What we claim is:
1. A manually guided implement having an air-cooled internal combustion engine for driving a tool, said implement comprising:
an implement housing in which said internal combustion engine is disposed, wherein said housing has a first air inlet;
a cooling air spiral case disposed in said implement housing and open toward said internal combustion engine, wherein ambient air can be drawn into said cooling air spiral case via said first air inlet for conveyance to said internal combustion engine; and
a fan wheel fixedly held on a crankshaft of said internal combustion engine and accommodated in said cooling air spiral case, wherein a second air inlet is formed in a base of said cooling air spiral case that is disposed between said fan wheel and said internal combustion engine, wherein said second air inlet provides communication between an intake chamber of said implement housing and portions of said cooling air spiral case that convey cooling air.
2. An implement according to claim 1, wherein said second air inlet is disposed on a side of said crankshaft that is remote from a cylinder of said internal combustion engine.
3. An implement according to claim 1, wherein said fan wheel has two end faces, each of which is provided with a respective suction ring of vanes.
4. An implement according to claim 3, wherein said fan wheel is provided with a radially extending partition that separates said suction rings of vanes.
5. An implement according to claim 4, wherein an outwardly disposed first one of said suction rings of vanes as viewed relative to said internal combustion engine, has a greater axial width than does the other inwardly disposed one of said suction rings of vanes.
6. An implement according to claim 1, wherein said fan wheel is disposed on a driver of said crankshaft that drives said tool.
7. An implement according to claim 1, wherein a fan cover is held on said implement housing and covers said cooling air spiral case wherein said first air inlet, as a plurality of inlet openings, are provided in said fan cover for air intake.
8. An implement according to claim 7, wherein said fan cover has a pot-shaped configuration and accommodates therein a clutch that cooperates with said crankshaft, wherein said first inlet openings are disposed on a periphery of said fan cover at the level of said clutch.
9. An implement according to claim 1, wherein an intake chamber of said implement housing is disposed between a crankcase of said internal combustion engine and said housing, and wherein said intake chamber is in flow communication with at least one-third inlet opening provided in said housing.
10. An implement according to claim 9, wherein said at least one-third inlet opening is disposed on a side of said internal combustion engine that is remote from said cooling air spiral case.
11. An implement according to claim 10, wherein a starter cover is disposed on said implement housing, wherein said at least one-third inlet opening is disposed in said starter cover, and wherein said starter cover accommodates a shaft pin of said crankshaft that is disposed opposite a driver thereof and also accommodates a starter mechanism for starting said internal combustion engine, that cooperates with said shaft pin.
12. An implement according to claim 1, wherein a flow path within said implement housing to said second air inlet in said base of said cooling air spiral case is delimited in stages by a fuel tank of said internal combustion engine.
13. An implement according to claim 9, wherein in the vicinity of a carburetor of said internal combustion engine, a flow access is provided to said intake chamber in said implement housing.
14. An implement according to claim 1, wherein said internal combustion engine is a four-stroke engine.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1.-105. (canceled)
106. A method for determining an absolute central aortic pressure waveform comprising:
measuring peripheral artery pressure signals or related signals at more than one peripheral location in an arterial tree of a subject;
modeling the arterial tree as a single input, multi-output system in which each output corresponds to one of the measured signals;
analyzing the measured signals so as to estimate a common input of the system to within an arbitrary scale factor; and
determining an absolute central aortic pressure waveform by scaling the estimated input based on the measured signals.
107. The method of claim 106 wherein channels of the single-input, multi-output system model of the arterial tree are first estimated to within an arbitrary scale factor and then an inverse of the estimated channels is applied to the measured signals so as to reconstruct the common input to within an arbitrary scale factor.
108. The method of claim 106 wherein channels of the single-input, multi-output system model of the arterial tree are characterized by linear and time-invariant impulse responses that are coprime.
109. The method of claim 108 wherein analyzing the measured signals further comprises applying multi-channel blind system identification to the measured signals to estimate parameters and order of the impulse responses and thereby estimate the impulse responses to within an arbitrary scale factor; and estimating the input of the system to within an arbitrary scale factor by deconvolution.
110. The method of claim 109 further comprising the representation of the impulse responses with a set of basis functions with unknown parameters prior to the step of applying multi-channel blind system identification.
111. The method of claim 110 wherein the basis functions are truncated exponential varying sinusoids or truncated polynomials for finite impulse responses.
112. The method of claim 110 wherein the basis functions are complex exponentials for infinite impulse responses.
113. The method of claim 110 wherein the number of basis functions takes on an assumed value or is determined.
114. The method of claim 113 wherein the number of basis functions is determined through mean squared error analysis.
115. The method of claim 109 wherein a subset of the unknown parameters of the impulse responses takes on assumed values.
116. The method of claim 109 wherein the parameters and order of the impulse responses are estimated based on cross relations between pairs of measured signals.
117. The method of claim 109 wherein the parameters and order of the impulse responses are estimated based on properties of a channel subspace.
118. The method of claim 109 wherein the parameters of the impulse responses are estimated using least squares methods.
119. The method of claim 109 wherein the parameters of the impulse responses are estimated with the eigenvector method, an iterative two-step maximum likelihood method, an adaptive neural network, or a numerical search.
120. The method of claim 109 wherein the orders of the impulse responses take on assumed values or are determined from the measured signals.
121. The method of claim 120 wherein a maximum order of the impulse responses is determined.
122. The method of claim 120 wherein the orders are determined from the signals by singular value analysis, cross validation, cross validation-based criteria, or information-based criteria.
123. The method of claim 109 wherein single channel deconvolution is applied to one or more of the estimated impulse responses and corresponding measured output signals so as to result in multiple versions of the common input to within an arbitrary scale factor.
124. The method of claim 123 wherein the single channel deconvolution is achieved with Fourier methods or least squares methods.
125. The method of claim 123 further comprising selecting one of the multiple versions as the common input to within an arbitrary scale factor.
126. The method of claim 123 further comprising using an average or median of at least some of the multiple versions as the common input to within an arbitrary scale factor.
127. The method of claim 109 wherein multi-channel deconvolution is applied to one or more of the estimated impulse responses and corresponding measured signals so as to result in a single common input to within an arbitrary scale factor.
128. The method of claim 127 wherein the multi-channel deconvolution is achieved based on Bezout’s theorem or least squares methods.
129. The method of claim 108 wherein the common input of the system is estimated to within an arbitrary scale factor in one step by applying multi-channel blind system identification to the measured signals.
130. The method of claim 129 wherein the estimation is achieved with the input subspace method.
131. The method of claim 10606 wherein the estimated input is scaled to have a mean value equal to that of one of the measured signals.
132. The method of claim 106 wherein the estimated input is scaled to have a mean value equal to that of the measured signal with the largest mean value.
133. The method of claim 106 wherein the estimated input is scaled to have a mean value equal to the mean or median of the mean values of select measured signals.
134. The method of claim 106 further comprising scaling the estimated impulse responses to have unity gain.
135. The method of claim 134 wherein the scaled estimated impulse responses are utilized to monitor local arterial functioning.
136. A method for determining an absolute central aortic pressure waveform comprising steps of:
measuring peripheral artery pressure waveforms or related signals at more than one peripheral location in an arterial tree of a subject;
modeling the arterial tree as a single input, multi-output system in which each output corresponds to one of the measured signals;
constraining a property of the system;
analyzing the signals so as to estimate the absolute common input of the constrained system; and
determining the absolute central aortic pressure waveform from the estimated input.
137. The method of claim 136 wherein channels of the constrained single-input, multi-output system model of the arterial tree are first estimated and then an inverse of the estimated channels is applied to the measured signals so as to reconstruct the absolute common input.
138. The method of claim 136 wherein channels of the single-input, multi-output system model of the arterial tree are characterized by linear and time-invariant impulse responses that are coprime.
139. The method of claim 136 wherein one or more of the channels are constrained to unity gain.
140. The method of claim 138 wherein analyzing the measured signals further comprises applying multi-channel blind system identification to the measured signals to estimate parameters and order of the impulse responses and thereby estimate the absolute impulse responses; and estimating the absolute input of the constrained system by deconvolution.
141. The method of claim 140 further comprising the representation of the impulse responses with a set of basis functions with unknown parameters prior to the step of applying multi-channel blind system identification.
142. The method of claim 141 wherein the basis functions are truncated exponential varying sinusoids or truncated polynomials for finite impulse responses.
143. The method of claim 141 wherein the basis functions are complex exponentials for infinite impulse responses.
144. The method of claim 141 wherein the number of basis functions takes on an assumed value or is determined.
145. The method of claim 144 wherein the number of basis functions is determined through mean squared error analysis.
146. The method of claim 145 wherein a subset of unknown parameters of the impulse responses takes on assumed values.
147. The method of claim 140 wherein the parameters and order of the impulse responses are estimated based on cross relations between pairs of measured signals.
148. The method of claim 140 wherein the parameters and order of the impulse responses are estimated based on properties of channel subspace.
149. The method of claim 140 wherein the parameters of the impulse responses are estimated using least squares methods.
150. The method of claim 140 wherein the parameters of the impulse responses are estimated with an eigenvector method, an iterative two-step maximum likelihood method, an adaptive neural network, or a numerical search.
151. The method of claim 140 wherein the orders of the impulse responses take on assumed values or are determined from the measured signals.
152. The method of claim 151 wherein a maximum order of the impulse responses is determined.
153. The method of claim 151 wherein the orders are determined from the measured signals by singular value analysis, cross validation, cross validation-based criteria, or information-based criteria.
154. The method of claim 140 wherein single channel deconvolution is applied to one or more of the estimated impulse responses and corresponding measured signals so as to result in multiple versions of the absolute common input.
155. The method of claim 154 wherein the single channel deconvolution is achieved with Fourier methods or least squares methods.
156. The method of claim 154 further comprising selecting one of the multiple versions as the absolute common input.
157. The method of claim 154 further comprising using an average or median of at least some of the multiple versions as the absolute common input.
158. The method of claim 130 wherein multi-channel deconvolution is applied to one or more of the estimated absolute impulse responses and corresponding measured output signals so as to result in a single absolute common input.
159. The method of claim 158 wherein the multi-channel deconvolution is achieved based on Bezout’s theorem or least squares methods.
160. The method of claim 138 wherein the absolute common input of the constrained system is estimated in one step by applying multi-channel blind system identification to the measured signals.
161. The method of claim 160 wherein the estimation is achieved with the input subspace method.
162. The method of claim 136, wherein the estimated absolute impulse responses are utilized to monitor local arterial functioning.
163. The method of claim 136 wherein an absolute central aortic pressure waveform is determined as the estimated common input.