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Communications data such as traffic levels in a communications network is analysed using techniques adapted from the study of chaos. Future values of a series of communications data are predicted and an attractor structure is determined from the communications data. This enables the communications p

Communications data such as traffic levels in a communications network is analysed using techniques adapted from the study of chaos. Future values of a series of communications data are predicted and an attractor structure is determined from the communications data. This enables the communications processes to be monitored, controlled and analysed. Action can be taken to modify the communications process using the results from the prediction and attractor structure to reduce costs and improve performance and efficiency. These methods may also be used for product data from manufacturing processes. An algorithm bank is compiled containing prediction algorithms suitable for different types of data series, including those exhibiting deterministic behaviour and those exhibiting stochastic behaviour. Recent past values of a data series are taken and assessed or audited in order to determine which of the algorithms in the bank would provide the optimal prediction. The selected algorithm is then used to predict future values of the data series. The assessment or auditing process is carried out in real time and a prediction algorithm selected using a “smart switch” such that different algorithms are used for different stages in a given series as required. This enables good prediction of data series which change in nature over time to be obtained.

대표청구항 ▼

Communications data such as traffic levels in a communications network is analysed using techniques adapted from the study of chaos. Future values of a series of communications data are predicted and an attractor structure is determined from the communications data. This enables the communications p

Communications data such as traffic levels in a communications network is analysed using techniques adapted from the study of chaos. Future values of a series of communications data are predicted and an attractor structure is determined from the communications data. This enables the communications processes to be monitored, controlled and analysed. Action can be taken to modify the communications process using the results from the prediction and attractor structure to reduce costs and improve performance and efficiency. These methods may also be used for product data from manufacturing processes. An algorithm bank is compiled containing prediction algorithms suitable for different types of data series, including those exhibiting deterministic behaviour and those exhibiting stochastic behaviour. Recent past values of a data series are taken and assessed or audited in order to determine which of the algorithms in the bank would provide the optimal prediction. The selected algorithm is then used to predict future values of the data series. The assessment or auditing process is carried out in real time and a prediction algorithm selected using a “smart switch” such that different algorithms are used for different stages in a given series as required. This enables good prediction of data series which change in nature over time to be obtained. lter filtering at least a portion of the nonlinear distortion sequence from the first sequence to produce a second sequence; (d) an adder combining the first sequence and the second sequence; and (e) wherein the transmitter is further characterized by a symbol generator for generating the test symbol sequence, wherein the symbol generator is further characterized by: a symbol mapper for mapping the bit sequences to the test symbol sequence; anda shift register for generating a set of bit sequences, wherein the shift register is further characterized by:a linear feedback shift register having eleven delay elements to implement the generator polynomial:g(s)=1+x9+x11in GF(2), wherein GF(2) denotes the Galois field of 2, the generator polynomial outputting a pseudo-random bit sequence x0ngenerated according to g(x), with subscript n denoting a time index;the linear feedback shift register being updated once per symbol period and at each symbol period the linear feedback shift register being advanced by one bit and one new bit of sequence x0nbeing generated;the linear feedback register providing two additional pseudo-random bit sequences x1nand x2nbeing generated by the linear combinations of time delayed versions of x0n, wherein:x 1n=x0n−1⊕x0n−4x 2n=x0n−2⊕x0n−4and wherein ⊕ denotes a logical exclusive OR operation, the three bit sequences x 0n, x1nand x2nbeing mutually uncorrelated over a time period greater than the length of an impulse response of a transmitter under test, the three bit sequences x0n, x1nand x2nbeing combined into a 3-bit sequence xnwhich is used as an input to the symbol mapper to generate the test symbol sequence S(n), the 3-bit sequence xnbeing generated repeatedly. n the marking is an implanted coil in combination with external magnetic field coils which are positionally registered in a treatment room.9. The method as set forth in claim 7, wherein the marking includes implanted markers selected from the group consisting of surgical clips, wires and noble-metal pellets.10. The method as set forth in claim 1, wherein the steps of detecting and tracking movement of the target volume inside the patient include deducing a current position of the target volume inside the patient from a positional association between parameters easily detected during treatment and the target volume inside the patient.11. The method as set forth in claim 10, wherein the positional association is obtained by detecting a relationship between the easily detected parameters and breath-dependent movement of the target volume inside the patient prior to the treatment using a movement detector, said easily detected parameters being tracked during the treatment, and the current position of the treatment target being deduced therefrom.12. The method as set forth in claim 10, wherein detecting and tracking movement of the target volume inside the patient include detecting at least one of: movement of stick-on markers fixed to the patient said stick-on markers reflecting infrared light; changes in the patient's contours using interference patterns or polarised light; change in length of wire strain gauges, which change their electrical resistance according to length; spirometry or breath flux analysis; electromyography or change of electric potentials in muscles; and movement of one or more points on a surface of the patient, which is scanned mechanically and detected as co-ordinates. 13. The method as set forth in claim 10, wherein a relationship between the easily detected parameters and breath-dependent movement of the target volume inside the patient is detected prior to treatment by at least one of: a mobile or stationary x-ray apparatus; an x-ray apparatus comprising at least one x-ray source and at least one image recorder/detector; and a breath-controlled body-section image recording CT or MR apparatus, which is triggered by one of the easily detected parameters. 14. The method as set forth in claim 13, wherein the patient's breathing activity is detected (I) prior to treatment during a diagnostic, breath-triggered, body-section image recording method, and (ii) during the treatment by tracking the position of the marking fixed to the patient, the trajectories of the marking being detected both prior to and during the treatment between a point of maximum inhalation and a point of maximum exhalation, in order to position the patient by allocating said trajectories.15. The method as set forth in claim 13, wherein the patient's breathing activity is detected (i) prior to the treatment during a diagnostic, breath-triggered, body-section image recording method, and (ii) during the treatment by tracking the position of the marking fixed to the patient, and wherein the patient is positioned by means of a position tracking system.16. The method as set forth in claim 1, further comprising: detecting the patient's current breath phase; and tuning the breath compensation and breath phase to one another. 17. The method as set forth in claim 16, wherein detecting the patient's current breath phase includes: affixing at least one marking to the patient's body, said at least one marking moving along characteristic trajectories during breathing; detecting and storing co-ordinates of the extrema corresponding to maximum inhalation and exhalation, which describe a defined difference in each patient; comparing the stored co-ordinates with currently detected co-ordinates of the markings, wherein the degree of inhalation or breath phase respectively is defined on the basis of the comparing step, if the position of the patient remains unchanged; and if the position of the patient changes after the co- ordinates have been stored, instructing the patient to fully inhale and exhale, detecting and storing the trajectories of the markings and their extrema, the trajectories before and after the change in the patient's position being referenced and the breath phases being qualitatively compared with one another. 18. The method as set forth in claim 16, wherein detecting the patient's breath phase includes: tracking a position of a marking fixed to the patient; and re-adjusting the breath phase during the treatment if a predetermined breath phase is deviated from. 19. The method as set forth in claim 14 wherein said breath phase is re-adjusted by supplying at least one of (I) acoustic, (ii) visual, and (iii) haptic signals to the patient.