Velocity estimation of moving ships using C-band SLC SAR data

A new algorithm for the velocity vector estimation of moving ships using Single Look Complex (SLC) SAR data in strip map acquisition mode is proposed. The algorithm exploits both amplitude and phase information of the Doppler decompressed data spectrum, with the aim to estimate both the azimuth antenna pattern and the backscattering coefficient as function of the look angle. The antenna pattern estimation provides information about the target velocity; the backscattering coefficient can be used for vessel classification. The range velocity is retrieved in the slow time frequency domain by estimating the antenna pattern effects induced by the target motion, while the azimuth velocity is calculated by the estimated range velocity and the ship orientation. Finally, the algorithm is tested on simulated SAR SLC data.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Controlo postural em adolescentes com défice cognitivo

Introdução: O controlo postural é base do movimento humano, e pode ser estudado através das tarefas como o levantar e o alcance. Nestas, observam-se frequentemente alterações neuromotoras em indivíduos com défice cognitivo. Objetivo: descrever as alterações na relação entre os segmentos corporais na sequência de movimento levantar-para-alcançar, em adolescentes com défice cognitivo, face à aplicação de um programa de intervenção em fisioterapia baseado no Conceito de Bobath/ Tratamento do Neurodesenvolvimento (TND). Métodos: antes e após a intervenção em fisioterapia, filmou-se as vistas lateral e posterior da sequência de movimento de levantar-para-alcançar, a qual foi posteriormente dividida em 5 fases, para a análise observacional e quantitativa. A análise quantitativa foi realizada através da distância entre tragus-acrómio, crista ilíaca-acrómio, espinha ilíaca postero-superior homolateral-T1, ângulos inferiores da omoplata e ângulo inferior da omoplata homolateral-T1, recorrendo-se ao software de Avaliação Postural - SAPo. Face à avaliação inicial e evolução dos participantes foram estabelecidos planos de intervenção, tendo em conta aspectos como o contexto, a tarefa e a motivação. Resultados: no geral, foram detetadas alterações na relação entre os segmentos corporais na análise observacional e quantitativa, após a intervenção. Conclusão: As alterações na relação entre os segmentos corporais poderão indicar uma possível reorganização motora.

Efeitos Imediatos da Mobilização com Movimento na Rotação Medial da Gleno-umeral em Jogadores de Andebol

Introdução: No andebol, o ombro é elevado numa amplitude superior a 90º e move-se com elevada velocidade de execução o que pode originar deslocação anterior da cabeça do úmero e diminuição da rotação medial. A técnica MWM pode ser uma mais valia na correção da falha posicional e recuperação da amplitude de movimento de rotação medial da articulação gleno-umeral. Objetivo: Este estudo teve como objetivo verificar os efeitos imediatos da técnica de MWM na amplitude de movimento de rotação medial da articulação gleno-umeral em jogadores de andebol. Métodos: O presente estudo, duplamente cego, é do tipo experimental. Foram incluídos no estudo 30 indivíduos do sexo masculino, jogadores de andebol, distribuídos, aleatoriamente, em dois grupos de 15, experimental e controlo. Em ambos os grupos foi avaliada a amplitude de movimento da rotação medial da gleno-umeral, em dois momentos, pré e pós intervenção. O grupo experimental foi submetido à técnica de MWM no movimento de rotação medial da gleno-umeral no membro dominante. Ao grupo de controlo, foi solicitada a realização do movimento ativo de rotação medial no membro dominante, o fisioterapeuta manteve os mesmos contactos manuais mas não aplicou pressão na cabeça do úmero. Para a comparação entre os grupos experimental e controlo recorreu-se ao teste de Mann-Whitney e para analisar diferenças entre os dois momentos, para cada grupo, foi utilizado o teste de Wilcoxon. Resultados: Foram encontradas diferenças significativas no grupo experimental e controlo, contudo essa diferença foi superior no grupo experimental. Após a intervenção, o grupo experimental apresentou amplitudes de rotação medial da gleno-umeral significativamente mais elevadas às do grupo de controlo (U=0,50; p <0,001). Conclusão: A técnica de MWM para rotação medial produziu um aumento significativo na amplitude desse movimento.

Matemática 100 stress: um projeto aberto no IPP

A Era Tecnológica em que nos vemos inseridos, cujos avanços acontecem a uma velocidade vertiginosa exige, por parte das Instituições de Ensino Superior (IES) uma atitude proactiva no sentido de utilização dos muitos recursos disponíveis. Por outro lado, os elementos próprios da sociedade da informação – flexibilidade, formação ao longo da vida, acessibilidade à informação, mobilidade, entre muito outros – atuam como fortes impulsionadores externos para que as IES procurem e analisem novas modalidades formativas. Perante a mobilidade crescente, que se tem revelado massiva, a aprendizagem tende a ser cada vez mais individualizada, visual e prática. A conjugação de várias formas/tipologias de transmissão de conhecimento, de métodos didáticos e mesmo de ambientes e situações de aprendizagem induzem uma melhor adaptação do estudante, que poderá procurar aqueles que melhor vão ao encontro das suas expetativas, isto é, favorecem um processo de ensino-aprendizagem eficiente na perspetiva da forma de aprender de cada um. A definição de políticas estratégicas relacionadas com novas modalidades de ensino/formação tem sido uma preocupação constante na nossa instituição, nomeadamente no domínio do ensino à distância, seja ele e-Learning, b-Learning ou, mais recentemente, “open-Learning”, onde se inserem os MOOC – Massive Open Online Courses (não esquecendo a vertente m-Learning), de acordo com as várias tendências europeias (OECD, 2007) (Comissão Europeia, 2014) e com os objetivos da “Europa 2020”. Neste sentido surge o Projeto Matemática 100 STRESS, integrado no projeto e-IPP | Unidade de e-Learning do Politécnico do Porto que criou a sua plataforma MOOC, abrindo em junho de 2014 o seu primeiro curso – Probabilidades e Combinatória. Pretendemos dar a conhecer este Projeto, e em particular este curso, que envolveu vários docentes de diferentes unidades orgânicas do IPP.

Stress Ratio Effects on the fatigue Properties of Biaxially Loaded Tubular FRP-Specimens – Experimental Study

In this work tubular fiber reinforced specimens are tested for fatigue life. The specimens are biaxially loaded with tension and shear stresses, with a load angle β of 30° and 60° and a load ratio of R=0,1. There are many factors that affect fatigue life of a fiber reinforced material and the main goal of this work is to study the effects of load ratio R by obtaining S-N curves and compare them to the previous works (1). All the other parameters, such as specimen production, fatigue loading frequency and temperature, will be the same as for the previous tests. For every specimen, stiffness, temperature of the specimen during testing, crack counting and final fracture mode are obtained. Prior to testing, a study if the literature regarding the load ratio effects on composites fatigue life and with that review estimate the initial stresses to be applied in testing. In previous works (1) similar specimens have only been tested for a load ratio of R=-1 and therefore the behaviour of this tubular specimens for a different load ratio is unknown. All the data acquired will be analysed and compared to the previous works, emphasizing the differences found and discussing the possible explanations for those differences. The crack counting software, developed at the institute, has shown useful before, however different adjustments to the software parameters lead to different cracks numbers for the same picture, and therefore a better methodology will be discussed to improve the crack counting results. After the specimen’s failure, all the data will be collected and stored and fibre volume content for every specimen is also determinate. The number of tests required to make the S-N curves are obtained according to the existent standards. Additionally are also identified some improvements to the testing machine setup and to the procedures for future testing.

Development of new biomaterials based on liquid crystalline phases of cellulose derivatives

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédica

Development of thermoresponsive (polysulfone/polyacrylonitrile) - based membranes for bioseparation processes

Mestrado integrado em Engenharia Química e Bioquímica

Development of cyclodextrin-hydrogel polymeric systems in scCO2 for drug delivery

Thesis for the Degree of Master of Science in Bioorganic Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Development of a piezoelectric biosensor based on PVDF films

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

A new biomimetic sensor for detecting carnitine, a potential biomarker in ovarian cancer

1st ASPIC International Congress

Strength improvement of adhesively-bonded scarf repairs in aluminium structures with external reinforcements

Adhesively bonded techniques are an attractive option to repair aluminium structures, compared to more traditional methods. Actually, as a result of the improvement in the mechanical characteristics of adhesives, adhesive bonding has progressively replaced the traditional joining methods. There are several bonded repair configurations, as single-strap, double-strap and scarf. Compared with strap repairs, scarf repairs have the advantages of a higher efficiency and the absence of aerodynamic disturbance. The higher efficiency is caused by the elimination of the significant joint eccentricities of strap repairs. Moreover, stress distributions along the bond length are more uniform, due to tapering of the scarf edges. The main disadvantages of this technique are the difficult machining of the surfaces, associated costs and requirement of specialised labour. This work reports on an experimental and numerical study of the tensile behaviour of two-dimensional (2D) scarf repairs of aluminium structures bonded with the ductile epoxy adhesive Araldite® 2015. The numerical analysis, by Finite Elements (FE), was performed in Abaqus® and used cohesive zone models (CZM) for the simulation of damage onset and growth in the adhesive layer, thus enabling the strength prediction of the repairs. A parametric study was performed on the scarf angle (α) and different configurations of external reinforcement (applied on one or two sides of the repair, and also different reinforcement lengths). The obtained results allowed the establishment of design guidelines for repairing, showing that the use of external reinforcements enables increasing α for equal strength recovery, which makes the repair procedure easier. The numerical technique was accurate in predicting the repairs’ strength, enabling its use for design and optimisation purposes.

Intraocular Pressure Correlates with Optic Nerve Sheath Diameter in Patients with Normal Tension Glaucoma

PURPOSE: 1. Identify differences in optic nerve sheath diameter (ONSD) as an indirect measure of intracranial pressure (ICP) in glaucoma patients and a healthy population. 2. Identify variables that may correlate with ONSD in primary open-angle glaucoma (POAG) and normal tension glaucoma (NTG) patients. METHODS: Patients with NTG (n = 46) and POAG (n = 61), and healthy controls (n = 42) underwent B-scan ultrasound measurement of ONSD by an observer masked to the patient diagnosis. Intraocular pressure (IOP) was measured in all groups, with additional central corneal thickness (CCT) and visual field defect measurements in glaucomatous patients. Only one eye per patient was selected. Kruskal-Wallis or Mann-Whitney were used to compare the different variables between the diagnostic groups. Spearman correlations were used to explore relationships among these variables. RESULTS: ONSD was not significantly different between healthy, NTG and POAG patients (6.09 ± 0.78, 6.03 ± 0.69, and 5.71 ± 0.83 respectively; p = 0.08). Visual field damage and CCT were not correlated with ONSD in either of the glaucoma groups (POAG, p = 0.31 and 0.44; NTG, p = 0.48 and 0.90 respectively). However, ONSD did correlate with IOP in NTG patients (r = 0.53, p < 0.001), while it did not in POAG patients and healthy controls (p = 0.86, p = 0.46 respectively). Patient's age did not relate to ONSD in any of the groups (p > 0.25 in all groups). CONCLUSIONS: Indirect measurements of ICP by ultrasound assessment of the ONSD may provide further insights into the retrolaminar pressure component in glaucoma. The correlation of ONSD with IOP solely in NTG patients suggests that the translaminar pressure gradient may be of particular importance in this type of glaucoma.

Antero-Medialisation of the Tibial Tubercle for Patellar Instability

We reviewed 19 patients (24 knees) with patellofemoral instability treated surgically with antero-medialisation of the tibial tubercle and lateral retinacular release. Twenty-two knees had recurrent patellar dislocation and two patellar subluxation. Lateral retinacular release was performed arthroscopically in 15 knees. Average follow-up was 52 (16-86) months. There was one postoperative haemarthrosis and one failed fixation, which needed surgical revision. The average Lysholm score improved from 63.3 to 98 and only one knee had persistent patello-femoral pain postoperatively. The patellar tilt angle improved from 9.4 degrees to 5.5 degrees . There were no redislocations. We find that the surgical technique produces a consistent correction of patellar instability, but long-term studies are needed to confirm whether it can prevent arthritic degeneration.