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The decoding procedure based on the B M algorithm is now introduced [6, 7]. Demonstration of its main properties can be found in [4]. The algorithm basically consists of nding the coef cients of the error-location polynomial, 1 , 2 , . . . , , whose roots then determine the error positions. The B M algorithm starts with the evaluation of the 2t syndrome vector components S = (s1 , s2 , . . . , s2t ), which then allow us to nd the coef cients 1 , 2 , . . . , of the error-location polynomial, whose roots are the inverses of the error-location numbers 1 , 2 , . . . , . In the case of a binary BCH code, the nal step is to perform error correction at the determined error positions, and in the case of an RS code or a non-binary BCH code, the error values at those positions have to be calculated, in order to nally perform error correction. As indicated above, the core of the B M algorithm is an iterative method for determining the error-location polynomial coef cients 1 , 2 , . . . , . The algorithm proceeds as follows [2]: The rst step is to determine a minimum-degree (1) polynomial BM (X ) that satis es the rst Newton identity described in (45). Then the second (1) Newton identity is tested. If the polynomial BM (X ) satis es the second Newton identity in (2) (1) (45), then BM (X ) = BM (X ). Otherwise the decoding procedure adds a correction term to

Molecular characterization of Lactobacillus species isolated from naturally fermented sausages produced in Greece, Hungary and Italy Food Microbiology 22:19 28 Rantsiou, K, L Iacumin, C Cantoni, G Comi, and L Cocolin 2005a Ecology and characterization by molecular methods of Staphylococcus species isolated from fresh sausages International Journal of Food Microbiology 97:277 284 Rebecchi, A, S Crivori, P G Sarra, and P S Cocconcelli 1998 Physiological and molecular techniques for the study of bacterial community development in sausage fermentation Journal of Applied Microbiology 84:1043 1049..

At midday on a sunny day, the incident light on the Earth s surface has a power density of roughly 100 mW/cm2. Single-crystal silicon solar cells exhibit ef ciencies of

Millon s Evolutionary-Based Model of Interrelated Domains and Psychosynergy or Personality-Guided Therapy

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should have a higher priority than IEEE 802.11b data type MAC protocol data units (MPDUs) while an IEEE 802.11b acknowledgment (ACK) MPDU should have a higher priority than all IEEE 802.15.1 packets. On the other hand, in the randomized scheme, the priority of packets might be assigned in a randomized fashion. For this purpose, a random variable r uniformly distributed between [0, 1] and a threshold T (0 r T o 1) are used. If the incoming packet is from an IEEE 802.11b device, a priority of 2 is assigned to it if the random number r is smaller than T. Otherwise, a priority of 0 is assigned. If the incoming packet is from an IEEE 802.15.1 device, a priority of 1 is assigned [1].

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We assume that the wireless sensor network is connected to the outside world (e.g., point of command and control, the Internet, etc.) through a sink. The sink may or may not be collocated with the sensors in the deployment area. In case of a noncollocated sink, the interface with the outside world may be achieved by a vehicle driving by the area of deployment, or a helicopter, aircraft, or low earth orbit (LEO) satellite over ying the sensor network, and collecting information from a select group of reporting nodes. In such scenarios communication between individual sensors is by radio, while the reporting nodes are communicating with the noncollocated sink by radio, infrared, or laser [8,9]. One can easily contemplate a collection of mobile sinks for fault tolerance. When the sink is collocated with the wireless sensor network, it can also be in charge of performing any necessary training and maintenance operations. Throughout this chapter we shall assume that the sink is collocated with the sensors, and we shall refer to it occasionally as training agent (TA, for short), especially in contexts

3. To allocate these funds effectively to the various assets of the company, again with the company s goals in mind. Only with clearly stated goals can an organization effectively manage its finances. Without them, a business operates without a plan. In a small owneroperated enterprise, a goal may be expressed in simple terms, such as that the owner wishes to make enough net income in the first 11 months of the year to take a vacation in the twelfth month. In a large or chain operation, goals would be established in a much more formal way by the board of directors. Goals are frequently expressed in monetary terms. Some of these financially measurable goals are discussed next.

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