Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

Unsupervised hyperspectral signal subspace identification

Hyperspectral imaging sensors provide image data containing both spectral and spatial information from the Earth surface. The huge data volumes produced by these sensors put stringent requirements on communications, storage, and processing. This paper presents a method, termed hyperspectral signal subspace identification by minimum error (HySime), that infer the signal subspace and determines its dimensionality without any prior knowledge. The identification of this subspace enables a correct dimensionality reduction yielding gains in algorithm performance and complexity and in data storage. HySime method is unsupervised and fully-automatic, i.e., it does not depend on any tuning parameters. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Hyperspectral signal subspace estimation

Given an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Parametric analog signal amplification applied to nanoscale cmos wireless digital transceivers

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering by the Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia

Bacterial inter-species communication mediated by the autoinducer-2 signal

Dissertation presented to obtain the Ph.D degree in Biology by Universidade Nova de Lisboa, Instituto de Tecnologia Química e Biológica, Instituto Gulbenkian de Ciência.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

Effect of sialic acid loss on dendritic cell maturation

Sialic acids are key structural determinants and contribute to the functionality of a number of immune cell receptors. Previously, we demonstrated that differentiation of human dendritic cells (DCs) is accompanied by an increased expression of sialylated cell surface structures, putatively through the activity of the ST3Gal.I and ST6Gal.I sialyltransferases. Furthermore, DC endocytosis was reduced upon removal of the cell surface sialic acid residues by neuraminidase. In the present work, we evaluate the contribution of the sialic acid modifications in DC maturation. We demonstrate that neuraminidase-treated human DCs have increased expression of major histocompatibility complex (MHC) and costimulatory molecules, increased gene expression of specific cytokines and induce a higher proliferative response of T lymphocytes. Together, the data suggest that clearance of cell surface sialic acids contributes to the development of a T helper type 1 proinflammatory response. This postulate is supported by mouse models, where elevated MHC class II and increased maturation of specific DC subsets were observed in DCs harvested from ST3Gal.I(-/-) and ST6Gal.I(-/-) mice. Moreover, important qualitative differences, particularly in the extent of reduced endocytosis and in the peripheral distribution of DC subsets, existed between the ST3Gal.I(-/-) and ST6Gal.I(-/-) strains. Together, the data strongly suggest not only a role of cell surface sialic acid modifications in maturation and functionality of DCs, but also that the sialic acid linkages created by different sialyltransferases are functionally distinct. Consequently, with particular relevance to DC-based therapies, cell surface sialylation, mediated by individual sialyltransferases, can influence the immunogenicity of DCs upon antigen loading.

Myoelectric Signal Monitoring System

The Electromyography (EMG) is an important tool for gait analyzes and disorders diagnoses. Traditional methods involve equipment that can disturb the analyses, being gradually substituted by different approaches, like wearable and wireless systems. The cable replacement for autonomous systems demands for technologies capable of meeting the power constraints. This work presents the development of an EMG and kinematic data capture wireless module, designed taking into account power consumption issues. This module captures and converts the analog myoeletric signal to digital, synchronously with the capture of kinetic information. Both data are time multiplexed and sent to a PC via Bluetooth link. The work carried out comprised the development of the hardware, the firmware and a graphical interface running in an external PC. The hardware was developed using the PIC18F14K22, a low power family of microcontrollers. The link was established via Bluetooth, a protocol designed for low power communication. An application was also developed to recover and trace the signal to a Graphic User Interface (GUI), coordinating the message exchange with the firmware. Results were obtained which allowed validating the conceived system in static and with the subject performing short movements. Although it was not possible to perform the tests within more dynamic movements, it is shown that it is possible to capture, transmit and display the captured data as expected. Some suggestions to improve the system performance also were made.

Reconstruction of Large Defect of Foot with Extensive Bone Loss Exclusively Using a Latissimus Dorsi Muscle Free Flap: a Potential New Indication for this Flap

In cases of extensive damage to the foot, with significant bone loss, it is generally accepted that reconstruction must include bone flaps or grafts either in the emergency setting or subsequently. In this report, we describe the case of an 18-year-old student with an avulsion injury of the dorsum of his right foot. Consequently, he lost most of the soft tissue over the dorsum of the foot and the cuboid, navicular, and cuneiform bones. A latissimus dorsi free flap was used to reconstruct the defect. A functional pseudoarthrosis developed between the remaining bones of the foot, and the patient experienced satisfactory foot function after rehabilitation. For this reason, no additional reconstructive procedure was undertaken. This case suggests that it might be adequate to use the latissimus dorsi muscle flap more liberally than previously reported in the reconstruction of extensive defects of the dorsum of the foot, including cases with significant bone loss. This option could avoid the morbidity and inconvenience of a second surgery and the need to harvest a bone flap or graft.

Analysis of Natural and Artificial Phenomena Using Signal Processing and Fractional Calculus

In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Post-Treated Prostate Cancer: Normal Findings and Signs of Local Relapse on Multiparametric Magnetic Resonance Imaging

The use of multiparametric magnetic resonance imaging (mp-MRI) for prostate cancer has increased over recent years, mainly for detection, staging, and active surveillance. However, suspicion of recurrence in the set of biochemical failure is becoming a significant reason for clinicians to request mp-MRI. Radiologists should be able to recognize the normal post-treatment MRI findings. Fibrosis and atrophic remnant seminal vesicles after prostatectomy are often found and must be differentiated from local relapse. Moreover, brachytherapy, external beam radiotherapy, cryosurgery, and hormonal therapy tend to diffusely decrease the signal intensity of the peripheral zone on T2-weighted images (T2WI) due to the loss of water content, consequently mimicking tumor and hemorrhage. The combination of T2WI and functional studies like diffusion-weighted imaging and dynamic contrast-enhanced improves the identification of local relapse. Tumor recurrence tends to restrict on diffusion images and avidly enhances after contrast administration either within or outside the gland. The authors provide a pictorial review of the normal findings and the signs of local tumor relapse after radical prostatectomy, external beam radiotherapy, brachytherapy, cryosurgery, and hormonal therapy.

Novel FGFR1 Mutations in Kallmann Syndrome and Normosmic Idiopathic Hypogonadotropic Hypogonadism: Evidence for the Involvement of an Alternatively Spliced Isoform

OBJECTIVE: To determine the prevalence of fibroblast growth factor receptor 1 (FGFR1) mutations and their predicted functional consequences in patients with idiopathic hypogonadotropic hypogonadism (IHH). DESIGN: Cross-sectional study. SETTING: Multicentric. PATIENT(S): Fifty unrelated patients with IHH (21 with Kallmann syndrome and 29 with normosmic IHH). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Patients were screened for mutations in FGFR1. The functional consequences of mutations were predicted by in silico structural and conservation analysis. RESULT(S): Heterozygous FGFR1 mutations were identified in six (12%) kindreds. These consisted of frameshift mutations (p.Pro33-Alafs*17 and p.Tyr654*) and missense mutations in the signal peptide (p.Trp4Cys), in the D1 extracellular domain (p.Ser96Cys) and in the cytoplasmic tyrosine kinase domain (p.Met719Val). A missense mutation was identified in the alternatively spliced exon 8A (p.Ala353Thr) that exclusively affects the D3 extracellular domain of FGFR1 isoform IIIb. Structure-based and sequence-based prediction methods and the absence of these variants in 200 normal controls were all consistent with a critical role for the mutations in the activity of the receptor. Oligogenic inheritance (FGFR1/CHD7/PROKR2) was found in one patient. CONCLUSION(S): Two FGFR1 isoforms, IIIb and IIIc, result from alternative splicing of exons 8A and 8B, respectively. Loss-of-function of isoform IIIc is a cause of IHH, whereas isoform IIIb is thought to be redundant. Ours is the first report of normosmic IHH associated with a mutation in the alternatively spliced exon 8A and suggests that this disorder can be caused by defects in either of the two alternatively spliced FGFR1 isoforms.

Spectrum and Frequency of GJB2 Mutations in a Cohort of 264 Portuguese Nonsyndromic Sensorineural Hearing Loss Patients

OBJECTIVE: To assess the spectrum and prevalence of mutations in the GJB2 gene in Portuguese nonsyndromic sensorineural hearing loss (NSSHL) patients. DESIGN: Sequencing of the coding region, basal promoter, exon 1, and donor splice site of the GJB2 gene; screening for the presence of the two common GJB6 deletions. STUDY SAMPLE: A cohort of 264 Portuguese NSSHL patients. RESULTS: At least one out of 21 different GJB2 variants was identified in 80 (30.2%) of the 264 patients analysed. Two mutant alleles were found in 53 (20%) of these probands, of which 83% (44/53) harboured at least one c.35delG allele. Twenty-seven (10.2%) of the probands harboured only one mutant allele. Subsequent analysis revealed that the GJB6 deletion del(GJB6-D13S1854) was present in at least 7.4% (2/27) of the patients carrying only one mutant GJB2 allele. Overall, one in five (55/264) of the patients were diagnosed as having DFNB1-related NSSHL, of which the vast majority (53/55) harboured only GJB2 mutations. CONCLUSIONS: This study provides clear demonstration that mutations in the GJB2 gene are an important cause of NSSHL in Portugal, thus representing a valuable indicator as regards therapeutical and rehabilitation options, as well as genetic counseling of these patients and their families.