Abstract: Electronic scan circuitry includes a decompressor (510), a plurality of scan chains (520.i) fed by the decompressor (510), a scan circuit (502, 504) coupled to the plurality of scan chains (520.i) to scan them in and out, a masking circuit (590) fed by the scan chains (520.i), and a scannable masking qualification circuit (550, 560, 580) coupled to the masking circuit (590), the masking qualification circuit (550, 560, 580) scannable by scan-in of bits by the decompressor (510) along with scan-in of the scan chains (520.i), and the scannable masking qualification circuit (550, 560, 580) operable to hold such scanned-in bits upon scan-out of the scan chains through the masking circuit (590). Other scan circuitry, processes, circuits, devices and systems are also disclosed.

Abstract: A system and method for predicting one or more cellular performance parameters associated with user equipment (UE) within a three-dimensional (3D) space having one or more cellular nodes, the cellular nodes including one or more cellular nodes, including a 5G cellular node. For each of one or more of pieces of UE within the 3D space, determine values associated with one or more UE-side features of each piece of UE. Predict values of the one or more cellular performance parameters for each UE as a function of the values associated with the one or more UE-side features of each respective piece of UE, wherein predicting values of the one or more cellular performance parameters includes applying the values determined for each respective piece of UE to a machine learning module trained using truth data associated with the one or more UE-side features.

Abstract: Sequences of computer network log entries indicative of a cause of an event described in a first type of entry are identified by training a long short-term memory (LSTM) neural network to detect computer network log entries of a first type. The network is characterized by a plurality of ordered cells Fi=(xi, ci-1, hi-1) and a final sigmoid layer characterized by a weight vector wT. A sequence of log entries xi is received. An hi for each entry is determined using the trained Fi. A value of gating function Gi(hi, hi-1)=II (wT(hi?hi-1)+b) is determined for each entry. II is an indicator function, b is a bias parameter. A sub-sequence of xi corresponding to Gi(hi, hi-1)=1 is output as a sequence of entries indicative of a cause of an event described in a log entry of the first type.

Abstract: Sequences of computer network log entries indicative of a cause of an event described in a first type of entry are identified by training a long short-term memory (LSTM) neural network to detect computer network log entries of a first type. The network is characterized by a plurality of ordered cells Fi=(xi, ci-1, hi-1) and a final sigmoid layer characterized by a weight vector wT. A sequence of log entries xi is received. An hi for each entry is determined using the trained Fi. A value of gating function Gi(hi, hi-1)=II (wT(hi?hi-1)+b) is determined for each entry. II is an indicator function, b is a bias parameter. A sub-sequence of xi corresponding to Gi(hi, hi-1)=1 is output as a sequence of entries indicative of a cause of an event described in a log entry of the first type.

Abstract: Disclosed is an inductor structure. The inductor structure includes a base material, at least one bottom spiral conductor disposed on the base material, a middle spiral conductor disposed on the bottom spiral conductor, a top spiral conductor disposed on the middle spiral conductor, and dielectric material separating the bottom, middle and top spiral conductors. The at least one bottom spiral conductor is connected electrically in parallel to the middle spiral conductor and the middle spiral conductor is connected electrically in series to the top spiral conductor. The top spiral conductor is thicker, narrower and less tightly wound than the middle spiral conductor and the bottom spiral conductor.

Abstract: Disclosed is an inductor structure. The inductor structure includes a base material, at least one bottom spiral conductor disposed on the base material, a middle spiral conductor disposed on the bottom spiral conductor, a top spiral conductor disposed on the middle spiral conductor, and dielectric material separating the bottom, middle and top spiral conductors. The at least one bottom spiral conductor is connected electrically in parallel to the middle spiral conductor and the middle spiral conductor is connected electrically in series to the top spiral conductor. The top spiral conductor is thicker, narrower and less tightly wound than the middle spiral conductor and the bottom spiral conductor.