Aria Serial Number Lookup !!TOP!!
I acquired an ARIA acoustic guitar today. The owner thought it was probably 20 years old but was not sure. I think it is probably a basic model even though it has binding used. I have looked on the ARIA site with no luck how to read the serial number. Not sure if the 66 means 1966 or not as one other site suggested. Any assistance would be appreciated
Aria Serial Number Lookup
Probably no help, but my friend just sent me a shot of the back brace stamp and it is 612036 for this mid 80s Aria. So my earlier post that the guy I bought it from might be wrong; he must have bought it new in '86 or near the first of the year. This may not help either scribd.com/doc/75948875/Electr...er-Dating since your 8 digit number doesn't fit the format - it couldn't be a '66 since they didn't use serial numbers before the mid 70s. I would bet its after '87, and I have read before they started moving manufacturing to Korea. That usually throws the whole serial number thing out.
Fitbit sets its customers up to be victims of theft by 1. not putting serial numbers on the bits so that lost or stolen ones can never be returned to their rightful owners, even if recovered by police (learned this when I went to file a police report) and 2. not making it possible to deactivate a lost or stolen device, allowing anyone to activate it as if it were their own. My son's Blaze was stolen at a track meet - the watch band and charger were left behind. Since the bit is tiny, it can easily be slipped into a sock or the pocket of a pair of track shorts. There is no identifying information on it or in it. Anyone can take it and create a new account and start using it. You have absolutely NO recourse. The most Fitbit will do is offer 25% off a new one. I'm not even tempted. It should be illegal to sell a $200.00 device without a serial number. It is illegal in most states to sell an item from which the serial number was removed. Ebay doesn't allow the sale of such items, but something that never had one is apparently fair game. Go to Ebay and see how many Blazes are for sale without the original packaging (which is the only place you will find a serial number) and ask yourself how many were probably stolen. It boggles the mind that Fitbit has not addressed this issue.
Interesting. I have owned a basis peak and now a philips watch. Both of these product had a serial number you could find under the about on the watch. Never thought that my fitbit did not have a serial number. Oh well. that is indeed strange.
Your serial number ( which is the FCC ID) is located on the bottom of your fitbit watch box. Also, go to settings on your watch. Scroll down to Regulatory Info, tap on it. The U.S. FCC ID # will be there & if it is your watch it will match the FCC ID from the bottom of the watch box.
There is no serial number imprinted on the Blaze. And while the Bluetooth MAC is unique, I don't see it on the watch or web dashboard. So practically speaking, that makes it impossible for a user or law enforcement to identify stolen property without the cooperation of Fitbit.
Now about the serial number - still need it on the case and a convenient way to locate it in app or web dashboard if your Blaze has been stolen. Boxes get tossed or lost. But that is setting the bar too low - we need some type of activation lock to discourage theft because it can't be used if stolen.
Scoring of REM sleep based on polysomnographic recordings is a laborious and time-consuming process. The growing number of ambulatory devices designed for cost-effective home-based diagnostic sleep recordings necessitates the development of a reliable automatic REM sleep detection algorithm that is not based on the traditional electroencephalographic, electrooccolographic and electromyographic recordings trio. This paper presents an automatic REM detection algorithm based on the peripheral arterial tone (PAT) signal and actigraphy which are recorded with an ambulatory wrist-worn device (Watch-PAT100). The PAT signal is a measure of the pulsatile volume changes at the finger tip reflecting sympathetic tone variations. The algorithm was developed using a training set of 30 patients recorded simultaneously with polysomnography and Watch-PAT100. Sleep records were divided into 5 min intervals and two time series were constructed from the PAT amplitudes and PAT-derived inter-pulse periods in each interval. A prediction function based on 16 features extracted from the above time series that determines the likelihood of detecting a REM epoch was developed. The coefficients of the prediction function were determined using a genetic algorithm (GA) optimizing process tuned to maximize a price function depending on the sensitivity, specificity and agreement of the algorithm in comparison with the gold standard of polysomnographic manual scoring. Based on a separate validation set of 30 patients overall sensitivity, specificity and agreement of the automatic algorithm to identify standard 30 s epochs of REM sleep were 78%, 92%, 89%, respectively. Deploying this REM detection algorithm in a wrist worn device could be very useful for unattended ambulatory sleep monitoring. The innovative method of optimization using a genetic algorithm has been proven to yield robust results in the validation set.
Solar, geomagnetic, gravitational and seismic activities cause disturbances in the ionospheric region of upper atmosphere that may disrupt or lower the quality of space based communication, navigation and positioning system signals. These disturbances can be categorized with respect to their amplitude, duration and frequency. Typically in the literature, ionospheric disturbances are investigated with gradient based methods on Total Electron Content (TEC) data estimated from ground based dual frequency Global Positioning System (GPS) receivers. In this study, a fast algorithm is developed for the automatic detection of the variability in Slant TEC (STEC) data. STEC is defined as the total number of electrons on the ray path between the ground based receiver and GPS satellite in the orbital height of 20,000 km. The developed method, namely, Differential Rate of TEC (DROT), is based on Rate of Tec (ROT) method. ROT is widely used in the literature and it is usually applied to Vertical TEC (VTEC) that corresponds to the projection of STEC to the vertical direction along the ray path at the Ionospheric Pierce Point (IPP) using a mapping function. The developed DROT method can be defined as the normalized metric norm between the ROT and its baseband trend structure. In this study, the performance of DROT is determined using synthetic data with variable bounds on the parameter set of amplitude, frequency and duration of disturbance. It is observed that DROT method can detect disturbances in three categories. For DROT values less than 50%, there is no significant disturbance in STEC data. For DROT values between 50% and 70%, a medium scale disturbance can be observed. For DROT values over 70%, severe disturbances such as Large Scale Traveling Ionospheric Disturbances (LSTIDs) can be observed. DROT method is highly sensitive to the amplitude of the wave-like oscillations. For a disturbance amplitude as low as 1.01 TECU, the disturbances that have durations longer than or
Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.
Automatic weapon platform is one of the important research directions at domestic and overseas, it needs to accomplish fast searching for the object to be shot under complex background. Therefore, fast detection for given target is the foundation of further task. Considering that chest-shape target is common target of shoot practice, this paper treats chestshape target as the target and studies target automatic detection method based on Deformable Part Models. The algorithm computes Histograms of Oriented Gradient(HOG) features of the target and trains a model using Latent variable Support Vector Machine(SVM); In this model, target image is divided into several parts then we can obtain foot filter and part filters; Finally, the algorithm detects the target at the HOG features pyramid with method of sliding window. The running time of extracting HOG pyramid with lookup table can be shorten by 36%. The result indicates that this algorithm can detect the chest-shape target in natural environments indoors or outdoors. The true positive rate of detection reaches 76% with many hard samples, and the false positive rate approaches 0. Running on a PC (Intel(R)Core(TM) i5-4200H CPU) with C++ language, the detection time of images with the resolution of 640 480 is 2.093s. According to TI company run library about image pyramid and convolution for DM642 and other hardware, our detection algorithm is expected to be implemented on hardware platform, and it has application prospect in actual system.