Abstract: Wearable items and methods of monitoring wearable items are disclosed. The wearable item comprises a flexible base material forming at least a portion of the wearable item, plural conductive traces traversing the flexible base material, and conductivity sensing circuitry coupled to the plural conductive traces. The conductivity sensing circuitry is configured to distinguish conductivity from non-conductivity of the plural conductive traces, and configured to generate a conductivity indication for at least one of the plural conductive traces. The plural conductive traces follow indirect paths across the flexible base material, allowing the flexible material to flex and stretch normally without breaking the conductive traces.

Abstract: Apparatuses and methods of operating apparatuses are disclosed. An apparatus comprises a flexible substrate and circuitry fabricated on the flexible substrate to perform data processing. At least one strain detector generates a strain signal which is dependent on a flexing state of the strain detector on the flexible substrate. A strain history control unit samples the at least one strain signal from the at least one strain detector at a plurality of time points and records a strain snapshot at each time point comprising data dependent on the at least one strain signal from the at least one strain detector. The data processing performed by the circuitry is dependent on the plurality of strain snapshots recorded.

Abstract: Wearable devices, systems of wearable devices, and methods of operating the same are disclosed. A first wearable device worn in contact with the user’s skin monitors the user and comprises a transmission electrode in contact with the user’s skin. A second wearable device comprises a reception electrode worn in contact with the user’s skin. The first wearable device can apply an alert signal to the transmission electrode and measures a transmission current at the transmission electrode. The second wearable device monitors an electrical status of the reception electrode and when the alert signal is detected applies an alert response signal to the receiver electrode. The first wearable device identifies application of the alert response signal to the receiver electrode by measurement of a variation of the transmission current at the transmission electrode whilst the alert signal is applied to the transmission electrode.

Abstract: There is provided a battery cell monitoring system comprising a flexible substrate able to conform to a surface of a battery cell to be monitored and wireless communication circuitry to be positioned proximate to a surface of the battery cell and arranged to communicate with one or more other battery cell monitoring systems. The battery cell monitoring system is provided with control circuitry integrated onto the flexible substrate to control the wireless communication circuitry to perform two types of communication. The first of the two types of communication is a local communication between the battery cell monitoring system and each of the one or more other battery cell monitoring systems. The second of the two types of communication is a non-local communication between the battery cell monitoring system and a battery management system routed via inter-cell communication with the one or more other battery cell monitoring systems.

Abstract: Methods of performing post-manufacturing adaptation of a data processing apparatus manufactured in accordance with a processor design and corresponding data processing apparatus configurations are provided. Post-manufacturing testing of the data processing apparatus determines any dysfunctional instructions by comparison between component usage profiles for each instruction and a component fault-detection procedure applied to the data processing apparatus. The data processing apparatus can be determined nevertheless to be operationally viable when any dysfunctional instructions can be substituted for by emulation using other functional instructions. The data processing apparatus can be provided with dysfunctional instruction handling circuitry configured to identify occurrence of a program instruction instance of a dysfunctional instruction and to invoke an interrupt handling routine associated with the dysfunctional instruction to emulate the instance of a dysfunctional instruction.

Abstract: A battery cell monitoring system comprises a flexible substrate able to conform to a surface of a battery cell to be monitored, and a plurality of first-level prediction units integrated onto the flexible substrate, where each first-level prediction unit is positioned at a different location on the flexible substrate to each other first-level prediction unit. Each first-level prediction unit comprises at least one sensor to generate sensor signals indicative of a physical state of the battery cell, and first-level prediction circuitry to generate a predicted battery cell status value in dependence on the sensor signals received from the at least one sensor of that first-level prediction unit.

Abstract: Wearable items and methods of monitoring wearable items are disclosed. The wearable item comprises a flexible base material forming at least a portion of the wearable item, plural conductive traces traversing the flexible base material, and conductivity sensing circuitry coupled to the plural conductive traces. The conductivity sensing circuitry is configured to distinguish conductivity from non-conductivity of the plural conductive traces, and configured to generate a conductivity indication for at least one of the plural conductive traces. The plural conductive traces follow indirect paths across the flexible base material, allowing the flexible material to flex and stretch normally without breaking the conductive traces.

Abstract: Battery cell monitoring systems comprising a flexible substrate and components integrated onto the flexible substrate, and methods of operating the same are disclosed. The components comprise a computing device and at least one sensor, where the at least one sensor is configured to generate sensor signals indicative of a physical state of the battery cell. The computing device is configured to hold characteristic data values which have been generated based on prior sensor signals. The computing device is configured to receive the sensor signals from the at least one sensor and to generate battery cell status data in dependence on the sensor signals and the characteristic data values.

Abstract: Packaging for a pharmaceutical product and a method of validating pharmaceuticals is disclosed. The packaging comprising a cavity; and validation circuitry comprising: a sensor adapted to output a signal in response to detecting a vapour in the cavity; and a detection circuit adapted to output an indication when the signal indicates a predefined vapour signature.

Abstract: Aspects of the present disclosure relate to an apparatus comprising: a substrate; communication circuitry deposited on said substrate; and ballot circuitry deposited on said substrate. The ballot circuitry comprises: a plurality of voting circuitry elements, each voting circuitry element being responsive to a voting operation to change a conductive state of that voting circuitry element; and logic circuitry communicatively coupled with each of the plurality of voting circuitry elements and with the communication circuitry. The logic circuitry is configured to: detect the conductive state of each of the plurality of voting circuitry elements; and transmit, via the communication circuitry and based on the conductive state of each of the plurality of voting circuitry elements, a voting result.

Abstract: Apparatus comprises at least one visual indicator element; at least one detector to detect access to the apparatus consistent with a cleaning operation being applied to a surface of the apparatus; and processing circuitry to control a visual indication state of the at least one visual indicator element in response to a detection by the detector of access to the surface of the apparatus.

Abstract: Aspects of the present disclosure relate to an apparatus comprising: a substrate; communication circuitry deposited on said substrate; and ballot circuitry deposited on said substrate. The ballot circuitry comprises: a plurality of voting circuitry elements, each voting circuitry element being responsive to a voting operation to change a conductive state of that voting circuitry element; and logic circuitry communicatively coupled with each of the plurality of voting circuitry elements and with the communication circuitry. The logic circuitry is configured to: detect the conductive state of each of the plurality of voting circuitry elements; and transmit, via the communication circuitry and based on the conductive state of each of the plurality of voting circuitry elements, a voting result.

Abstract: Apparatuses and methods for supporting class prediction based on multiple items of feature data are provided. Learning phase training data with known classification are used as inputs. Each event of the training data maps multiple items of feature data to encodings, where a range of values for each feature input are mapped to a given encoding. The concatenated encoding for the event form a joint feature item. Class counters are used to count class known to associated with the training event for the joint feature item in a table. At the conclusion of the training phase the class counter values enable a predicted class to be associated with each joint feature item in the table. In the inference phase the table is used for class prediction generation for new data events. The inference phase may be implemented in hardware which has less data handling capability than in the learning phase.

Abstract: A safety-based prediction apparatus, system and method are provided. A machine learning hardware accelerator (MLHA) includes a main classifier (MC) module, at least one guardian classifier (GC) module, and a final predicted class decision module. The MC module predicts an MC predicted class based on input data, and includes a pre-trained, machine learning main classifier (MLMC) that has at least one safety critical (SC) class and a plurality of non-SC classes. Each guardian classifier (GC) module is associated with an SC class, and predicts a GC predicted class based on the input data. Each GC module includes a pre-trained, machine learning guardian classifier (MLGC) having two classes including an associated SC class and a residual class that includes any non-associated SC classes and the plurality of non-SC classes. A decision module determines and outputs a final predicted class based on the MC predicted class and each GC predicted class.

Abstract: An apparatus comprises a plurality of redundant processing units to perform data processing redundantly in lockstep; common mode fault detection circuitry to detect an event indicative of a potential common mode fault affecting each of the plurality of redundant processing units; a memory shared between the plurality of redundant processing units; and memory checking circuitry to perform a memory scanning operation to scan at least part of the memory for errors; in which the memory checking circuitry performs the memory scanning operation in response to a common mode fault signal generated by the common mode fault detection circuitry indicating that the event indicative of a potential common mode fault has been detected.

Abstract: Data processing apparatus having a binary neural network, BNN, circuitry to implement a BNN; the BNN circuitry having at least one instance of hidden layer circuitry responsive to trained one-bit weight values and input data values to generate a hidden layer output signal; each input data value has a one-hot n-bit data value, where n is an integer greater than one; the hidden layer circuitry is configured to generate the hidden layer output signal dependent upon an intermediate result of a selective inversion operation applied to each bit of a given input data value; the hidden layer circuitry has circuitry to generate a respective intermediate result as a first predetermined result value for the given input data value; and, for a group of trained one-bit weight values, circuitry to generate a respective intermediate result as a second predetermined result value for the given input data value.

Abstract: Apparatuses and methods of operating apparatuses are disclosed. An apparatus comprises a flexible substrate and circuitry fabricated on the flexible substrate to perform data processing. At least one strain detector generates a strain signal which is dependent on a flexing state of the strain detector on the flexible substrate. A strain history control unit samples the at least one strain signal from the at least one strain detector at a plurality of time points and records a strain snapshot at each time point comprising data dependent on the at least one strain signal from the at least one strain detector.

Abstract: Smart labels, methods of operating smart labels, and associated contexts in which such smart labels may be used are disclosed. The smart label, for use in conjunction with consumer product packaging, comprises an energy harvester to capture ambient energy to provide a source of electrical energy and electronic circuitry powered by the electrical energy. A fuse provides an electrical connection between the energy harvester and the electronic circuitry and destruction of the fuse permanently disconnects the energy harvester from the electronic circuitry. Unnecessary continued operation of the electronic circuitry powered by the energy harvester can therefore be prevented, for example when the consumer product packaging is disposed of or recycled, which may be an undesirable heat source. Smart labelling, and a connected network of smart bins which can read the smart labelling, may also be used to promote consumer recycling of consumer product packaging.

Abstract: Apparatuses and methods of operating such apparatuses are disclosed. An apparatus comprises feature dataset input circuitry to receive a feature dataset comprising multiple feature data values indicative of a set of features, wherein each feature data value is represented by a set of bits. Class retrieval circuitry is responsive to reception of the feature dataset from the feature dataset input circuitry to retrieve from class indications storage a class indication for each feature data value received in the feature dataset, wherein class indications are predetermined and stored in the class indications storage for each permutation of the set of bits for each feature. Classification output circuitry is responsive to reception of class indications from the class retrieval circuitry to determine a classification in dependence on the class indications. A predicated class may thus be accurately generated from a simple apparatus.

Abstract: Apparatuses, methods of operating apparatuses, and corresponding computer programs are disclosed. In the apparatuses input circuitry receives input data comprising at least one data element and shift circuitry generates, for each data element of the input data, a bit-map giving a one-hot encoding representation of the data element, wherein a position of a set bit in the bit-map is dependent on the data element. Summation circuitry generates a position summation value for each position in the bit-map, wherein each position summation value is a sum across all bit-maps generated by the shift circuitry from the input data. Maximum identification circuitry determines at least one largest position summation value generated by the summation circuitry and output circuitry to generate an indication of at least one data element corresponding to the at least one largest position summation value. The statistical mode of the data elements in the input data is thereby efficiently determined.