utiliza en óptica, acústica, física cuántica, teorías de sistemas, tratamiento de señales, reconocimiento de voz, entre otros.Tesi

Estudi comparatiu de la publicació científica de la UPC i l’ETSETB vs. altres universitats (2006-2016)

L'informe es centra en la publicació científica especialitzada en l'àmbit temàtic propi de l'ETSETB: l'enginyeria de telecomunicacions i l'electrònica. Es comparen indicadors bibliomètrics de la UPC i l'ETSETB amb els d'altres universitats nacionals, europees i internacionals amb activitat de recerca notable en l'àrea de les telecomunicacions i l'electrònica.Postprint (published version

Modeling and Compensation of Transceiver Non-Reciprocity in TDD Multi-Antenna Base-Station

Due to the increasing demands for higher system capacity, higher data rates and better quality of service in wireless networks, advanced techniques that improve wireless link reliability and spectral efficiency are introduced. This includes different multi-antenna technologies, in particular multi-user (MU) MIMO-OFDM. In MU MIMO-OFDM systems, base-station with multiple antennas communicates simultaneously with multiple users over a given time-frequency resource. In downlink transmission, base-station transmits multiple data streams through its antennas towards the user devices. In uplink transmission, the user equipment send in parallel multiple data streams towards the base-station. In general, channel non-reciprocity is a very important factor in cellular communications, in particular in precoded MU MIMO-OFDM systems adopting time division duplexing (TDD). Based on the channel reciprocity principle, the channel state information at base-station for the downlink transmission can be determined through estimating the uplink channels. In practice, however, there are always unavoidable frequency mismatch characteristics between transmitter and receiver. Frequency response mismatch can thus change the reciprocal nature of downlink and uplink channels. The impact of transceiver non-reciprocity at equipment on user side causes inter-stream interference which can be compensated using detection processing. The impact of transceiver non-reciprocity at base-station causes inter-user interference and degrades the system performance of MU MIMO-OFDM systems. To ensure the system reliability and high performance in case of transceiver non-reciprocity, some non-reciprocity estimation and compensation methods are required. The previous work has proposed the estimation-compensation framework that gives a flexible solution to restore the channel reciprocity. But there is a need to validate the findings and performance of the proposed estimation-compensation framework. The modeling of transceiver frequency response mismatch characteristics using actual measurement data has been carried out in this thesis research work. The actual measurement data comprises of one base-station with two antennas and two user equipment devices with single antenna. The estimated uplink and downlink channels from measurement data are used to compute the non-reciprocity matrix at base-station and at the equipment on user side after mathematical calculations. The normalized parameters for transceiver non-reciprocity matrices are extracted subcarrier-wise. The frequency-domain normalized non-reciprocity parameters are modeled as a FIR filter in the time-domain and the most energy concentrates then on few time-domain taps. The extracted parameters are mildly frequency-selective. The impact of extracted transceiver non-reciprocity is then analyzed by implementing a simulator of TDD precoded MU MIMO-OFDM system. In general, the frequency-selectivity implies that the reciprocity estimation and compensation is needed subcarrier-wise. The pilot-based estimation of non-reciprocity parameters at base-station is carried out in order to enhance the system performance. To estimate channel non-reciprocity parameters, a link between base-station and one of user equipment devices is assumed. The right choice of selecting the user is also important for noise reduction in estimation. For estimation, the DL transmission channel is modeled as a Rayleigh fading multipath channel with a given 7-tap channel power delay profile. The downlink data including sparsely located pilots at selected subcarriers is transmitted to the user through downlink channel without precoding. The downlink channel is then estimated at the user equipment side. This provides estimates only at the pilot subcarriers. Therefore, linear interpolation is used to obtain channel response estimates at the actual data subcarriers. The uplink pilot data is transmitted to base-station from user equipment through uplink channel. The uplink channel is obtained by estimated downlink channel in case of non-reciprocity parameters. Then, estimate of non-reciprocity at base-station is computed by using inverse processing and an interpolator. The estimated parameters are used as a compensator filter in order to compensate the channel non-reciprocity in the system. The simulated results show that the performance deviates from the ideal linear precoded MU MIMO-OFDM system because of non-reciprocity in case of both error control coded and uncoded channels. The compensated results in terms of coded and uncoded channel schemes have been evaluated which are closer to ideal linear precoded MU-MIMO OFDM system. These results show that the impact of non-reciprocity on system performance is less severe when a coded channel is deployed as compared to uncoded channel. The modeling of transceiver frequency response mismatch characteristics using actual measurement data proves that the proposed non-reciprocity model in the previous research work is close to reality

Stepping Stone Detection for Tracing Attack Sources in Software-Defined Networks

Stepping stones are compromised hosts in a network which can be used by hackers and other malicious attackers to hide the origin of connections. Attackers hop from one compromised host to another to form a chain of stepping stones before launching attack on the actual victim host. Various timing and content based detection techniques have been proposed in the literature to trace back through a chain of stepping stones in order to identify the attacker. This has naturally led to evasive strategies such as shaping the traffic differently at each hop. The evasive techniques can also be detected. Our study aims to adapt some of the existing stepping stone detection and anti-evasion techniques to software-defined networks which use network function virtualization. We have implemented the stepping-stone detection techniques in a simulated environment and uses Flow for the traffic monitoring at the switches. We evaluate the detection algorithms on different network topologies and analyze the results to gain insight on the effectiveness of the detection mechanisms. The selected detection techniques work well on relatively high packet sampling rates. However, new solutions will be needed for large SDN networks where the packet sampling rate needs to be lower

Representation Learning for Spoken term Detection

Spoken Term Detection (STD) is the task of searching a given spoken query word in large speech database. Applications of STD include speech data indexing, voice dialling, telephone monitoring and data mining. Performance of STD depends mainly on representation of speech signal and matching of represented signal. This work investigates methods for robust representation of speech signal, which is invariant to speaker variability, in the context of STD task. Here the representation is in the form of templates, a sequence of feature vectors. Typical representation in speech community Mel-Frequency CepstralCoe cients (MFCC) carry both speech-specific and speaker-specific information, so the need for better representation. Searching is done by matching sequence of feature vectors of query and reference utterances by using Subsequence Dynamic Time Warping (DTW). The performance of the proposed representation is evaluated on Telugu broadcast news data. In the absence of labelled data i.e., in unsupervised setting, we propose to capture joint density of acoustic space spanned by MFCCs using Gaussian Mixture Models (GMM) and Gaussian-Bernoulli Restricted Boltzmann Machines (GBRBM). Posterior features extracted from trained models are used to search the query word. It is noticed that 8% and 12% improvement in STD performance compared to MFCC by using GMM and GBRBM posterior features respectively. As transcribed data is not required, this approach is optimal solution to low-resource languages. But due to it’s intermediate performance, this method cannot be immediate solution to high resource language

Estudi comparatiu de la publicació científica de la UPC i l’ETSETB vs. altres universitats d’àmbit europeu (2002-2012)

L'informe es centra en la publicació científica especialitzada en l'àmbit temàtic propi de l'ETSETB: l'enginyeria de telecomunicacions i l'electrònica. Es comparen indicadors bibliomètrics de la UPC i l'ETSETB amb els d'altres universitats europees amb activitat de recerca notable en l'àrea de les telecomunicacions i l'electrònica.Postprint (author’s final draft

Estudi comparatiu de la publicació científica de la UPC i l'ETSETB vs. altres universitats d'àmbit europeu (1999 - 2009)

L'informe es centra en la publicació científica especialitzada en l'àmbit temàtic propi de l'ETSETB: l'enginyeria de telecomunicacións i l'electrònica. Es comparen indicadors bibliomètrics de la UPC i l'ETSETB amb els d'altres universitats europees amb activitat de recerca notable en l'àrea de les telecomunicacions i l'electrònica