Comportamento motor do tronco e membro superior, num sistema de posicionamento rígido e dinâmico, durante atividade

A funcionalidade dos indivíduos com Paralisia Cerebral está muitas vezes comprometida devido às alterações do movimento e do controlo postural. Dadas estas alterações, a posição de sentado oferece uma maior estabilidade sendo muitas das atividades de vida diária desempenhadas nesta posição. O objetivo mais importante de intervenção é obter o máximo de funcionalidade na posição de sentado, particularmente do membro superior. Este objectivo, na maioria das vezes, só pode ser atingido com o uso de sistemas de posicionamento que tentam colmatar as alterações posturais e do movimento. Assim, o objetivo deste estudo de caso é verificar se existem diferenças no comportamento motor do tronco e do membro superior, com um sistema de posicionamento rígido e com um sistema de posicionamento dinâmico, numa jovem com Paralisia Cerebral, aquando da ativação manual de um switch. Foi realizado um estudo de caso único em que foi feita uma análise cinemática do movimento do tronco e membro superior na ativação de um switch BigMack, em três posições de teste com distâncias diferentes. Simultaneamente mediu-se a distribuição do peso durante o movimento, através do mapa de pressão e foi registada, bilateralmente a atividade dos músculos trapézio (porção média), longuíssimo, recto abdominal e oblíquo externo. Os resultados obtidos apontam, neste caso em particular, para uma melhoria na qualidade do movimento e da distribuição de peso, com o sistema de posicionamento dinâmico, sem diferenças entre os dois sistemas relativamente à ativação muscular.

Exploiting a prioritized MAC protocol to efficiently compute min and max in multihop networks

Consider a wireless sensor network (WSN) where a broadcast from a sensor node does not reach all sensor nodes in the network; such networks are often called multihop networks. Sensor nodes take sensor readings but individual sensor readings are not very important. It is important however to compute aggregated quantities of these sensor readings. The minimum and maximum of all sensor readings at an instant are often interesting because they indicate abnormal behavior, for example if the maximum temperature is very high then it may be that a fire has broken out. We propose an algorithm for computing the min or max of sensor reading in a multihop network. This algorithm has the particularly interesting property of having a time complexity that does not depend on the number of sensor nodes; only the network diameter and the range of the value domain of sensor readings matter.

Task assignment algorithms for two-type heterogeneous multiprocessors

Consider the problem of assigning implicit-deadline sporadic tasks on a heterogeneous multiprocessor platform comprising two different types of processors—such a platform is referred to as two-type platform. We present two low degree polynomial time-complexity algorithms, SA and SA-P, each providing the following guarantee. For a given two-type platform and a task set, if there exists a task assignment such that tasks can be scheduled to meet deadlines by allowing them to migrate only between processors of the same type (intra-migrative), then (i) using SA, it is guaranteed to find such an assignment where the same restriction on task migration applies but given a platform in which processors are 1+α/2 times faster and (ii) SA-P succeeds in finding a task assignment where tasks are not allowed to migrate between processors (non-migrative) but given a platform in which processors are 1+α times faster. The parameter 0<α≤1 is a property of the task set; it is the maximum of all the task utilizations that are no greater than 1. We evaluate average-case performance of both the algorithms by generating task sets randomly and measuring how much faster processors the algorithms need (which is upper bounded by 1+α/2 for SA and 1+α for SA-P) in order to output a feasible task assignment (intra-migrative for SA and non-migrative for SA-P). In our evaluations, for the vast majority of task sets, these algorithms require significantly smaller processor speedup than indicated by their theoretical bounds. Finally, we consider a special case where no task utilization in the given task set can exceed one and for this case, we (re-)prove the performance guarantees of SA and SA-P. We show, for both of the algorithms, that changing the adversary from intra-migrative to a more powerful one, namely fully-migrative, in which tasks can migrate between processors of any type, does not deteriorate the performance guarantees. For this special case, we compare the average-case performance of SA-P and a state-of-the-art algorithm by generating task sets randomly. In our evaluations, SA-P outperforms the state-of-the-art by requiring much smaller processor speedup and by running orders of magnitude faster.